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Understanding Monitoring and Observability:

In today’s fast-paced digital landscape, where agility and scalability are paramount, cloud-native
environments have become the cornerstone of modern businesses. Leveraging cloud-native
technologies such as containers, microservices, and serverless computing empower organizations to
innovate rapidly and deliver exceptional user experiences. However, with this transition comes the
challenge of ensuring robust monitoring and observability to maintain the performance, reliability, and
security of these dynamic environments.

Monitoring vs. Observability:

Monitoring and observability are two critical pillars of modern IT operations, each serving a distinct
purpose:

Monitoring involves collecting and analyzing metrics, logs, and other data points to gain insights into the
health and performance of systems and applications. It focuses on detecting anomalies, identifying
trends, and triggering alerts when predefined thresholds are breached.

Observability extends beyond traditional monitoring by emphasizing the ability to understand and debug
complex, distributed systems. It encompasses a holistic view of the entire system’s behavior, including
interactions between various components, to facilitate root cause analysis and troubleshooting.

Challenges in Cloud-Native Environments:

Cloud-native environments introduce unique challenges for monitoring and observability:

Dynamic Infrastructure: With containers, auto-scaling, and ephemeral resources, infrastructure components are constantly changing, making it challenging to track and monitor them effectively.

Microservices Architecture: Decomposing applications into microservices enhances scalability and
agility but increases the complexity of monitoring. Each microservice may have its own metrics and logs,
requiring a cohesive approach to aggregate and analyze data across the entire ecosystem.

Distributed Systems: As applications span multiple containers, services, and even cloud providers,
traditional monitoring tools may struggle to provide a unified view of the entire system, hindering effective
observability.

Best Practices for Monitoring and Observability:

To address these challenges and harness the full potential of cloud-native environments, organizations
should adopt the following best practices:

Instrumentation: Embed monitoring and observability capabilities directly into applications and
infrastructure components using standardized frameworks such as Prometheus, OpenTelemetry, and
Fluentd. This ensures consistent data collection and enables deep visibility into system behavior.

Unified Monitoring Platform: Implement a centralized monitoring platform that can ingest, correlate,
and visualize metrics, logs, and traces from across the entire stack. Solutions like Grafana,
Elasticsearch and Splunk provide powerful tools for aggregating and analyzing telemetry data.

Service Mesh: Utilize service mesh technologies like Istio and Linkerd to enhance observability by
providing transparent communication, traffic management, and security between microservices. Service
meshes offer built-in telemetry features for monitoring service-to-service communication and capturing
distributed traces.

Automated Alerting and Remediation: Implement intelligent alerting mechanisms that leverage
machine learning and anomaly detection to proactively identify and respond to issues before they impact
users. Integrate with incident management tools like PagerDuty and OpsGenie to streamline incident
response workflows.

Continuous Improvement: Embrace a culture of continuous improvement by regularly reviewing and
refining monitoring and observability practices. Solicit feedback from stakeholders, conduct post-incident
reviews, and iterate on monitoring strategies to adapt to evolving business requirements.

Choosing the Right Monitoring and Observability Tools:

Selecting the appropriate monitoring and observability tools is crucial for effectively managing cloud-
native environments. Organizations should evaluate tools based on factors such as scalability, interoperability, ease of integration, and support for cloud-native technologies. Some popular tools and
platforms include:

Prometheus: An open-source monitoring and alerting toolkit designed for cloud-native environments,
with support for multi-dimensional data collection and querying.

Elastic Stack (ELK): A comprehensive suite of tools, including Elasticsearch, Logstash, and Kibana, for
collecting, storing, and visualizing logs and metrics data.

Grafana: A visualization and analytics platform that integrates with various data sources, including
Prometheus, to create customizable dashboards and monitor system performance.

OpenTelemetry: A vendor-neutral observability framework that provides libraries and instrumentation for
collecting and exporting telemetry data from applications and infrastructure.

Jaeger: An open-source distributed tracing system for monitoring and troubleshooting microservices-
based architectures, compatible with OpenTelemetry.

