Tag: data center as a service providers in usa

Patient 360 & Journey Mapping using Graph Technology

Posted on by Subhadeep Datta

Srinivasan Sundararajan

360 Degree View of Patient

With rising demands for quality and cost-effective patient care, healthcare providers are focusing on data-driven diagnostics while continuing to utilize their hard-earned human intelligence. In other words, data-driven healthcare is augmenting human intelligence.

360 Degree View of Patient, as it is called, plays a major role in delivering the required information to the providers. It is a unified view of all the available information about a patient. It could include but is not limited to the following information:

  • Appointments made by the patients
  • Interaction with different doctors
  • Medications prescribed by the doctors
  • Patient’s relationship to other patients within the eco-systems specially to identify the family history related risks
  • Patient’s admission to hospitals or other healthcare facilities
  • Discharge and ongoing care
  • Patient personal wellness activities
  • Patient billing and insurance information
  • Linkages to the same patient in multiple disparate databases within the same hospital
  • Information about a patient’s involvement in various seminars, medical-related conferences, and other events

Limitations of Current Methods

As evident in most hospitals, these information are usually scattered across multiple data sources/databases. Hospitals typically create a data warehouse by consolidating information from multiple resources and try to create a unified database. However, this approach is done using relational databases and the relational databases rely on joining tables across entities to arrive at a complete picture. The RDBMS is not meant to handle relationships which extend to multiple hops and require drilling down to many levels.

Role of Graph Technology & Graph Databases

A graph database is a collection of nodes (or entities typically) and edges (or relationships). A node represents an entity (for example, a person or an organization) and an edge represents a relationship between the two nodes that it connects (for example, friends). Both nodes and edges may have properties associated with them.

While there are multiple graph databases in the market today like, Neo4J, JanusGraph, TigerGraph, the following technical discussions pertain to graph database that is part of SQL server 2019. The main advantage of this approach is that it helps utilize the best RDBMS features wherever applicable, while keeping the graph database options for complex relationships like 360 degree view of patients, making it a true polyglot persistence architecture.

As mentioned above, in SQL Server 2019 a graph database is a collection of node tables and edge tables. A node table represents an entity in a graph schema. An edge table represents a relationship in a graph. Edges are always directed and connect two nodes. An edge table enables users to model many-to-many relationships in the graph. Normal SQL Insert statements are used to create records into both node and edge tables.

While the node tables and edge tables represent the storage of graph data there are some specialized commands which act as extension of SQL and help with traversing between the nodes to get the full details like patient 360 degree data.

MATCH statement

MATCH statement links two node tables through a link table, such that complex relationships can be retrieved. An example,

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SHORTEST_PATH statement

It finds the relationship path between two node tables by performing multiple hops recursively. It is one of the useful statements to find the 360 degree of a patient.

There are more options and statements as part of graph processing. Together it will help identify complex relationships across business entities and retrieve them.

GRAPH processing In Rhodium  

As mentioned in my earlier articles (Healthcare Data Sharing & Zero Knowledge Proofs in Healthcare Data Sharing), GAVS Rhodium framework enables Patient and Data Management and Patient Data Sharing and graph databases play a major part in providing patient 360 as well as for provider (doctor) credentialing data. The below screen shots show the samples from reference implementation.

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Patient Journey Mapping

Typically, a patient’s interaction with the healthcare service provider goes through a cycle of events. The goal of the provider organization is to make this journey smooth and provide the best care to the patients. It should be noted that not all patients go through this journey in a sequential manner, some may start the journey at a particular point and may skip some intermediate journey points. Proper data collection of events behind patient journey mapping will also help with the future prediction of events which will ultimately help with patient care.

Patient 360 data collection plays a major role in building the patient journey mapping. While there could be multiple definitions, the following is one of the examples of mapping between patient 360-degree events and patient journey mapping.

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The below diagram shows an example of a patient journey mapping information.

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Understanding patients better is essential for improving patient outcomes. 360 degree of patients and patient journey mapping are key components for providing such insights. While traditional technologies lack the need of providing those links, graph databases and graph processing will play a major role in patient data management.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Data Management Solutions for new-age enterprises using the combination of Multi Modal databases, Blockchain and Data Mining. The solutions aim at data sharing within enterprises as well as with external stakeholders.

Post – Pandemic Recruiting Practices

Posted on by Subhadeep Datta

Prabhakar Kumar Mandal

The COVID pandemic has transformed business as we know it. This includes recruitment. Right from the pre-hire activities to the post-hire ones, no hiring practices will be exempt from change we’re witnessing. To maintain a feasible talent acquisition program now and in the coming years, organizations face a persistent need to reimagine the way they do things at every step of the hiring funnel. 

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In my perspicacity, following are the key aspects to look at:

1. Transforming Physical Workspaces

Having employees be physically present at workplace is fraught with challenges now. We envision many companies transitioning into a fully or partially remote workforce to save on costs and give employees more flexibility.

This means companies that maintain a physical headquarter will be paying much closer attention to the purpose those spaces really serve—and so will the candidates. The emphasis now will be on spaces of necessity—meeting areas, spaces for collaborative work, and comfortable, individual spaces for essential workers who need to be onsite. 

2. Traveling for interviews will be an obsolete

It’s going to be a while before non-essential travel assumes its pre-corona importance. In a study of traveler attitudes spanning the U.S., Canada, the U.K., and Australia, the portion of people who said they intended to restrict their travel over the next year increased from 24% in the first half of March to 40% in the second half of March.

Candidates will be less willing than they once were to jump on a plane for an in-person interview when a video conference is a viable alternative. 

3. Demand for workers with cross-trained skills will increase

Skills-based hiring has been on the rise now and will keep increasing as businesses strive to do more with a lesser headcount. We anticipate organizations to increasingly seek out candidates who can wear multiple hats. 

Additionally, as machines take on more jobs that were once reserved for people, we will see even greater demand for uniquely human skills like problem solving and creative thinking. Ravi Kumar, president of Infosys Ltd., summed it up perfectly in an interview with Forbes: “machines will handle problem-solving and humans will focus on problem finding.” 

