November, 2023

In today’s hyper-connected and fast-paced global marketplace, connectivity is the lifeblood needed to keep businesses running smoothly. Enterprise, small or large, need reliable and fast communication networks to work and grow.

Organizations are given access to a communication network that is entirely dependable, quick, flexible, and cost effective due to MPLS and its “labelling protocol” Businesses are considering shifting to MPLS networks due to the clear advantages of MPLS connectivity, However, choosing an MPLS service provider can be time-consuming and complicated given the industry’s diverse offerings and management methods.

There are criteria that needs to be taken into consideration while evaluating the various telecom service providers to choose the most suitable solution for your businesses.

The first element that businesses need to check is the network coverage offered by the service provider. It is critical to confirm that the network carrier’s service area includes all your targeted locations, and if global connectivity is required, the network service can provide reliable international coverage. Therefore, latency, jitter, resilience, and cost of the MPLS network are all directly impacted by the service providers’ reach and coverage.

The second criteria is to consider the Network Project Management and Monitoring, because complex networks require constant management to run smoothly. Proactive management can result in reduced expense, fewer outages, and enhanced security. Therefore, the specifics of pricing and the range of services offered under the heading of network management and monitoring services must be understood by businesses.

In order to free up IT employees, it is also crucial to check whether the service provider monitors the entire network and provides managed services.

The third criteria to be mindful about while choosing an MPLS service provider is their Network Security. Security capabilities of the MPLS service provider need to be scrutinized closely. Businesses need to ensure that service providers can support and monitor the entire network closely and provide access to a dedicated security infrastructure, including experienced NOC engineers to ensure guaranteed uptime.

Another sure-fire way to assess the capabilities of the service provider is to check the SLAs (Service Level Agreements) offered. While experienced service providers with tested capabilities are usually willing to back their services with SLAs, it pays off to compare SLAs of several MPLS network providers. It is also helpful to look at their existing clients and check references to evaluate their performance.

Subsequent to comparing SLA’s, it is important for businesses to assess, evaluate and understand the Class of Service (CoSs) and Quality of Service (QoS) offered by the carrier. The Quality of Service (QoS) is impacted by the carrier’s consolidation and prioritisation decisions for different traffic categories. These standards must comply with the requirements of your business, and the contract with the service provider must specifically describe and list these service commitments.

While the mentioned points form a base to shortlist the right MPLS network provider, other factors such as routing protocols, staff expertise, 3rd party partnerships, network architecture and future scalability capabilities must also essentially be a part of the decision making process.

Despite the fact that cost is a major consideration in any business decision, your company’s long-term objectives and alignment with current and future needs should ultimately take precedence. After all, your MPLS Network will form the backbone of your business while looking at it from a wider perspective.

 

Credits: Published by our strategic partner Kaiburr

Digital transformation has turned every organization into a software company, regardless of the industry they are part of. Companies are required to react faster to changing customer needs but on the other hand, deliver stable services to their customers. In order to meet these requirements, DevOps teams and lean practitioners have to constantly improve themselves.

DORA metrics addresses this need by providing objective data to measure the performance of software delivery teams and drive product improvement.

What are DORA metrics?

To determine the right metrics to track the effectiveness and efficiency of software teams, DevOps Research and Assessment team came up with four metrics that can be used by teams to measure their performance and find out whether they are “low performers” to “elite performers”.

The four metrics used are deployment frequency (DF), Lead time (LT), Mean time to recovery (MTTR), and Change Failure Rate (CFR).

Lead Time metrics help a team to:

• Spot the areas for improvement when it comes to the delivery process
• Understand what actions to take to streamline the process
• Deliver quickly, ensuring higher quality
• Ensure stability and accountability of a product
• Make decisions based on data, not assumptions
• Spot problems like the lack of people or inefficiency of testing
• Deliver more value to clients by ensuring fast iterations and better responsiveness to feedback
• Increase the business value of a product faster and reduce waste

Let’s try to understand Lead Time in more detail.

Lead Time: As mentioned earlier, it is the time from when request for a requirement/ feature gets raised to the time it gets released into production i.e., the total time elapsed from the creation of work items to their completion. It measures the time spent in development, testing, implementation and integration but does not include the time spent in backlog prioritization and design. Shorter product  lead times enable faster feedback on what is getting built and allows for quick correction.