Real-World Use Cases and Case Studies:

Use Case 1: Retail E-commerce Platform:
A retail e-commerce platform adopts a microservices architecture to scale and innovate rapidly. By
leveraging Prometheus and Grafana, the platform monitors key metrics such as response times, error
rates, and inventory levels across its distributed services. When a surge in traffic occurs during peak
shopping seasons, automated alerting notifies the operations team of potential performance bottlenecks,
enabling proactive optimization and ensuring a seamless shopping experience for customers.

Use Case 2: Financial Services Provider:
A financial services provider migrates its legacy monolithic applications to a cloud-native environment to
improve agility and reduce costs. Using the Elastic Stack, the organization gains visibility into transaction
logs, user authentication events, and system performance metrics. With real-time monitoring and
analysis capabilities, the provider detects suspicious activities, such as unauthorized access attempts or
unusual trading patterns, and promptly responds to mitigate security risks and comply with regulatory
requirements.

As cloud-native technologies continue to evolve, monitoring and observability practices will also undergo
significant transformations. Some emerging trends and challenges include:

AI-driven Observability: The integration of artificial intelligence and machine learning technologies will
enable predictive analytics and automated anomaly detection, empowering organizations to anticipate
and mitigate issues before they impact users.

Serverless Monitoring: With the growing adoption of serverless computing, monitoring and observability
tools will need to adapt to the unique characteristics of serverless architectures, such as event-driven
execution and ephemeral workloads.

Security Monitoring: As cyber threats become more sophisticated, organizations must prioritize security
monitoring to detect and respond to security incidents in real-time, safeguarding sensitive data and
protecting against breaches.

Multi-Cloud Observability: With the increasing use of multi-cloud and hybrid cloud environments,
organizations will require comprehensive observability solutions that can monitor and analyze data
across disparate cloud platforms and on-premises infrastructure.

Conclusion:

In conclusion, effective monitoring and observability are essential for ensuring the performance,
reliability, and security of cloud-native environments. By adopting best practices, selecting the right tools,
and leveraging real-time insights, organizations can navigate the complexities of cloud-native computing
with confidence. As technology continues to evolve, staying abreast of emerging trends and challenges will be critical for optimizing monitoring and observability strategies and driving business success in the digital era.

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Developed a centralized log management system for a well-known Indian Multinational Conglomerate https://euphoricthought.com/developed-a-centralized-log-management-system-for-a-well-known-indian-multinational-conglomerate/ Fri, 23 Feb 2024 18:14:17 +0000 https://www.euphoricthought.com/?p=3328 The post Developed a centralized log management system for a well-known Indian Multinational Conglomerate appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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CHALLENGES

The client faced challenges related to security and payment. Their existing log management system was inefficient and lacked the capabilities to detect and respond to security threats effectively. The manual log analysis process was time-consuming and prone to errors, resulting in delays in issue resolution and increased operational costs.

The absence of a centralized log management system posed significant hurdles in swiftly pinpointing the underlying causes of payment discrepancies, alongside code bugs, feature malfunctions, API invocation logs, and response message diagnostics.

SOLUTION

To address these challenges, Euphoric suggested Graylog for a comprehensive log management and analysis solution. Key elements of the Graylog implementation included:

  1. Centralized Log Collection: Graylog was deployed to collect logs from various sources, including servers, network devices, and applications, providing a centralized repository for log data.
  2. Real-time Analysis: Graylog’s real-time log analysis capabilities allowed the company to monitor network traffic and security events as they happened, improving threat detection and response times.
  3. Custom Dashboards and Alerts: Customized dashboards and alerting in Graylog provided actionable insights and notifications for security incidents and operational issues.
  4. Enabling Exception Logs – Turning on the Detective Mode. Exception logs were activated, empowering Graylog to capture critical details during unsuccessful payments. This detective mode became the key to understanding the root causes.