4. Recruiting events will look a lot different 

It’s unclear when large-scale, in-person gatherings like job fairs will be able to resume, but it will likely be a while. We will likely see most events move to a virtual model, which will not only reduce risk but significantly cut costs for those involved. This may open new opportunities to allocate that budget to improve some of the other pertinent recruiting practices on this list. 

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5. Time to hire may change dramatically

The current approach is likely to change. For example, that most people who took a new job last year were not searching for one: Somebody came and got them. Businesses seek to fill their recruiting funnel with as many candidates as possible, especially ‘passive candidates’, who are not looking to move. Frequently employers advertise jobs that do not exist, hoping to find people who might be useful later or in a different framework. We are always campaigning the importance of minding our recruiting metrics, which can help us not only to hire more competently but identify interruptions in our recruiting process.

Are there steps in the hiring process, like screening or onboarding, that can be accelerated to balance things out? Are there certain recruitment channels that typically yield faster hires than others that can be prioritized? These are important questions to ask as you analyze the pandemic’s impacts to your hiring funnel. 

6. How AI can be leveraged to screen candidates?

AI is helping candidates get matched with the right companies. There are over 100 parameters to assess the candidates. This reduces wastage of time, money, and resources. The candidates are marked on their core strengths. This helps the recruitment manager to place them in the apt role.

The current situation presents the perfect opportunity for companies to adopt new tools. Organizations can reassess their recruitment processes and strategies through HR-aligned technology.

Post-pandemic hiring strategy

This pertains more to the industries most impacted by the pandemic, like businesses in the hospitality sector, outdoor dining, and travel to name a few. Many of the applicants in this domain have chosen to make the shift towards more promising or booming businesses.

However, once the pandemic blows over and restrictions are lifted, you can expect suffering sectors to come back with major recruitment changes and fierce competition over top talent.

Companies that take this time to act by cultivating relationships and connections with promising talent in their sphere, will have the advantage of gathering valuable data from probable candidates.

About the Author –

Prabhakar is a recruiter by profession and cricketer by passion. His focus is on hiring for the infra verticle. He hails from a small town in Bihar was brought up in Pondicherry. Prabhakar has represented Pondicherry in U-19 cricket (National School Games). In his free time he enjoys reading, working on his health and fitness and spending time with his family and friends.

Reduce Test Times and Increase Coverage with AI & ML

Posted on by Subhadeep Datta

Kevin Surace

Chairman & CTO, Appvance.ai

With the need for frequent builds—often many times in a day—QEs can only keep pace through AI-led testing. It is the modern approach that allows quality engineers to create scripts and run tests autonomously to find bugs and provide diagnostic data to get to the root cause.

AI-driven testing means different things to different QA engineers. Some see it as using AI for identifying objects or helping create script-less testing; some consider it as autonomous generation of scripts while others would think in terms of leveraging system data to create scripts which mimic real user activity.

Our research shows that teams who are able to implement what they can in scripts and manual testing have, on average, less than 15% code, page, action, and likely user flow coverage. In essence, even if you have 100% code coverage, you are likely testing less than 15% of what users will do. That in itself is a serious issue.

Starting in 2012, Appvance set out to rethink the concept of QA automation. Today our AIQ Technology combines tens of thousands of hours of test automation machine learning with the deep domain knowledge, the essential business rules, each QE specialist knows about their application. We create an autonomous expert system that spawns multiple instances of itself that swarm over the application testing at the UX and at the API-levels. Along the way these Intelligences write the scripts, hundreds, and thousands of them, that describes their individual journeys through the application.

And why would we need to generate so many tests fully autonomously. Because applications today are 10X the size they were just ten years ago. But your QE team doesn’t have 10X the number of test automation engineers. And because you have 10X less time to do the work than 10 years ago. Just to keep pace with the dev team requires each quality engineer to be 100X more productive than they were 10 years ago.

Something had to change; that something is AI.

AI-testing in two steps

We leveraged AI and witnessed over 90% reduction in human effort to find the same bugs. So how does this work?

It’s really a two-stage process.

First, leveraging key AI capabilities in TestDesigner, Appvance’s codeless test creation system, we make it possible to write scripts faster, identify more resilient accessors, and substantially reduce maintenance of scripts.

With AI alongside you as you implement an automated test case, you get a technology that suggests the most stable accessors and constantly improves and refines them. It also creates “fallback accessors” when tests run and hit an accessor change enabling the script to continue even though changes have been made to the application. And finally, the AI can self-heal scripts which must and update them with new accessors without human assistance. These AI-based, built-in technologies give you the most stable scripts every time with the most robust accessor methodologies and self-healing. Nothing else comes close.

The final two points above deal with autonomous generation of tests. To beat the queue and crush it, you have to get a heavy lift for finding bugs. And as we have learnt, go far beyond the use cases that a business analyst listed. Job one is to find bugs and prioritize them, leveraging AI to generate tests autonomously.

Appvance’s patented AI engine has already been trained with millions of actions. You will teach it the business rules of your application (machine learning). It will then create real user flows, take every possible action, discover every page, fill out every form, get to every state, and validate the most critical outcomes just as you trained it to do. It does all this without writing or recording a single script. We call this is ‘blueprinting’ an application. We do this at every new build. Multiple instances of the AI will spin up, each selecting a unique path through the application, typically finding 1000s or more flows in a matter of minutes. When complete, the AI hands you the results including bugs, all the diagnostic data to help find the root cause, and the reusable test-scripts to repeat the bug. A further turn of the crank can refine these scripts into exact replicas of what production users are doing and apply them to the new build. Any modern approach to continuous testing needs to leverage AI in both helping QA engineers create scripts as well as autonomously create tests so that both parts work together to find bugs and provide data to get to the root cause. That AI driven future is available today from Appvance.

About the Author –

Kevin Surace is a highly lauded entrepreneur and innovator. He’s been awarded 93 worldwide patents, and was Inc. Magazine Entrepreneur of the Year, CNBC Innovator of the Decade, a Davos World Economic Forum Tech Pioneer, and inducted into the RIT Innovation Hall of Fame. Kevin has held leadership roles with Serious Energy, Perfect Commerce, CommerceNet and General Magic and is credited with pioneering work on AI virtual assistants, smartphones, QuietRock and the Empire State Building windows energy retrofit.