The lower the lead time for changes, the more efficient a DevOps team is in deploying code.

In order to measure Lead Time, two pieces of data (or timestamps) are required: The exact time of the commit and the exact time of the deployment – in other words, the time from start to finish of a product. The average time is then used as an indicator for overall performance.

The question this metrics answers is, how long does it take to go from code committed to code successfully running in production.

How to calculate Lead Time?

To measure Lead Time for Changes, we need two pieces of data:

• The time taken for commit
• The time taken to release it into production

To measure lead time, calculate the time elapsed between making a commit and releasing it to production.

Calculating lead time beginning after creating a task helps teams understand the full feature life cycle, including the design and planning stages.

Based on this metric, performance of a DevOps team can be divided into four categories as per the following benchmark:

• Elite Performers: Less than one hour
• High Performer: Between one day and one week
• Medium Performers: Between one month and six months
• Low Performers: More than months

Collecting Data

One of the ways to collect data is to simply ask developers for the average lead times. A better way would be with the help of a dedicated DevOps metrics platform.

Integrating the DevOps software and committing data into everyday tracking using an automated metrics platform will give you more accurate results that are easier to aggregate and analyze. 

Why should we measure Lead Time?

Lead Time is an indicator of how quickly a team responds to needs and fixes. It represents the efficiency of the process, code complexity, and team’s capacity.

The metric helps to understand how long it takes to get changes to production. If Lead Time is too high, it shows inefficiencies and bottlenecks in the process hurting team velocity and morale. A short lead time shows that a team reacts to feedback and changes quickly, sets their industry’s pace for innovation, and ultimately wins the marketplace. The team’s goal should be to reduce Lead Time for changes and react to issues in a timely manner.

In high-performing organizations, developers integrate commits to the main branch with fewer roadblocks and less time is spent waiting. Hence, deploying features into production becomes effortless, automatic and smooth.

This metric is also essential while working with clients, who prefer to work with a team that responds to urgent bug fixes within hours.

Causes of high Lead Time

High Lead Time for changes is most often caused by inefficient processes and introducing too big changes to production. Other reasons are:

• Poor Definition of Ready
• Manual testing
• Blocks and dependencies
• Inefficient development process
• Complex routes to production
• Huge changes introduced to code

How to optimize Lead Time?

Optimizing and reducing lead time can be beneficial for a DevOps team and for the success of business.

To optimize Lead Time for Changes, a team has to:

• Work with smaller iterations – smaller changes will help developers to get feedback faster and resolve issues quickly.
• CI/ CD – Automate the pipeline – Continuous deployment is a commonly used strategy in software development process where each code commit or merged pull request is automatically released to production once it passes the automated testing stage. CI/CD integrates the continuous deployment strategy with continuous integration, a practice where code changes are made regularly.

By employing these two strategies, CI/CD enables development teams to deploy code to production more efficiently and quickly.

By implementing CI/CD, speeding up the delivery process and reducing manual tasks, lead time can be reduced substantially.

• Test Automation – Use automated tests at every stage of CI/CD pipeline to spot issues earlier and to make sure the build meets acceptance criteria.

Test automation replaces many of the manual checks for developers and also allows for any necessary changes to be made early in the development life cycle, reducing change failure rates. When you integrate CI/CD with test automation, you can reduce your overall lead time, allowing you to release products to customers at a much faster rate.

• Automate code reviews – Code review is one such process that involves a lot of manual intervention. Automating this activity helps to improve quality and save time.
• Implement Test Driven Development – Integrate testing into the development process i.e., Developers have to understand the requirement, create unit test cases, execute them and then move the code into the test environment. This initiative has the following benefits:
  • Raises the level of code quality and guarantees that all procedures are carried out.
  • Enables the developers to plan their code before writing the same thereby reducing the time required for development.
  • Supports the development teams in delivering high-quality products with fewer bugs and cleaner code.

All these initiatives will have a significant effect in reducing the lead time.

• Identify bottlenecks and eliminate them – One of the initiatives is to identify the bottlenecks in areas like change approval, streamline the required processes and eliminate the processes which do not add any value.