BENEFITS

The client gained a complete set of following benefits from the solution provided by Euphoric:

  1. Enhanced Payment System Reliability: Graylog’s real-time log analysis and centralized log collection contribute to a more reliable and robust payment system.
  2. Issue Identification and Resolution: The centralized log management solution empowered Aditya Birla Health Insurance to identify the root causes of payment issues swiftly, leading to quicker resolution and improved customer satisfaction.
  3. Improved Operational Efficiency: Custom dashboards and alerts in Graylog streamline the monitoring process, leading to quicker issue identification and resolution, and enhancing overall operational efficiency.

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Migration from Onpremise to Azure Cloud for a leading airline company based out of South East Asia https://euphoricthought.com/migration-from-onpremise-to-azure-cloud-for-a-leading-airline-company-based-out-of-south-east-asia/ Mon, 29 Jan 2024 12:52:50 +0000 https://www.euphoricthought.com/?p=3157 The post Migration from Onpremise to Azure Cloud for a leading airline company based out of South East Asia appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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CHALLENGES

The client reached out to Euphoric’s team with the following challenges in place:

  1. Complex Infrastructure: The client’s data center housed numerous VMware VMs with interconnected applications, databases, and services. Migrating this complex environment while ensuring data integrity and application compatibility was a primary challenge.
  2. Minimal downtime: The financial services sector operates around the clock. The client required a migration strategy that would minimize downtime during the migration process to avoid any negative impact on customer services.
  3. Resource Allocation: Selecting the appropriate Azure virtual machine sizes and configurations to match the on-premise VMs’ performance was critical to ensure consistent application performance post-migration.
  4. Network Connectivity: Ensuring a seamless network transition from the on-premise environment to Azure’s cloud network infrastructure required careful planning to maintain secure and efficient communication.

 

SOLUTIONS

  1. Assessment and Planning: An in-depth assessment of the on-premise VMware environment was conducted to identify VM dependencies, application interconnections, and performance characteristics. A detailed migration plan was devised, outlining the migration sequence, timelines, and resource allocation. Azure Cloud Design: Azure resources were provisioned to replicate the onpremise VM environment. Azure Virtual Machines were chosen based on the performance and resource requirements of the existing VMs.
  2. Network Connectivity: Azure Virtual Network was established to mirror the onpremise network configuration, including subnets and security groups. Site-toSite VPN or Azure ExpressRoute was configured for secure data transfer. Data Migration: The client used Azure Site Recovery to replicate on-premise VMs to Azure. Once replication was established, a final synchronization was performed to minimize data loss during cutover.
  3. Testing and Validation: A comprehensive testing phase was executed to ensure that the migrated VMs functioned properly in the Azure environment. Application functionality, data integrity, and performance were rigorously tested.
  4. Cutover and Validation: After successful testing, a scheduled maintenance window was chosen for the cutover. During this window, the final synchronization was completed, and DNS records were updated to point to the new Azure VMs.

 

BENEFITS

The client gained a complete set of following benefits from the solution provided by Euphoric:

  1. Minimized Downtime: By using the lift-and-shift approach, ABC Enterprises was able to minimize downtime during the migration. This ensured that critical financial services remained operational without disruptions.
  2. Application compatibility: The migration process retained the integrity of interdependent applications, databases, and services, ensuring seamless functionality post-migration. Performance Consistency: The Azure VMs were selected and configured to match the performance requirements of the on-premise VMs, ensuring consistent application performance.
  3. Scalability: The client gained the flexibility to scale resources up or down as needed by taking advantage of Azure’s scalability features. Cost Savings: While the initial investment was required for Azure resources, the client anticipated long-term cost savings due to reduced hardware maintenance and increased efficiency.
  4. Security and compliance: Azure’s built-in security features and compliance certifications helped ensure that data remained secure and compliant throughout the migration process.

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Evolution of Cloud Computing https://euphoricthought.com/evolution-of-cloud-computing/ Wed, 03 Jan 2024 12:17:51 +0000 https://euphoricthought.com/?p=3058 Cloud Computing is a technology model that provides on-demand access...

