Sify Technologies
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In 1998, we were Indiaβs first Indian private ISP. Millions experienced the internet for the first time on the Sify network. We pioneered voice and data services from our public internet access points.
Two decades and a smart shift in business positioning later, Sify is now an indispensable convergence bridge that enterprises seek. Sify has matured into Indiaβs largest digital transformation company, bringing together a converged ICT ecosystem for the benefit of market-leading enterprises.
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To Infinity and Beyond!
Vamsi Nekkanti looks at the future of data centers β in space and underwater
Data centers can now be found on land all over the world, and more are being built all the time. Because a lot of land is already being utilized for them, Microsoft is creating waves in the business by performing trials of enclosed data centers in the water.
They have already submitted a patent application for an Artificial Reef Data Center, an underwater cloud with a cooling system that employs the ocean as a large heat exchanger and intrusion detection for submerged data centers. So, with the possibility of an underwater cloud becoming a reality, is space the next-or final-frontier?
As the cost of developing and launching satellites continues to fall, the next big thing is combining IT (Information Technology) principles with satellite operations to provide data center services into Earth orbit and beyond.
Until recently, satellite hardware and software were inextricably linked and purpose-built for a single purpose. With the emergence of commercial-off-the-shelf processors, open standards software, and standardized hardware, firms may reuse orbiting satellites for multiple activities by simply downloading new software and sharing a single spacecraft by hosting hardware for two or more users.
This βSpace as a Serviceβ idea may be used to run multi-tenant hardware in a micro-colocation model or to provide virtual server capacity for computing βabove the clouds.β Several space firms are incorporating micro-data centers into their designs, allowing them to analyze satellite imaging data or monitor dispersed sensors for Internet of Things (IoT) applications.
HPE Spaceborne Computer-2 (a set of HPE Edgeline Converged EL4000 Edge and HPE ProLiant machines, each with an Nvidia T4 GPU to support AI workloads) is the first commercial edge computing and AI solution installed on the International Space Station in the first half of 2021Β (Image credit:Β NASA)
Advantages of Space Data Centers
The data center will collect satellite data, including images, and analyze it locally. Only valuable data is transmitted down to Earth, decreasing transmission costs, and slowing the rate at which critical data is sent down.
The data center might be powered by free, abundant solar radiation and cooled by the chilly emptiness of space. Outside of a solar flare or a meteorite, there would be a minimal probability of a natural calamity taking down the data center. Spinning disc drives would benefit from the space environment. The lack of gravity allows the drives to spin more freely, while the extreme cold in space helps the servers to handle more data without overheating.
Separately, the European Space Agency is collaborating with Intel and Ubotica on the PhiSat-1, a CubeSat with AI (Artificial Intelligence) computing aboard. LyteLoop, a start-up, seeks to cover the sky with light-based data storage satellites.
NTT and SKY Perfect JV want to begin commercial services in 2025 and have identified three primary potential prospects for the technology.
The first, a βspace sensing project,β would develop an integrated space and earth sensing platform that will collect data from IoT terminals deployed throughout the world and deliver a service utilizing the worldβs first low earth orbit satellite MIMO (Multiple Input Multiple Output) technology.
The space data center will be powered by NTTβs photonics-electronics convergence technology, which decreases satellite power consumption and has a stronger capacity to resist the detrimental effects of radiation in space.
Finally, the JV is looking into βbeyond 5G/6Gβ applications to potentially offer ultra-wide, super-fast mobile connection from space.
The Challenge of Space-Based Data Centers
Of course, there is one major obstacle when it comes to space-based data centers. Unlike undersea data centers, which might theoretically be elevated or made accessible to humans, data centers launched into space would have to be completely maintenance-free. That is a significant obstacle to overcome because sending out IT astronauts for repair or maintenance missions is neither feasible nor cost-effective! Furthermore, many firms like to know exactly where their data is housed and to be able to visit a physical site where they can see their servers in action.
While there are some obvious benefits in terms of speed, there are also concerns associated with pushing data and computing power into orbit. In 2018, Capitol Technology University published an analysis of many unique threats to satellite operations, including geomagnetic storms that cripple electronics, space dust that turns to hot plasma when it reaches the spacecraft, and collisions with other objects in a similar orbit.
The concept of space-based data centers is intriguing, but for the time being-and until many problems are worked out-data centers will continue to dot the terrain and the ocean floor.
Elite Teams recover Systems from Failures in No time (MTTR)
Credits: Published by our strategic partner Kaiburr
Effective Teams in a right environment under Transformative Leadership by and large achieves goals all the time, innovates consistently, resolves issues or fixes problems quickly.