Design-led Organization: Creative Thinking as a Practice!

Posted on by Site Admin

Gogul R G

This is the first article in the series of ‘Design-led organization’ writing about creative thinking as a practice in GAVS. It is the first step for the readers to explore the world of design and creativity. So, let’s get started!

First let’s see what is design thinking is all about

There is a common misconception that design thinking is new. But when you look back, people have applied a human-centric creative process to build meaningful and effective solutions. Design has been practiced for ages to build monuments, bridges, automobiles, subway systems, etc. Design is not only limited to aesthetics, it is more of a mindset to think of a solution. Design thinking is a mindset to iteratively think about a complex problem and come up with a viable solution. 

Thinking outside of the box can provide an innovative solution to a sticky problem. However, thinking outside of the box can be a real challenge as we naturally develop patterns of thinking that are based on the repetitive activities and commonly accessed knowledge surround ourselves. It takes something to detach away from a situation where we’re too closely involved to be able to find better possibilities.

To illustrate how a fresh way of thinking can create unexpectedly good solutions, let’s look at a famous incident. Some years ago, an incident occurred where a truck driver had tried to pass under a low bridge. But, he failed, and the truck became firmly lodged under the bridge.

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The driver was unable to continue driving through or reverse out. The struck truck caused massive traffic problems, which resulted in emergency personnel, engineers, firefighters, and truck drivers gathering to negotiate various solutions to dislodge the truck.

Emergency workers were debating whether to dismantle parts of the truck or chip away at parts of the bridge. Each of one were looking for a solution with their respective level of expertise. A boy walking by and witnessing the intense debate looked at the truck, at the bridge, then looked at the road and said, “Why not just let the air out of the tires?” to the absolute amazement of all the specialists and experts trying to resolve the issue.

When the solution was tested, the truck could drive with ease, having suffered only the damage caused by its initial attempt to pass underneath the bridge. It symbolizes the struggles we face where often the most obvious solutions are the ones hardest to come by because of the self-imposed constraints we work within.  

“Challenging our assumptions and everyday knowledge is often difficult for us humans, as we rely on building patterns of thinking in order not to have to learn everything from scratch every time.“

Let’s come back to our topic “What is Design thinking?” Tim Brown, Executive Chairman of IDEO – an international design and consulting firm quoted design thinking as below.

“Design thinking is a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”

Now let’s think about our truck example. A boy with his fresh mindset provides a simple solution to address a complex problem. Yeah! this is the sweet spot. Everyone is creative and capable of thinking like a designer, and out of the box, to come up with a solution. This way of inculcating design as a mindset for a solution is known as Design thinking.

Yes, you read it right, everyone is creative…

We forget that back in kindergarten, we were all creative. We all played and experimented with weird things without fear or shame. We didn’t know enough not to. The fear of social rejection is something we learned as we got older. And that’s why it’s possible to regain our creative abilities, even decades later. In the field of design and user experience, there are individuals to stick with a methodology a while, they will end up doing amazing things. They come up with break through ideas or suggestions and work creatively with a team to develop something truly innovative. They surprise themselves with the realization that they are a lot more creative than they had thought. That early success shakes up how they see themselves and makes them eager to do more.

We just need to rediscover what we already have: the capacity to imagine, or build upon, new to the world ideas.  But the real value of creativity doesn’t emerge until you are brave enough to act on those ideas.

Geshe Thupten Jinpa, who has been the Dalai Lama’s chief English translator for more than twenty years, shared an insight about the nature of creativity. Jinpa pointed out that there’s no word in the Tibetan language for ‘creativity’ or ‘being creative’. The closest translation is ‘natural’. In other words, if you want to be more creative, you should be more natural! So…be natural!

At your workplace, the complex problems can be easily sorted out when you find a solution using creativity with the mindset of design thinking. Creativity can be improved by following the below steps.

  1. Go for a walk.
  2. Play your favorite games.
  3. Move your eyes.
  4. Take a break and enjoy yourself.
  5. Congratulate yourself each time you do something well.
  6. Estimate time, distance, and money.
  7. Take a route you never have taken before.
  8. Look for images in mosaics, patterns, textures, clouds, stars…
  9. Try something you have never done before.
  10. Do a creative exercise.
  11. Start a collection (stamps, coins, art, stationery, anything you wish to collect)
  12. Watch Sci-Fi or fantasy films.
  13. Change the way you do things – there are no routine tasks, only routine way of doing things.
  14. Wear a color you do not like.
  15. Think about how they invented equipment or objects you use daily.
  16. Make a list of 10 things you think are impossible to do and then imagine how you could make each one possible.
  17. For every bad thing that happens to you, remember at least 3 good things that happened.
  18. Read something you have not read yet.
  19. Make friends with people on the other side of the world.
  20. When you have an idea, make a note of it, and later check to see if it happened.
  21. Connect a sport with your work.
  22. Try food you never tried before.
  23. Talk to grandparents and relatives and listen to their stories.
  24. Give an incorrect answer to a question.
  25. Find links between people, things, ideas, or facts.
  26. Ask children how to do something and observe their creativity.

Start doing the above-mentioned steps to inculcate a creative mindset and apply it in your day-to-day work. Companies like GE health care, Procter & Gamble, UBER practiced design thinking and implemented in their new product launches and for solving complex problems in their organizations. Be natural to be more creative! When you are more creative, you can apply design thinking for seeking any solution for a complex problem in your work.

This is the first article in the series of Design led Organization in GAVS. Keep watching this space for more articles on design and keep exploring the world of design-thinking!

References:

About the Author –

Gogul is a passionate UX designer with 8+ years of experience into designing experiences for digital channels like Enterprise apps, B2C, B2B apps, Mobile apps, Kiosk, Point of Sale, Endless aisle, telecom products. He is passionate about transforming complex problems into actionable solutions using design.