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Cloud Computing is a technology model that provides on-demand access to a shared pool of configurable computing resources like servers, storage, networks, etc. It allows users to access and use computing resources over the internet without needing to own or maintain the underlying physical infrastructure.
Cloud Computing has come a long way over the last few decades. The term Cloud Computing was only coined in the early 2000s. Here is a glimpse of Cloud Computing Evolution:-

1950s and 1960s –

During this time period, the computing landscape was dominated by mainframes and early networks. Here are some key aspects of computing during that period:

  • Mainframes and Centralized Computing: Mainframe computers were the primary computing platforms during this time. These large and powerful machines were centralized and typically housed in dedicated data centers. Mainframes were capable of processing large volumes of data and supporting multiple users concurrently.
  • Punch Cards: Input to computers was often done using punch cards. Users would create stacks of punch cards with their programs or data, and these cards would be fed into the computer in batches for processing. This batch-processing approach was a precursor to later computing models.
  • Limited User Access: Computing was primarily the domain of scientists, researchers, and large organizations due to the high cost and complexity of the technology. Users typically had scheduled time slots to access the mainframe.
  • Limited Networking: Computer systems were not interconnected in the way we understand networks today. Most computers operated as standalone units at the time.

1970s and 1980s –

During this time period, computing continued to evolve with advancements primarily in hardware and networking:

  • Mainframes and Mini-Computers: Mainframes remained a dominant computing platform during the 1970s, but mini-computers started gaining popularity. Mini computers were smaller and more affordable than mainframes, making them accessible to a broader range of organizations.
  • Time-Sharing Systems: Time-sharing systems continued to be refined during this period. These systems allowed multiple users to interact with a computer simultaneously by dividing processing time among them. This marked a departure from the batch processing model and contributed to more interactive computing.
  • Emergence of Microprocessors: We witnessed the emergence of microprocessors, leading to the development of personal computers. This shift brought computing power to individuals and small businesses, further decentralizing computing.
  • Client-Server Architecture: The client-server architecture started to gain prominence in the 1980s. This model distributed computing tasks between Clients and Servers. It laid the groundwork for more distributed and collaborative computing environments.
  • Networking Advances: Local Area Networks (LANs) became more prevalent, allowing computers within a limited geographic area to be connected. This facilitated better communication and resource sharing among connected systems. The development of TCP/IP networking protocol during this time contributed to the growth of networking capabilities.

1990s –

The development during the 1990s laid the foundation for cloud computing concepts:

  • Internet Growth: The 1990s saw the rapid expansion of the internet. The World Wide Web became publicly accessible, and web browsers like Netscape Navigator allowed users to navigate and interact with online content. The Internet became a platform for information exchange and collaboration.
  • Application Service Providers (ASPs): The concept of Application Service Providers (ASPs) emerged during this period. ASPs provided access to business applications and services over the internet, allowing users to subscribe to software on a subscription basis rather than installing it locally. This can be considered a precursor to the Software as a Service (SaaS) model in cloud computing.
  • Grid Computing: Grid computing, a distributed computing model that involved pooling resources from multiple networks to solve complex problems, gained traction. While not directly related to cloud computing, the principles of resource sharing and collaboration in grid computing contributed to later cloud concepts.
  • Virtualization Technologies: Virtualization technologies started to emerge, allowing multiple virtual machines to run on a single physical server. This marked a step toward more efficient use of computing resources.

2000s –

Cloud Computing as a concept emerged in the 2000s. It marked as a transformative period for computing:

  • Adoption by Enterprises: Enterprises began to embrace cloud computing for its scalability, flexibility, and cost-effectiveness. The ability to scale resources up or down based on demand and the shift from capital expenditures to operational expenditures were key drivers of cloud adoption.
  • Development of Platform as a Service (PaaS) and Software as a Service (SaaS): The 2000s saw the expansion of cloud service models. Alongside Infrastructure as a Service (IaaS), the PaaS model, which provides a platform for developers to build and deploy applications without managing the underlying infrastructure, gained traction. Additionally, Software as a Service (SaaS) offerings, where applications are delivered over the Internet, have become more prevalent.
  • Cloud services: Beginning with Amazon AWS(Amazon Web Services), and following with Google Cloud Platform(GCP) and Microsoft Azure Cloud, these platforms helped in shaping and advancing the cloud computing industry.
  • Evolving Security and Compliance Standards: As cloud adoption increased, there was a growing emphasis on addressing security and compliance concerns. Cloud service providers implemented robust security measures, and industry standards and certifications were developed to establish best practices.
  • OpenStack and Cloud Standards: OpenStack, an open-source cloud computing platform, was launched in 2010. It aimed to provide standardization in cloud computing and enable organizations to build and manage public and private clouds.