By measuring and tracking Lead Time, a team can make better, more informed decisions about what can be improved and understand how to do that. When Lead Time improves, a team can be sure that they’re making good choices and delivering more value to customers and users of a product. A team should not only thrive to become an elite performer but also has to deliver better business value faster.

Kaiburr helps every product engineering team to measure their performance near real time using industry standard KPIs like those shown below –

You can then drill down to specific trends in lead time for a portfolio, project, initiative, team, epic, sprint or release like the below –

You can also compare lead time with other KPIs to identify bottlenecks and improve –

If you want to get started with your Stage Gate Compliance journey using Kaiburr reach us at marketing@sifycorp.com

Credits: Published by our strategic partner Kaiburr

 

The IT landscape is accelerating at an unprecedented rate powered by AI and cloud infrastructure. Businesses across industries are exploring the potential of hybrid IT in their digital transformation journey. Today, agility, resiliency and security are of paramount importance to businesses, as they continuously focus on innovation, scalability, operational efficiency, and growth.

By leveraging automation through AI/ML across hybrid cloud solutions, business leaders unlock a range of benefits, including embracing an automated agile, scalable, and secure platform while maintaining a cost-competitive edge amidst the changing market dynamics. Ultimately, all of this enables businesses to maximize the value of their investments, drive sustainable growth, faster go-to-market and shorter time to adapt.

However, before enterprises set out to build the right hybrid cloud ecosystem, business leaders need to address some critical key points following below:

• App stratification to adopt multi-cloud and hybrid IT
• Building the right architecture to adopt multi cloud and enabling hybrid IT
• Seamless migration milestone mapping to meet business objectives
• Visibility for right planning and productivity
• Unified platform driving cost savings, operational efficiency, and cloud governance

Understanding these key factors has a profound impact on maximizing the benefits that hybrid IT offers. Now, let’s look at the fundamentals of hybrid IT, its challenges, and how AI/ML helps in mitigating these challenges.

What constitutes Hybrid IT and makes it advantageous?

Hybrid IT refers to an IT infrastructure environment that combines elements of traditional on-premises or in-house data centers with cloud-based solutions. Hybrid cloud combines both public clouds as well as managed private clouds. It brings together the best of both worlds, allowing organizations to leverage the scalability and cost-efficiency of the public cloud for their elastic workloads and managed private cloud for fixed and regulated workloads.

Challenges while adopting multi-cloud and hybrid IT

While it has a lot to offer, hybrid IT infrastructure comes with its own set of challenges. Security & data protection, regulatory compliance, cloud sprawl, performance management, vendor lock-in, network complexity, and cost management are among the few challenges enterprises faces while adopting multi-cloud and enabling hybrid It. Let’s look at the two major challenges-

• Data Governance: As organizations navigate the complexities of managing data across both public and private cloud environments, ensuring data security and compliance becomes both difficult and paramount.
• Integration and Management: The technical challenges of integrating and managing diverse cloud platforms and technologies can be daunting. Ensuring seamless workload allocation, optimizing resource usage, and overcoming challenges in managing and analyzing large data sets for business intelligence is essential.

Mitigation plan for the challenges above

Analyzing your existing cloud infrastructure to transform it into a future-ready state that aligns with your organization’s strategic business objectives is the first step in your cloud journey. It will help you identify and address the real-time challenges while adopting multi/hybrid cloud. These challenges can be mitigated with the help of emerging technologies like Artificial Intelligence and Machine learning.

The infusion of AI and machine learning technologies takes hybrid cloud management to the next level. By creating a multiplier effect, the combination accelerates transformation with result-driven capabilities, such as intelligent automation, predictive and proactive analytics, and enhanced data processing. It enables enterprises to extract valuable insights from massive amounts of data and empower them to make data-driven decisions.

AI/ML can also optimize the management and utilization of hybrid cloud resources by dynamically allocating workloads, optimizing resource allocation, and ensuring efficient performance across environments, including predicting and automating recovery from failures. It also enables advanced security and compliance to identify and mitigate potential threats in real-time, detect anomalies and patterns, and proactively address security risks.