Center of Excellence – Network

Posted on by Site Admin

The Network CoE was established to focus on Network solution design, Network design, Advanced Network troubleshooting, Network consulting, Network automation, and competency development in Next Generation Network technologies. It is also involved in conducting Network and Network security assessments in the customer’s IT infrastructure environments focused on optimization and transformation.

Network and Network Security Certification drive

As part of Network CoE, we focus on upgrading the skill sets of L1, L2, L3 Network engineers so that their competency levels are high. This is achieved by Network certification drives organized by Network COE. There are many certification drives focusing on Routing, Switching, Network security, Data Center Technologies, and Network automation driven by Network CoE like CCNA, CCNP, PCNSE, CCNA Data Center and Cisco Certified DevNet Associate. There is an active participation in these certification drives, and many GAVS engineers got themselves certified.

Standard Best Practices and Standard Operating Procedures

In Network CoE, the focus is on industry best practices. Standard Operating Practices are created for various technologies within Networking and Network security and used for Network operations.  We have Standard Operating Practices for Monitoring, NOC, switching, routing, WIFI, load balancers and Network security.

Next generation Network Transformation

The Network and Network Security Industry is undergoing key changes in terms of next generation technologies,Next Generation Firewall, Software defined Networks, WIFI 6 standard. There is an added impetus to Network automation, Intent based Networking. We enable Network transformation by enabling these technologies in customer environments.

Network Automation

We are focusing on Network automation of Standard Operating practices pertaining to Network and Network Security technologies. Instead of usual script-based automation, we focus on automation through Network Programmability via standard API interfaces. This gives much finer control and increased functionality in automation.

Network Assessments and Recommendations

We undertake Network Assessments which focuses on Networking and Network security infrastructure including devices and monitoring tools. We focus on various device types like routers, switches, firewall, WIFI controllers, WIFI access points, load balancers, Layer-3 switches, collaboration devices, SD-WAN devices, MPLS devices, VPN devices, IPS devices, etc. We also focus on Network monitoring tools.  We have a GAVS tool called GAVS topology mapper which can be used to discover network topology and its serves as one of the inputs during Network assessment. We apply standard best practices and come out with findings and recommendations. The recommendations will be directed towards Network optimization and/or Network transformation.

Solutions for Pain Points

We identify customer paint points in Networking and Network security areas and address it with comprehensive solutions. A case in point is where we designed a disaster recovery solution for an enterprise network, where the main site and DR site had different subnet schemes and for the Disaster recovery solution to work the VMs in main site and DR site need to have the same IP address.

Network Maturity Model

In GAVS, we have a Network Maturity Model. We have various levels with the Model. We use the Network Maturity Model to rate Network and Network Security setup.

Network Maturity Levels
ScoreLevel
5Optimised
4Managed
3Defined
2Repeatable
1Ad hoc
Network Design

We undertake Network design of Green Field projects (New Network) or Network re-design of Brownfield projects (Existing Network).  A case in point is where we re-designed an existing data center for better resiliency.

Data Center Design

We have designed Data Centers with N+1 Redundancy based on Cisco Nexus 9K and ACI as part of Data Center move and consolidation.  We used spine and leaf architecture for high availability. We have migrated Catalyst 6000 based data center to a Data Center with Nexus 9K.

Advanced Network and Network Security Services

We undertake several Advanced Network and Network security services. We have done large scale Cisco Identity Service Engine (ISE) Hardening and upgrade. We also migrated to DMVPN for several hundreds of sites.

Advanced Network and Network SecurityTroubleshooting

There are situations when a problem will involve two or more towers, e.g., Networking, server applications etc., we get involved and crack these kinds of problems.

For example, a problem which involved DHCP Network service running in a server. The DHCP network service became slow. We systematically analysed and found out that the actual problem is the server slowness and not the DHCP Network service. In another situation, we found out that DMZ firewall was having 90% CPU utilization which led to connection drops of Applications and we fixed it by upgrading the firewall devices.

Conclusion

We continue to partner with GAVS Customer success managers to provide unique experience to customers in the Networking area.

If you have any questions about the CoE, you may reach out to them at COE_NETWORK@gavstech.com

CoE Team Members

  • Ambika Tripathi
  • Andrew Ellis
  • AvineshYokanathan
  • Deepak Narayanaswamy
  • Durai Murugan Prakash
  • Faheem koyatty
  • Ganesh Kumar J
  • Gayathri R
  • Ibrahim Silver Nooruddin
  • JettiTarakesh
  • Justin Robinson
  • Krishnakumar R
  • Nabiulla A
  • Nandhini Prabhu
  • Navaneetha Krishnan
  • Palanisamy Sakthivel
  • Prasad R
  • Rajeshkanna S
  • Ravichandran V
  • Shafi H
  • Shamini P
  • Shanmukha Ganesh
  • Sridhar
  • Srijith
  • Suresh Chander
  • Venkata Manikrishna Soma
  • Vishal Manuhar

Center of Excellence – Java

Posted on by Site Admin

The Java CoE was established to partner with our customers and aid them in realizing business benefits through effective adoption of cutting-edge technologies; thus, enabling customer success.

Objectives

  • Be the go-to team for anything related to Java across the organization and customer engagements.
  • Build competency by conducting training and mentoring sessions, publishing blogs, whitepapers and participating in Hackathons.
  • Support presales team in creating proposals by providing industry best solutions using the latest technologies, standards & principles.
  • Contribute a certain percent of revenue growth along with the CSMs.
  • Create reusable artifacts, frameworks, solutions and best practices which can be used across organization to improve delivery quality.

Focus Areas

  1. Design Thinking: Setting up a strong foundation of “Design Thinking and Engineering Mindset” is paramount for any business. We aim to do so in the following way:
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2. Solution and Technology: Through our practice, we aim to equip GAVS with solution-oriented technology leaders who can lead us ahead through disruptive times

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3. Customer success

  • Identify opportunities in accounts based on the collaboration with CSMs, understand customer needs, get details about the engagement, understand the focus areas and challenges.
  • Understand the immediate need of the project, provide solution to address the need.
  • Java council to help developers arrive at solutions.
  • Understand architecture in detail and provide recommendation / create awareness to use new technologies
  • Enforce a comprehensive review process to enable quality delivery.