2010s –

During this decade, cloud computing became a mainstream IT paradigm, transforming how businesses and individuals approached technology infrastructure, application development, and data management.

  • Diverse Cloud Service Offerings: Cloud service providers, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), expanded their service portfolios. This included a wide array of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offerings.
  • Serverless Computing and Architectures: Serverless computing gained prominence. It allowed developers to build and run applications without managing the underlying infrastructure. AWS Lambda, Azure Functions, and Google Cloud Functions were key serverless platforms that enabled event-driven, scalable architectures. Serverless architectures, where applications are built using functions that automatically scale based on demand, gained popularity. This approach simplified deployment and reduced infrastructure management overhead.
  • Containers: Containers and containerization platforms, like Docker and Kubernetes, became foundational to cloud-native development. Containerization facilitated consistency across different environments and improved resource utilization.
  • Hybrid and Multi-Cloud Strategies: Businesses adopted hybrid cloud and multi-cloud strategies, combining on-premises infrastructure with public and private cloud services. This approach provided flexibility, redundancy, and the ability to leverage the strengths of different cloud providers.
  • Artificial Intelligence (AI) and Machine Learning (ML): Cloud providers integrated AI and ML services into their offerings. This allowed businesses to leverage pre-built models and tools for tasks such as image recognition, natural language processing, and predictive analytics.
  • Security and Compliance Focus: Cloud providers enhanced security features, and industry standards and certifications became more established. Focus on compliance with regulations such as GDPR increased, addressing concerns around data protection and privacy.
  • DevOps and Continuous Integration/Continuous Deployment: DevOps practices, emphasizing collaboration between development and operations teams, became integral to cloud development. CI/CD pipelines were widely adopted, allowing for automated testing and deployment of applications.
  • Open Source Contributions: Open source continued to play a vital role in cloud computing. Projects like Kubernetes, Prometheus, and Istio, hosted by the Cloud Native Computing Foundation, became the standard tools for container orchestration, monitoring, and service mesh.
  • Evolution of Data Management: Big Data and analytics services evolved, allowing organizations to process and derive insights from large datasets. Cloud-based data lakes and warehouses became essential components of modern data architectures.

2020s and Future –

Due to the COVID-19 pandemic in 2020, every organization adopted cloud adoptions at an accelerated rate. Cloud services provide the necessary infrastructure and tools to support remote collaboration and business continuity. The outlook for this decade and the future are:

  • Edge Computing Expansion: Due to increasing demand for low-latency applications in areas such as IoT, augmented reality (AR), and autonomous systems, the integration of edge computing is expected to deepen, enabling processing closer to the data source.
  • Increased Integration of AI and ML: Continuation of Integration of artificial intelligence (AI) and machine learning (ML) services into various cloud offerings.
  • Enhanced Hybrid and Multi-Cloud Management: Solutions for managing hybrid and multi-cloud environments will become more sophisticated. Tools that provide visibility, governance, and seamless management across diverse cloud infrastructures are expected to evolve.
  • Sustainability and Green Computing: Efforts to use renewable energy sources, optimize data center efficiency, and reduce carbon footprints are expected to continue.
  • Advancements in Quantum Computing: Research and Experimentation with quantum computing are likely to continue, and cloud providers may offer more accessible quantum computing resources for developers and researchers.