Enterprises can leverage AI/ML to make real-time decisions while running modern applications and derive insights across velocity of omnichannel data. An AI/ML enabled centrally managed platform on the cloud and edge locations can seamlessly sync data pipelines with multiple connectors from applications, APIs, and databases to data warehouses/lakes and back. This enables intelligent decision making with AI predictions from operations data at the edge. Also, it empowers customers with necessary architectural guidance and design.

Impact of AI/ML on FinOps

AI/ML-enriched hybrid cloud environments have a significant impact on FinOps by increasing overall productivity with substantial cost savings. AI/ML algorithms can be utilized to effectively manage and orchestrate several instances, virtual machines (VMs), containers, and legacy infrastructure components. Furthermore, natural language processing (NLP) and generative AI can be leveraged for hybrid cloud transformation, code debugging, and user experience (UX) improvement.

For instance, enterprises can identify customer segmentation by blending ML and existing techniques to deepen product expertise and marketing effectiveness. This directly drives down acquisition costs and increases retention rates.

The resulting automation and decisive business intelligence helps to optimize resource allocation and cost management in the backend while enabling businesses to proactively address potential issues. AI/ML in hybrid cloud environments also influences customer focused business operations by unlocking enhanced customer experiences, improving response times, and driving higher customer satisfaction (CSAT) scores. By performing sentiment analysis, businesses can spot and prevent issues with the product experiences. This, in turn, positively impacts key performance indicators, turns reviews into actionable insights, and contributes to the organization’s success.

Sify’s Cloud Anywhere: Enhancing operational efficiency

With the changing business needs and market dynamics, enterprises must start investing in the right tools, technology, and partner that can assist them with change management, cost & risk management, and eliminating other challenges. Sify empowers organizations to navigate the complexities of multi cloud and hybrid IT environments with confidence. Our AI/ML-enabled hybrid cloud solutions provide a reliable foundation for successful digital transformation initiatives.

Sify’s Cloud Infinit is a next generation enterprise grade cloud infrastructure and managed services offering with highly available architecture and automation-led intelligent operations, empowers enterprises with business resiliency on a larger scale and minimum investment.

Here are a few strengths of Sify Cloud Anywhere:

• Unified dashboards
• Unified monitoring
• Multi-skilled support
• Integrated delivery framework
• Cloud connect with low latency
• Expertise in DC, Network, and Managed services
• Services for infrastructure assessment and consultation

Sify FinOps to fulfil cloud promise

Through its comprehensive FinOps capabilities, Sify offers industry-leading AI/ML-driven cloud management platforms and solutions that give organizations unified visibility into their cloud infrastructure. With Sify’s expertise and AI/ML-driven solutions, organizations can confidently navigate their cloud transformation, optimize costs, and streamline their operations for maximum effectiveness and success. Here’s how Sify AI/ML enabled cloud platform delivers-

1. Cost Governance:
   • Rightsizing of cloud resources by simplifying analytics on overall utilization, trending, and resource & capacity planning
   • Enables multi-cloud coverage for enhanced cost optimization
   • Financial domains for project management, budget control and chargeback enablement
2. Real-time ML based observability:
   • Real-time cloud waste analysis, smart startup & shutdown scheduler recommendations
   • Anomaly detection to identify problems now than later
   • Real-time observability of cost and performance metrics

Conclusion : Changing the game with AI/ML infused hybrid clouds

Automation holds significant potential at every stage of an organization’s cloud journey. Whether they are taking the first step towards cloud adoption, seeking to reduce bill shocks and manage multiple cloud ecosystems efficiently, or aiming to modernize legacy applications, AI/ML has a lot to offer. Reimaging your digital transformation with automation across multi cloud and hybrid IT requires a holistic approach. It involves tech adoption, process reengineering, and a cultural shift with the organization. By embracing automation, enterprises can unlock new levels of efficiency, scalability, and innovation while mitigating risks associated with modern IT landscape.

Sify provides a range of customizable solutions to cater to businesses of all sizes, whether they are embarking on comprehensive IT transformation initiatives, focused application migrations, infrastructure modernization projects, or adopting the lifts-and-shifts land-and-expand approach. With its diverse portfolio, Sify ensures that organizations can find tailored solutions that align with their specific needs and objectives, regardless of their scale or scope of transformation.

To learn more about Sify Hybrid and Multi- Cloud Services, visit here!