Accomplishments

  • Formed the CoE team
  • Identified the focus Areas
  • Identified leads for every stream
  • Socialized the CoEwithin GAVS
  • Delivered effective solutions across projects to improve delivery quality
  • Conducted trainings on standards and design-oriented coding practices across GAVS
  • Publishedblogs to bring in design-oriented development practices
  • Identified the areas for creating re-usable artefacts (Libraries / Frameworks)
  • Brainstormed and finalized the design for creating Frameworks (For the identified areas)
  • Streamlined the DevOps process which can be applied in any engagement
  • Built reusable libraries, components and frameworks which can be used across GAVS
  • Automated the Code Review process
  • Organized and conducted hackathons and tech meetups
  • Discovered potential technical problems/challenges across teams and offered effective solutions, thereby enabling customer success
  • Supported the presales team in creating customized solutions for prospects

Upcoming Activities

  • Establishing tech governance and align managers / tech leads to the process
  • Setting up security standards and principles across domain
  • Buildingmore reusable libraries, components and frameworks which can be used across GAVS
  • Adopting Design Patterns / Anti-patterns
  • Enforcing a strong review process to bring in quality delivery
  • Enabling discussions with the customers
  • Setting up a customer advisory team

Contribution to Organizational Growth

As we continue our journey, we aim to support the revenue growth of our organization. Customer Success being a key goal of GAVS, we will continue to enable it by improving the quality of service delivery and building a solid foundation across all technology and process streams. We also want to contribute to the organization by developing a core competency around a strategic capability and reduce knowledge management risks.

If you have any questions about the CoE, you may reach out to them at COE_JAVA@gavstech.com

CoE Team Members

  • Lakshminarasimhan J
  • Muraleedharan Vijayakumar
  • Bipin V
  • Meenakshi Sundaram
  • Mahesh Rajakumar M
  • Ranjith Joseph Selvaraj
  • Jagathesewaren K
  • Sivakumar Krishnasamy
  • Vijay Anand Shanmughadass
  • Sathya Selvam
  • Arun Kumar Ananthanarayanan
  • John Kalvin Jesudhason

Observability versus Monitoring

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Sri Chaganty

“Observability” has become a key trend in Service Reliability Engineering practice.  One of the recommendations from Gartner’s latest Market Guide for IT Infrastructure Monitoring Tools released in January 2020 says, “Contextualize data that ITIM tools collect from highly modular IT architectures by using AIOps to manage other sources, such as observability metrics from cloud-native monitoring tools.”

Like so many other terms in software engineering, ‘observability’ is a term borrowed from an older physical discipline: in this case, control systems engineering. Let me use the definition of observability from control theory in Wikipedia: “observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.”

Observability is gaining attention in the software world because of its effectiveness at enabling engineers to deliver excellent customer experiences with software despite the complexity of the modern digital enterprise.

When we blew up the monolith into many services, we lost the ability to step through our code with a debugger: it now hops the network.  Monitoring tools are still coming to grips with this seismic shift.

How is observability different than monitoring?

Monitoring requires you to know what you care about before you know you care about it. Observability allows you to understand your entire system and how it fits together, and then use that information to discover what specifically you should care about when it’s most important.

Monitoring requires you to already know what normal is. Observability allows discovery of different types of ‘normal’ by looking at how the system behaves, over time, in different circumstances.

Monitoring asks the same questions over and over again. Is the CPU usage under 80%? Is memory usage under 75% percent? Or, is the latency under 500ms? This is valuable information, but monitoring is useful for known problems.

Observability, on the other side, is about asking different questions almost all the time. You discover new things.

Observability allows the discovery of different types of ‘normal’ by looking at behavior, over time, in different circumstances.

Metrics do not equal observability.

What Questions Can Observability Answer?

Below are sample questions that can be addressed by an effective observability solution:

  • Why is x broken?
  • What services does my service depend on — and what services are dependent on my service?
  • Why has performance degraded over the past quarter?
  • What changed? Why?
  • What logs should we look at right now?
  • What is system performance like for our most important customers?”
  • What SLO should we set?
  • Are we out of SLO?
  • What did my service look like at time point x?
  • What was the relationship between my service and x at time point y?
  • What was the relationship of attributed across the system before we deployed? What’s it like now?
  • What is most likely contributing to latency right now? What is most likely not?
  • Are these performance optimizations on the critical path?

About the Author –

Sri is a Serial Entrepreneur with over 30 years’ experience delivering creative, client-centric, value-driven solutions for bootstrapped and venture-backed startups.

JAVA – Cache Management

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Sivaprakash Krishnan

This article explores the offering of the various Java caching technologies that can play critical roles in improving application performance.

What is Cache Management?

A cache is a hot or a temporary memory buffer which stores most frequently used data like the live transactions, logical datasets, etc. This intensely improves the performance of an application, as read/write happens in the memory buffer thus reducing retrieval time and load on the primary source. Implementing and maintaining a cache in any Java enterprise application is important.

  • The client-side cache is used to temporarily store the static data transmitted over the network from the server to avoid unnecessarily calling to the server.
  • The server-side cache could be a query cache, CDN cache or a proxy cache where the data is stored in the respective servers instead of temporarily storing it on the browser.

Adoption of the right caching technique and tools allows the programmer to focus on the implementation of business logic; leaving the backend complexities like cache expiration, mutual exclusion, spooling, cache consistency to the frameworks and tools.

Caching should be designed specifically for the environment considering a single/multiple JVM and clusters. Given below multiple scenarios where caching can be used to improve performance.

1. In-process Cache – The In-process/local cache is the simplest cache, where the cache-store is effectively an object which is accessed inside the application process. It is much faster than any other cache accessed over a network and is strictly available only to the process that hosted it.

Data Center Consolidation Initiative Services

  • If the application is deployed only in one node, then in-process caching is the right candidate to store frequently accessed data with fast data access.
  • If the in-process cache is to be deployed in multiple instances of the application, then keeping data in-sync across all instances could be a challenge and cause data inconsistency.
  • An in-process cache can bring down the performance of any application where the server memory is limited and shared. In such cases, a garbage collector will be invoked often to clean up objects that may lead to performance overhead.