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Created A Robust Cloud Infrastructure Monitoring System That Guaranteed Zero Downtime For A Well-Reputed CRM Tool https://euphoricthought.com/created-a-robust-cloud-infrastructure-monitoring-system-that-guaranteed-zero-downtime-for-a-well-reputed-crm-tool/ Fri, 24 Mar 2023 04:22:42 +0000 https://www.euphoricthought.com/?p=1389 The post Created A Robust Cloud Infrastructure Monitoring System That Guaranteed Zero Downtime For A Well-Reputed CRM Tool appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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CHALLENGES

The client was not able to understand the gaps that the engineers were facing during the configuration of the APM tool with any application to identify the application’s performance bottleneck.

SOLUTIONS

Euphoric’s team of DevOps engineers and Cloud experts ran through deployment and testing of the multiple applications of different tech stacks like Java agent, Python agent, Node agent etc, to understand the complete workflow from configuring the application with the tool to code level analysis of any applications that are highly distributed.

BENEFITS

The client gained a complete understanding of all the flaws that existed in configuring their APM tool with any custom application of different technologies.

Euphoric’s team suggested the client with a better user interface which makes it easier for anyone to understand the complete topology and issues related to any application of different technology with just a few clicks. The team also came up with multiple custom scripts to make the instrumentation easier.

This approach helped the client’s internal technical team to release a newer version of the APM tool with all the changes suggested.

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Store Terraform state in s3 bucket https://euphoricthought.com/store-terraform-state-in-s3-bucket/ Thu, 16 Mar 2023 01:55:25 +0000 https://www.euphoricthought.com/?p=1088 The post Store Terraform state in s3 bucket appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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Terraform keeps the remote state of the infrastructure.

It stores in a file called terraform.tfstate
There is also a backup of the previous state in terraform.tfstate.backup.When you execute terraform apply, a new terraform.tfstate and backup is written.
This is how terraform keeps track of the remote state. If the remote state changes and you hit terraform apply again, terraform will make changes to the correct remote state again.
Eg: you terminated an instance that is managed by terraform, after terraform it will be started again. You can keep the terraform.tfstate in version control.
Eg: git
It gives you a history of your terraform.tfstate file (which is just a big JSON file) It allows you to collaborate with other team members. Unfortunately you can get conflicts when 2 people work at the same time.
Local state works well in the beginning, but when your project becomes bigger, you might want to store your state remote.
The terraform state can be saved remotely, using the backend functionality in terraform. The default backend is the ‘local backend’(the terraform.state file is gonna be saved locally).
Other backends include:
S3(with a locking mechanism using DynamoDB)
Consul (with locking mechanism)
Terraform Enterprise(the commercial solution)

Using the backend functionality definitely has benefits:

Working in team: it allows for collaboration, the remote state will always
be available for the whole team. The state file is not stored locally anymore. Possible sensitive information is now only stored in the remote state.

There are 2 steps to configure remote state:

image (1)

It avoids having to commit and push the terraform.tfstate to version control.

How to achieve?

  • Create a S3 bucket with versioning enabled.
  • Configure aws locally.
  • Sudo apt-get update
  • Sudo apt-get install awscli
  • aws configure
  • AWS Access Key ID [None]:
  • AWS Secret Access Key [None]:
  • Default region name [None]: ca-central-1
  • Default output format [None]:
  • These credentials are stored in the file ~/.aws/credentials

Create a file called “backend.tf”

image-2

Let’s Talk

Your thoughts matter to us – get in touch today!

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Monolithic Architecture vs Microservices Architecture https://euphoricthought.com/monolithic-architecture-vs-microservices-architecture/ Thu, 16 Mar 2023 01:48:08 +0000 https://www.euphoricthought.com/?p=1083 The post Monolithic Architecture vs Microservices Architecture appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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Monolithic vs Microservices Architecture- Which ones the best..?

When developing an application we need to start with a modular architecture which consists of the following.

  • Presentation Layer which is responsible for handling HTTP requests and responding with HTML or JSON/XML
  • Business Layer which will be the applications business logic
  • Database layer which is responsible for accessing the database
  • Integration with the application which can be REST API or other services.