DevOps is to primarily improve Software Engineering practices, Culture, Processes and build effective teams to better serve and delight the Users of IT systems. DevOps focuses on productivity by Continuous Integration and Continuous Deployment (CI-CD) to effectively deliver services with speed and improve Systems Reliability.
The productivity of a System is higher with high performance teams and slower with low performance teams. High performance teams are more agile and highly reliable. We can have better insights on Team performance by measuring Metrics.
DORA (DevOps research and assessment) with their research on several thousands of software professionals across wide geographic regions had come up with their findings that the Elite, High performance, medium and low performance can be differentiated by just the four metrics on Speed and Stability.
The metric βMean time to restore(MTTR) β, is the average time to restore or recover the system to normalcy from any production failures. Improving on MTTR, Our teams become Elite and reduces the heavy cost of System downtime.
Measure MTTR
MTTR is the time measured from the moment the System fails to serve the Users or other Systems requests in the most expected way to the moment it isΒ brought back to normalcy for the Systemβs intended response.
The failure of the System could be, because of semantic errors in the new features or new functions or Change requests deployed, memory or integrationΒ failures, malfunctioning of any physical components, network issues, External threats(hacks) or just the System Outage.
The failure of the running system against its intended purpose is always an unplanned incident and its restoration to normalcy in the least possible time depends on the teamβs capability and its preparedness. Lower MTTR values are better and a higher MTTR value signifies an unstable system and also the teamβs inability to diagnose the problem and provide a solution in less time.
MTTR doesnβt take into account the amount of time and resources the teams spend for their preparedness and the proactive measures but its lower value indirectly signifies teams strengths, efforts and Savings for the Organization. MTTR is a measure of team effectiveness.
As per CIO insights, 73% say System downtimes cost their Organization more than $10000/day and the top risks to System availability are Human error, Network failures, Software Bugs, Storage failures and Security threats (hacks).
How to Calculate MTTR
We can use a simple formula to calculate MTTR.
MTTR, Mean time to restore = Total Systems downtime / total no. of Outages.
If the System is down for more time, MTTR is obviously high and it signifies the System might be newly deployed, complex, least understood or it is an unstable version. A system down for more time and more frequently causes Business disruptions and Users dissatisfaction. MTTR is affected by the teamβs experience, skills and the tools they use. A highly experienced, right skilled team and the right tools they use helps in diagnosing the problem quickly and restoring it in less time. Low MTTR value signifies that the team is very effective in restoring the system quickly and that the team is highly motivated, collaborates well and is well led in a good cultured environment.
Well developed, elite teams are like the Ferrari F1 pit shop team, just in the blink of an eye with superb preparedness, great coordination and collaboration, they Change tyres, repairs the F1 Car and pushes it into the race. MTTRβs best analogy is the time measured from the moment the F1 Car comes into the pit shop till the moment it is released back onto the F1 track. All the productivity and Automation tools our DevOps teams use are like the tools the F1 pitstop team uses.
How to improve, lower MTTR
Going with the assumption that a System is stable and still the MTTR is considerably high then there is plenty of room for improvement. In the present times of AI, we have the right tools and DevOps practices to transform teams to high performance and Systems to lower MTTR. Reports of DORA says high performance teams are 96x faster with very low mean time to recover from downtime.
It seems they take very less time, just a few minutes to recover the System from failures than others who take several days. DevOps teams that had been using Automation tools had reduced their costs at least by 30% and lowered MTTR by 50%. The 2021 Devops report says 70% of IT organizations are stuck in the low to mid-level of DevOps evolution.
Kaiburrβs AllOps platform helps track and measure MTTR by connecting to tools like JIRA, ServiceNow, Azure Board, Rally. You can continuously improve your MTTR with near real time views like the following
You can also track and measure other KPIs, KRIs and metrics like Change Failure Rate, Lead Time for Changes, Deployment Frequency. Kaiburr helps software teams to measure themselves on 350+ KPIs and 600+ Best Practices so they can continuously improve every day.
Reach us at marketing@sifycorp.com to get started with metrics driven continuous improvement in your organization.
Credits: Published by our strategic partner Kaiburr
Visit DevSecOps – Sify Technologies to get valuable insights
AI and human existence: The ABCs of ADLs
Explained: How even the simplest of day-to-day activities are deeply influenced by ML & AI. Will it become a life-changer?