In-Memory Distributed Cache

Distributed caches can be built externally to an application that supports read/write to/from data repositories, keeps frequently accessed data in RAM, and avoid continuous fetching data from the data source. Such caches can be deployed on a cluster of multiple nodes, forming a single logical view.

  • In-memory distributed cache is suitable for applications running on multiple clusters where performance is key. Data inconsistency and shared memory aren’t matters of concern, as a distributed cache is deployed in the cluster as a single logical state.
  • As inter-process is required to access caches over a network, latency, failure, and object serialization are some overheads that could degrade performance.

2. In-memory database

In-memory database (IMDB) stores data in the main memory instead of a disk to produce quicker response times. The query is executed directly on the dataset stored in memory, thereby avoiding frequent read/writes to disk which provides better throughput and faster response times. It provides a configurable data persistence mechanism to avoid data loss.

Redis is an open-source in-memory data structure store used as a database, cache, and message broker. It offers data replication, different levels of persistence, HA, automatic partitioning that improves read/write.

Replacing the RDBMS with an in-memory database will improve the performance of an application without changing the application layer.

3. In-Memory Data Grid

An in-memory data grid (IMDG) is a data structure that resides entirely in RAM and is distributed among multiple servers.

Key features

  • Parallel computation of the data in memory
  • Search, aggregation, and sorting of the data in memory
  • Transactions management in memory
  • Event-handling

Cache Use Cases

There are use cases where a specific caching should be adapted to improve the performance of the application.

1. Application Cache

Application cache caches web content that can be accessed offline. Application owners/developers have the flexibility to configure what to cache and make it available for offline users. It has the following advantages:

  • Offline browsing
  • Quicker retrieval of data
  • Reduced load on servers

2. Level 1 (L1) Cache

This is the default transactional cache per session. It can be managed by any Java persistence framework (JPA) or object-relational mapping (ORM) tool.

The L1 cache stores entities that fall under a specific session and are cleared once a session is closed. If there are multiple transactions inside one session, all entities will be stored from all these transactions.

3. Level 2 (L2) Cache

The L2 cache can be configured to provide custom caches that can hold onto the data for all entities to be cached. It’s configured at the session factory-level and exists as long as the session factory is available.

  • Sessions in an application.
  • Applications on the same servers with the same database.
  • Application clusters running on multiple nodes but pointing to the same database.

4. Proxy / Load balancer cache

Enabling this reduces the load on application servers. When similar content is queried/requested frequently, proxy takes care of serving the content from the cache rather than routing the request back to application servers.

When a dataset is requested for the first time, proxy saves the response from the application server to a disk cache and uses them to respond to subsequent client requests without having to route the request back to the application server. Apache, NGINX, and F5 support proxy cache.

Desktop-as-a-Service (DaaS) Solution

5. Hybrid Cache

A hybrid cache is a combination of JPA/ORM frameworks and open source services. It is used in applications where response time is a key factor.

Caching Design Considerations

  • Data loading/updating
  • Performance/memory size
  • Eviction policy
  • Concurrency
  • Cache statistics.

1. Data Loading/Updating

Data loading into a cache is an important design decision to maintain consistency across all cached content. The following approaches can be considered to load data:

  • Using default function/configuration provided by JPA and ORM frameworks to load/update data.
  • Implementing key-value maps using open-source cache APIs.
  • Programmatically loading entities through automatic or explicit insertion.
  • External application through synchronous or asynchronous communication.

2. Performance/Memory Size

Resource configuration is an important factor in achieving the performance SLA. Available memory and CPU architecture play a vital role in application performance. Available memory has a direct impact on garbage collection performance. More GC cycles can bring down the performance.

3. Eviction Policy

An eviction policy enables a cache to ensure that the size of the cache doesn’t exceed the maximum limit. The eviction algorithm decides what elements can be removed from the cache depending on the configured eviction policy thereby creating space for the new datasets.

There are various popular eviction algorithms used in cache solution:

  • Least Recently Used (LRU)
  • Least Frequently Used (LFU)
  • First In, First Out (FIFO)

4. Concurrency

Concurrency is a common issue in enterprise applications. It creates conflict and leaves the system in an inconsistent state. It can occur when multiple clients try to update the same data object at the same time during cache refresh. A common solution is to use a lock, but this may affect performance. Hence, optimization techniques should be considered.

5. Cache Statistics

Cache statistics are used to identify the health of cache and provide insights about its behavior and performance. Following attributes can be used:

  • Hit Count: Indicates the number of times the cache lookup has returned a cached value.
  • Miss Count: Indicates number of times cache lookup has returned a null or newly loaded or uncached value
  • Load success count: Indicates the number of times the cache lookup has successfully loaded a new value.
  • Total load time: Indicates time spent (nanoseconds) in loading new values.
  • Load exception count: Number of exceptions thrown while loading an entry
  • Eviction count: Number of entries evicted from the cache

Various Caching Solutions

There are various Java caching solutions available — the right choice depends on the use case.

Software Test Automation Platform

At GAVS, we focus on building a strong foundation of coding practices. We encourage and implement the “Design First, Code Later” principle and “Design Oriented Coding Practices” to bring in design thinking and engineering mindset to build stronger solutions.

We have been training and mentoring our talent on cutting-edge JAVA technologies, building reusable frameworks, templates, and solutions on the major areas like Security, DevOps, Migration, Performance, etc. Our objective is to “Partner with customers to realize business benefits through effective adoption of cutting-edge JAVA technologies thereby enabling customer success”.

About the Author –

Sivaprakash is a solutions architect with strong solutions and design skills. He is a seasoned expert in JAVA, Big Data, DevOps, Cloud, Containers, and Micro Services. He has successfully designed and implemented a stable monitoring platform for ZIF. He has also designed and driven Cloud assessment/migration, enterprise BRMS, and IoT-based solutions for many of our customers. At present, his focus is on building ‘ZIF Business’ a new-generation AIOps platform aligned to business outcomes.

IoT Adoption during the Pandemic

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Artificial Intelligence for IT Operations

Naveen KT

From lightbulbs to cities, IoT is adding a level of digital intelligence to various things around us. Internet of Things or IoT is physical devices connected to the internet, all collecting and sharing data, which can then be used for various purposes. The arrival of super-cheap computers and the ubiquity of wireless networks are behind the widespread adoption of IoT. It is possible to turn any object, from a pill to an airplane, into an IoT-enabled device. It is making devices smarter by letting them ‘sense’ and communicate, without any human involvement.

Let us look at the developments that enabled the commercialization of IoT.

History

The idea of integrating sensors and intelligence to basic objects dates to the 1980s and 1990s. But the progress was slow because the technology was not ready. Chips were too big and bulky and there was no way for an object to communicate effectively.

Processors had to be cheap and power-frugal enough to be disposed of before it finally becomes cost-effective to connect to billions of devices. The adoption of RFID tags and IPV6 was a necessary step for IoT to scale.

Kevin Ashton penned the phrase ‘Internet of Things’ in 1999. Although it took a decade for this technology to catch up with his vision. According to Ashton “The IoT integrates the interconnectedness of human culture (our things) with our digital information system(internet). That’s the IoT”.

Early suggestions for IoT include ‘Blogjects’ (object that blog and record data about themselves to the internet), Ubiquitous computing (or ‘ubicomp’), invisible computing, and pervasive computing.

How big is IoT?

AIOps in Infrastructure Management

IDC predicts that there will be 41.6 billion connected IoT devices by 2025. It also suggests industrial and automotive equipment represent the largest opportunity of connected ‘things’.

Gartner predicts that the enterprise and automotive sectors will account for 5.8 billion devices this year.

However, the COVID-19 pandemic has further enhanced the need for IoT-enabled devices to help the nations tackle the crisis.

IoT for the Government

Information about the movement of citizens is urgently required by governments to track the spread of the virus and potentially monitor their quarantine measures. Some IoT operators have solutions that could serve these purposes.

AIOps platform
  • Telia’s Division X has developed Crowd Insights which provides aggregated smartphone data to city and transport authorities of Nordic Countries. It is using the tool which will track the movement of citizens during the quarantine.
  • Vodafone provides insights on traffic congestion.
  • Telefonica developed Smart steps, which aggregates data on footfall and movement for the transport, tourism, and retail sectors.

Personal data of people will also help in tracking clusters of infection by changing the privacy regulations. For example, in Taiwan, high-risk quarantined patients were being monitored through their mobile phones to ensure compliance with quarantine rules. In South Korea, the officials track infected citizens and alert others if they come into contact with them. The government of Israel went as far as passing an emergency law to monitor the movement of infected citizens via their phones.

China is already using mass temperature scanning devices in public areas like airports. A team of researchers at UMass Amherst is testing a device that can analyze coughing sounds to identify the presence of flu-like symptoms among crowds.

IoT in Health care

COVID-19 could be the trigger to explore new solutions and be prepared for any such future pandemics, just as the SARS epidemic in 2003 which spurred the governments in South Korea and Taiwan to prepare for today’s problems.

IT operations analytics

Remote patient monitoring (RPM) and telemedicine could be helpful in managing a future pandemic. For example, patients with chronic diseases who are required to self-isolate to reduce their exposure to COVID-19 but need continuous care would benefit from RPM. Operators like Orange, Telefónica, and Vodafone already have some experience in RPM.

Connected thermometers are being used in hospitals to collect data while maintaining a social distance. Smart wearables are also helpful in preventing the spread of the virus and responding to those who might be at risk by monitoring their vital signs.

Connected thermometers are being used in hospitals to collect data while maintaining a social distance. Smart wearables are also helpful in preventing the spread of the virus and responding to those who might be at risk by monitoring their vital signs.

Telehealth is widely adopted in the US, and the authorities there are relaxing reimbursement rules and regulations to encourage the extension of specific services. These include the following.

  • Medicare, the US healthcare program for senior citizens, has temporarily expanded its telehealth service to enable remote consultations.
  • The FCC has made changes to the Rural Health Care (RHC) and E-Rate programs to support telemedicine and remote learning. Network operators will be able to provide incentives or free network upgrades that were previously not permitted, for example, for hospitals that are looking to expand their telemedicine programs.

IoT for Consumers

The IoT promises to make our environment smarter, measurable, and interactive.COVID-19 is highly contagious, and it can be transmitted from one to another even by touching the objects used by the affected person. The WHO has instructed us to disinfect and sanitize high touch objects. IoT presents us with an ingenious solution to avoid touching these surfaces altogether. Hands-free and sensor-enabled devices and solutions like smart lightbulbs, door openers, smart sinks, and others help prevent the spread of the virus.

Security aspects of IoT

Security is one of the biggest issues with the IoT. These sensors collect extremely sensitive data like what we say and do in our own homes and where we travel. Many IoT devices lack security patches, which means they are permanently at risk. Hackers are now actively targeting IoT devices such as routers and webcams because of their inherent lack of security makes them easy to compromise and pave the way to giant botnets.

Machine learning service provider
Machine learning service provider

IoT bridges the gap between the digital and the physical world which means hacking into devices can have dangerous real-world consequences. Hacking into sensors and controlling the temperature in power stations might end up in catastrophic decisions and taking control of a driverless car could also end in disaster.

Overall IoT makes the world around us smarter and more responsive by merging the digital and physical universe. IoT companies should look at ways their solutions can be repurposed to help respond to the crisis.

Enterprise IT infrastructure services
Enterprise IT infrastructure services

References:

  • https://www.analysysmason.com/Research/Content/Comments/covid19-iot-role-rdme0-rma17/
  • shorturl.at/wBFGT

Naveen is a software developer at GAVS. He teaches underprivileged children and is interested in giving back to society in as many ways as he can. He is also interested in dancing, painting, playing keyboard, and is a district-level handball player.