On any given day, a regular human being does several activities which are performed ritually and without fail. From cleaning to eating and drinking and finding our way across our environments, human life involves many activities that are deemed Activity of Daily Living, or ADL. We have performed these activities in the past without any technological help or advice. Throughout human history, man has invented many a tool to improve the execution of these ADLs, and our current age is no different. Technologies such as βMachine Learningβ and βArtificial Intelligenceβ have enabled many advancements to our lifestyle. Some assist us, however there a few that we need to be cautious about.
Let us stride through these ADLs one by one by following a simple activity timeline of a personβs one-day life as we discover how Machine Learning is shaping human life.
Morning β 5 AM to 10 AM
A day for us typically starts with several activities. Brushing, eating, washing, etc., are a few morning routines that we follow in our day-to-day life.
A day begins by waking up from sleep. Nowadays smart devices such as wrist-fit bands, and smartwatches tell us more about our sleep. These devices take leverage of AI and Machine Learning to provide accurate results with improvement. They help us to improve our sleep quality and behavior which in turn improves our health.
After waking up, we essentially brush our teeth to maintain hygiene and dental wellness. Even in this activity with the help of novel smart wrist devices and smart toothbrushes which use AI and Machine Learning, we study and measure our hand movements, direction, speed, etc., that improve the quality of our brushing which in turns keep our oral health in check. An example of such an application is Oral B Genius X.
Nowadays, washing our hands regularly to maintain hygiene and immunity is very important especially given the COVID scenario around the globe. Many kinds of research have been made to take advantage of the technological development to help in monitoring hand hygiene and give a quality assessment to an individual. Many privatized hospitals have tied up with several industries to implement a smart solution for providing hand hygiene quality assessment. The doctors from these hospitals take advantage of it daily to improve their hygiene and their patientsβ as well. An example of such an application is The MedSense Clear system by the MIT Medlab Alumni.
Physical health and maintaining shape have become very underrated due to the new awareness around mental health and its importance. Nevertheless, staying in shape is a very important aspect of peopleβs lives as it indirectly constitutes mental well-being. Diet planning and eating healthy is something that must be taken care of. With the help of smart mobile and computer applications, we nowadays plan our diet efficiently. With the rise of βMachine Learningβ, this system is scrutinized, and further research is being conducted to find solutions for problems such as peopleβs preferences in their eating habits to provide an even better solution.
Mid-Day Activities β 10 AM to 3 PM
Mid-day activities constitute a very wide range of tasks. We regularly use map applications to commute to a certain location. These applications use AI and Machine Learning extensively to provide the best route possible by predicting traffic and other obstacles well before we commute. These applications suggest the best means of transport and the best route to take, they even track and alert us on breakdown of transport services. Examples of such applications are Google Maps, Apple Maps, etc.
Many people work in closed environments either in the office or at home. We are always sedentary, and desk bound. It becomes inevitable to take breaks and go for a short walk and stretch ourselves. And, equally important is to keep ourselves hydrated. With the help of smart devices, we can track the amount of time that we sit continuously. They help us to take a break or even correct our posture if required. Some also help us stay hydrated and suggest improvements based on the environment and atmosphere quality around us. Examples of such devices are Apple Watch, Mi Band, etc.
Evening Activities β 3 PM to 8 PM
Evenings are when we generally try to relax after work and indulge in leisure activities that differs from individual to individual. These activities involve a lot of AI and Machine Learning as it takes advantage of our data right from our preferences to our recent practices. These technology-driven applications and systems need to be handled with utmost caution. We all use the e-commerce facility extensively as it helps us to reduce the time and energy to buy a product as it enables us to shop from wherever we are. This has huge benefits. But we sometimes are ignorant and innocent about the implications that might come. Few applications read our technology usage colossally as they keep track of us more than we know. They improve their recommendation system using this data with the help of AI and Machine Learning and suggest products well before we want to search. If we are not using an official or a recognized application, we are at risk of a privacy breach with the history of our shopping data being stolen or hacked.
Social media applications have invaded our lives. We connect via different platforms to share, exchange ideas and also to relax. However, some of these applications hold sensitive data about us. These applications have plenty of recommendation systems that are constantly updated to feed posts enjoyable to us. But are we compromising on the sensitive data like our name, address, phone number, etc., as well as our preferences that reveal who we are while doing so? An imminent danger to watch out for here is βdata leakageβ. Some of these applications never encode or cipher our passwords. Other activities during the evening include working out, which a few people prefer in the morning too. With the prevailing COVID situation, we have restricted using the gym frequently. Many applications and systems have been created to assist us to work from home. With the help of AI and Machine Learning, they streamline our workout routine.
End Day Activities β 8 PM to 5 AM
End-day activities help us unwind as we call it a day. We perform certain activities as mentioned previously like eating, brushing, washing, etc. Some smart devices assist us by providing an alarm to indicate our sleeping time. This heavily depends upon technology as it tracks our previous sleep history to let us know in what areas we need to improve. These applications help us learn from our sleep pattern, like how much time we spent in deep sleep and so on. This system heavily uses AI and other sensors to read our breathing, heartbeat and measures those accurately to provide insights. Many wristwatches and fit bands provide this feature.
As we get to the end of it, these applications help us save energy and time as well as lead an enriching life.Having said that, we must also be cautious regarding their influence on us. Take precautions and double-check the application for privacy policies. Always use trusted applications, instead of randomly selecting one that might store unwanted cookies to store sensitive data which might lead to an imminent threat. So, whatβs your ADL?
In case you missed:
Blue Brain β a tool or a crutch for humanity?
How Hyperscale Data Centers are reshaping Indiaβs IT
In todayβs times, a common question arises while discussing technology: what is the difference between Data Centers and Hyperscale Data Centers?
The answer: Data Centers are like hotels β the spaces are shared with multiple guests, whereas, in the case of Hyperscale Data Centers, the entire building/campus are dedicated to a single customer.
Companies like Amazon, Google, Microsoft, Facebook, and OTTs, which have millions and millions of end-users, have infused their services into our day-to-day life to cater to our personal and professional needs. Data centers are the backbone of this digital world.
This is where Hyperscale Data Centers come into play and provide seamless experiences to such massive end-users.
The term Hyperscale means the ability of an infrastructure to scale up when the demand for the service increases. The infrastructure comprises of computers, storage, memory, networks etc. The maintenance of such infrastructure is not an easy task. Constant monitoring of the machines, the server hall temperature and humidity control check and other critical parameters are monitored 24Γ7 by the Building Management System (BMS).
Data Centers are important because everyone uses data. It is safe to say that perhaps everyone, from individual users like you and me to multinationals, used the services offered by data centers at some point in their lives. Whether youβre sending emails, shopping online, playing video games, or casually browsing social media, every byte of your online storage is stored in your data center. As remote work quickly becomes the new standard, the need for data centers is even greater. The cloud data center is rapidly becoming the preferred mode of data storage for medium and large enterprises. This is because it is much more secure than using traditional hardware devices to store information. Cloud data centers provide a high degree of security protection, such as firewalls and back-up components, in the event of a security breach. The COVID-19 pandemic paved the way for the work-from-home culture, and the global internet traffic increased by 40% in 2020
Also, the rise of new technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), 5G, Augmented Reality (AR), Virtual Reality (VR) and Blockchain caused an explosion of data generation and an increased demand for storage capacities.
Cloud infrastructure has helped businesses and governments with solutions to respond to the pandemic. To cater to such needs, the demand for cloud data center facilities has increased. A heavy infrastructure with a lot of power is needed to cater to such needs.
Data Centers have quite a negative impact on the environment, because of the large consumption of power sources and has 2% of the global contribution of greenhouse gas emissions. To reduce these carbon footprints and work towards a sustainable environment, many data center providers globally have started using power from renewable energy sources like solar and wind energy through Power Purchase Agreements (PPA). The Data Center power consumption can be lowered by regularly updating their systems with new technologies and regular maintenance of the existing infrastructure.
The Indian market will see multifold growth in the Data Center industry due to ease of doing business in the country and thanks to the attractive subsidiaries provided by the state governments, huge investments are committed in the next four years.
Interesting facts about Data Center:
- A large Data Center uses the electricity equivalent to a small Indian town.
- The largest data center in the world is of 10.7 million sq.ft. in China, approximately 1.5 times of the Pentagon building in USA.
- Data Centers will nearly consume 2% of the worldβs electricity by 2030. Hence, the Green Data Center initiatives are taken up by various organizations.
How OTT platforms provide seamless content β A Data Center Walkthrough
With the number of options and choices available, it almost seems like thereβs no end to what you can and canβt watch on these platforms. It shouldnβt be difficult for a company like Netflix to store such a huge library of shows and movies at HD quality. But the question remains as to how they provide this content to so many people, at the same time, at such a large scale?
The India CTV Report 2021 says around 70% users in the country spend up to four hours watching OTT content. As India is fast gearing up to be one of the largest consumers of OTT content, players like Netflix, PrimeVideo, Zee5 et al are competing to provide relevant and user-centric content using Machine Learning algorithms to suggest what content you may like to watch.