Smart Spaces Tech Trends for 2020

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data center as a service providers in usa

Priyanka Pandey

These are unprecedented times. The world hadn’t witnessed such a disruption in recent history. It is times like these test the strength and resilience of our community. While we’ve been advised to maintain social distancing to flatten to curve, we must keep the wheels of the economy rolling.

In my previous article, I covered the ‘People-Centric’ Tech Trends of the year, i.e., Hyper automation, Multiexperience, Democratization, Human Augmentation and Transparency and Traceability. All of those hold more importance now in the light of current events. Per Gartner, Smart Spaces enable people to interact with people-centric technologies. Hence, the next Tech Trends in the list are about creating ‘Smart Spaces’ around us.

Smart spaces, in simple words, are interactive physical environments decked out with technology, that act as a bridge between humans and the digital world. The most common example of a smart space is a smart home, also called as a connected home. Other environments that could be a smart space are offices and communal workspaces; hotels, malls, hospitals, public places such as libraries and schools, and transportation portals such as airports and train stations. Listed below are the 5 Smart Spaces Technology Trends which, per Gartner, have great potential for disruption.

Trend 6: Empowered Edge

Edge computing is a distributed computing topology in which information processing and data storage are located closer to the sources, repositories and consumers of this information. Empowered Edge is about moving towards a smarter, faster and more flexible edge by using more adaptive processes, fog/mesh architectures, dynamic network topology and distributed cloud. This trend will be introduced across a spectrum of endpoint devices which includes simple embedded devices (e.g., appliances, industrial devices), input/output devices (e.g., speakers, screens), computing devices (e.g., smartphones, PCs) and complex embedded devices (e.g., automobiles, power generators). Per Gartner predictions, by 2022, more than 50% of enterprise-generated data will be created and processed outside the data center or cloud. This trend also includes the next-generation cellular standard after 4G Long Term Evolution (LTE), i.e., 5G. The concept of edge also percolates to the digital-twin models.

Trend 7: Distributed Cloud

Gartner defines a distributed cloud as “distribution of public cloud services to different locations outside the cloud providers’ data centers, while the originating public cloud provider assumes responsibility for the operation, governance, maintenance and updates.” Cloud computing has always been viewed as a centralized service, although, private and hybrid cloud options compliments this model. Implementing private cloud is not an easy task and hybrid cloud breaks many important cloud computing principles such as shifting the responsibility to cloud providers, exploiting the economics of cloud elasticity and using the top-class services of large cloud service providers. A distributed cloud provides services in a location which meets organization’s requirements without compromising on the features of a public cloud. This trend is still in the early stages of development and is expected to build in three phases:

Phase 1: Services will be provided from a micro-cloud which will have a subset of services from its centralized cloud.

Phase 2: An extension to phase 1, where service provider will team up with a third-party to deliver subset of services from the centralized cloud.

Phase 3: Distributed cloud substations will be setup which could be shared by different organizations. This will improve the economics associated as the installation cost can be split among the companies.

Trend 8: Autonomous Things

Autonomous can be defined as being able to control oneself. Similarly, Autonomous Things are devices which can operate by themselves without human intervention using AI to automate all their functions. The most common among these devices are robots, drones, and aircrafts. These devices can operate across different environments and will interact more naturally with their surroundings and people. While exploring use cases of this technology, understanding the different spaces the device will interact to, is very important like the people, terrain obstacles or other autonomous things. Another aspect to consider would be the level of autonomy which can be applied. The different levels are: No automation, Human-assisted automation, Partial automation, Conditional automation, High automation and Full automation. With the proliferation of this trend, a shift is expected from stand-alone intelligent things to collaborative intelligent things in which multiple devices work together to deliver the final output. The U.S. Defense Advanced Research Projects Agency (DARPA) is studying the use of drone swarms to defend or attack military targets.

Trend 9: Practical Blockchain

Most of us have heard about Blockchain technology. It is a tamper-proof, decentralized, distributed database that stores blocks of records linked together using cryptography. It holds the power to take industries to another level by enabling trust, providing transparency, reducing transaction settlement times and improving cash flow. Blockchain also makes it easy to trail assets back to its origin, reducing the chances of substituting it with counterfeit products. Smart contracts are used as part of the blockchain which can trigger actions on encountering any change in the blockchain; such as releasing payment when goods are received. New developments are being introduced in public blockchains but over time these will be integrated with permissioned blockchains which supports membership, governance and operating model requirements. Some of the use cases of this trend that Gartner has identified are: Asset Tracking, Identity Management/Know Your Client (KYC), Internal Record Keeping, Shared Record Keeping, Smart Cities/the IoT, Trading, Blockchain-based voting, Cryptocurrency payments and remittance services. Per the 2019 Gartner CIO Survey, in the next three years 60% of CIOs expect blockchain deployment in some way.

Trend 10: AI Security

Per Gartner, over the next five years AI-based decision-making will be applied across a wide set of use cases which will result in a tremendous increase of potential attack surfaces. Gartner provides three key perspectives on how AI impacts security: protecting AI-powered systems, leveraging AI to enhance security defense and anticipating negative use of AI by attackers. ML pipelines have different phases and at each of these phases there are various kinds of risks associated. AI-based security tools can be very powerful extension to toolkits with use cases such as security monitoring, malware detection, etc. On the other hand, there are many AI-related attack techniques which include training data poisoning, adversarial inputs and model theft and per Gartner predictions, through 2022, 30% of all AI cyberattacks will leverage these attacking techniques. Every innovation in AI can be exploited by attackers for finding new vulnerabilities. Few of the AI attacks that security professionals must explore are phishing, identity theft and DeepExploit.

One of the most important things to note here is that the trends listed above cannot exist in isolation. IT leaders must analyse what combination of these trends will drive the most innovation and strategy fitting it into their business models. Soon we will have smart spaces around us in forms of factories, offices and cities with increasingly insightful digital services everywhere for an ambient experience.

Sources:

https://www.pcmag.com/news/gartners-top-10-strategic-technology-trends-for-2020

About the Author:

Priyanka is an ardent feminist and a dog-lover. She spends her free time cooking, reading poetry and exploring new ways to conserve the environment.