With the number of options and choices available, it almost seems like thereβs no end to what you can and canβt watch on these platforms. It shouldnβt be difficult for a company like Netflix to store such a huge library of shows and movies at HD quality. But the question remains as to how they provide this content to so many people, at the same time, at such a large scale?
Here, we attempt to provide an insight into the architecture that goes behind providing such a smooth experience of watching your favourite movie on your phone, tablet, laptop, etc.
Until not too long ago, buffering YouTube videos were a common household problem. Now, bingeing on Netflix shows has become a common household habit. With Data-heavy and media-rich content now being able to be streamed at fast speed speeds at high quality and around the world, forget about buffering, let alone downtime due to server crashes (Ask an IRCTC ticket booker). Letβs see how this has become possible:
Initially, to gain access to an online website, the data from the origin server (which may be in another country) needs to flow through the internet through an incredulously long path to reach your device interface where you can see the website and its content. Due to the extremely long distance and the origin server having to cater to several requests for its content, it would be near impossible to provide content streaming service for consumers around the world from a single server farm location. And server farms are not easy to maintain with the enormous power and cooling requirements for processing and storage of vast amounts of data.
This is where Data Centers around the world have helped OTT players like Netflix provide seamless content to users around the world. Data Centers are secure spaces with controlled environments to host servers that help to store and deliver content to users in and around that region. These media players rent that space on the server rather than going to other countries and building their own and running it, and counter the complexities involved in colocation services.
How Edge Data Centers act as a catalyst
Hosting multiple servers in Data Centers can sometimes be highly expensive and resource-consuming due to multiple server-setups across locations. Moreover, delivering HD quality film content requires a lot of processing and storage. A solution to tackle this problem areΒ Edge Data CentersΒ which are essentially smaller data centers (which could virtually also be a just a regional point of presence [POP] in a network hub maintained by network/internet service providers).
As long as there is a POP to enable smaller storage and compute requirements and interconnected to the data center, the edge data center helps to cache (copy) the content at its location which is closer to the end consumer than a normal Data Center. This results in lesser latency (or time taken to deliver data) and makes the streaming experience fast and effortless.
Role of Content Delivery Networks (CDN)Β
The edge data center therefore acts as a catalyst to content delivery networks to support streaming without buffering. Content Delivery Networks (CDNs) are specialized networks that support high bandwidth requirements for high-speed data-transfer and processing. Edge Data Centers are an important element of CDNs to ensure you can binge on your favorite OTT series at high speed and high quality.
Although many OTT players like Sony/ Zee opt for a captive Data Center approach due to security reasons, a better alternative would be to colocate (outsource) servers with a service provider and even opt for a cloud service that is agile and scalable for sudden storage and compute requirements. Another reason for colocating with Service providers is the interconnected Data Center network they bring with them. This makes it easier to reach other Edge locations and Data Centers and leverage on an existing network without incurring costs for building a dedicated network.
Demand for OTT services has seen a steady rise and the pandemic, in a way, acted as a catalyst in this drive.
However, OTT platform business models must be mindful of the pitfalls.
Target audience has to be top of the list to build a loyal user base. New content and better UX (User Experience) could keep subscribers, who usually opt out after the free trial, interested.
The infrastructure and development of integral elements of Edge Data Centers are certain to take centerstage to enable content flow more seamlessly in the future that would open the job market to more technical resources, engineers and other professionals.
Digital transformation powered by Cloud@core
Premier life insurer embarks on Sifyβs manage-services-led hybrid cloud transformation of legacy IT with zero downtime and 25% reduction in annual cost.
Project Objective
Implement integrated future-ready digital infrastructure on hyperscale cloudβ
Project Model
Fully services model (vis-Γ -vis existing capex + services model)
Sifyβs Uniqueness
Integrated value proposition built on: Cloud-Adjacent DC, Hyperscale (AWS) partnership, Hybrid Cloud Management Platform, DRaaS, DC and Sify Cloud Interconnect, Security Services and Migration Services
Integrated Value and Outcome
Cloud@core products and services:
- All cloud-ready applications migrated to hyperscale (AWS)β
- Legacy apps and infra in cloud-adjacent DC with near 0 latencyββ
- DR at a different seismic zone with hybrid deployment of post cloud infra and DRaaS from cloud@core
- Hybrid security framework for both hosted & cloud footprintβ
- Full lifecycle responsibility through assessment, zero downtime migration & implementation within a record timelineββ
- Hybrid cloud management platform for management of the Hosted Private and AWS Cloud footprint
Value for client
25% reduction in yearly cost, return on investments, agile digital-ready infrastructure, end-to-end ownership by one partner, no conflict of interest with application partners.