There are various pros and cons for Monolithic and micro services architecture which are listed below

Screen-Shot-2020-03-05-at-5.58.17-PM

Benefits of Monolithic Architecture

  • Simpler development and deployment
  • Fewer cross-cutting concerns
  • Better performance

Cons of Monolithic Architecture

  • Codebase gets cumbersome over time
  • Difficult to adopt new technologies
  • Limited agility

Benefits of Microservices Architecture

  • Easy to develop, test, and deploy
  • Increased agility
  • Ability to scale horizontally

Cons of Microservices Architecture

  • Complexity
  • Security concerns
  • Different programming languages

The Conclusion

The Monolithic model works great in some cases. Monolithic software architecture is advisable and is beneficial if your team is at the startup stage, you’re building a product that is yet to be proven, and you have no experience with microservices, a simple and lighter application. Monolithic is perfect for startups that need to get a product up and running as soon as possible. However, certain issues mentioned above come with the monolithic package.

Microservices are good, but not for all types of apps. This pattern works great for complex and highly distributed applications that are developed by an experienced team. Consider choosing a Microservices architecture when you have a strong team and when the app is complex enough to break into services. When an application is large and needs to be flexible and scalable, microservices are beneficial.

Let’s Talk

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Monitoring Kubernetes https://euphoricthought.com/monitoring-kubernetes/ Thu, 16 Mar 2023 01:27:20 +0000 https://www.euphoricthought.com/?p=1073 The post Monitoring Kubernetes appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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Importance of Monitoring Kubernetes

Is it easy to monitor Kubernetes?

Kubernetes is designed to manage service-oriented applications using containers distributed across clusters of hosts. Kubernetes provides mechanisms for application deployment, scheduling, updating, service discovery and scaling. Kubernetes has taken the container ecosystem by storm and it is the brain for your distributed container deployment.

While Kubernetes has the potential to dramatically simplify the act of deploying your application in containers – and across clouds – it also adds a new set of complexities for your day-to-day tasks, managing application performance, gaining visibility into services, and your typical monitoring -> alerting -> troubleshooting workflow.

Legacy monitoring tools, collecting metrics from static targets, built for monitoring servers that you could name and services that didn’t change overnight, worked well past, but won’t work well today. This is why these tools fail at monitoring Kubernetes:

  • Increase Infrastructure Complexity
    New layers of infrastructure complexity are appearing in the hopes of simplifying application deployments: dynamic provisioning via IaaS, automated configuration with configuration management tools, and lately, orchestration platforms like Kubernetes, which sit between your bare metal or virtual infrastructure and the services that empower your applications. This is why monitoring the Kubernetes health at the control plane is part of the job.

  • Scaling requirements and Cloud native explosionWhile we adopt cloud native architectures, the changes that they bring carry away an increased amount of smaller components. The number of metrics simply explodes and traditional monitoring systems just cannot keep up. While we used to know how many instances we had of each service component and where they were located, that’s no longer the case. Now, metrics have high cardinality. Kubernetes adds some multidimensional levels, like cluster, node, namespace or service, so the different aggregations or perspectives that need to be controlled can explode; many labels that represent attributes from the logical groups of the microservices, to application version, API endpoint, specific resources or actions, etc.

  • Microservices Architecture
    In addition to increased infrastructure complexity, new applications are being designed for microservices, where the number of components communicating with each other has increased in an order of magnitude. Each service can be distributed across multiple instances, and containers move across your infrastructure as needed. This is why monitoring the Kubernetes orchestration state is key to understanding if Kubernetes is doing its job. Trust but verify that all the instances of your service are up and running.In addition to increased infrastructure complexity, new applications are being designed for microservices, where the number of components communicating with each other has increased in an order of magnitude. Each service can be distributed across multiple instances, and containers move across your infrastructure as needed. This is why monitoring the Kubernetes orchestration state is key to understanding if Kubernetes is doing its job. Trust but verify that all the instances of your service are up and running.

Why is it important to monitor Kubernetes?

Kubernetes sits between your bare metal or virtual infrastructure and the services that run your apps. That’s why you need to monitor the health of the Kubernetes control plane. Kubernetes introduces all these new layers of infrastructure complexity. Service can be distributed across multiple instances. Containers are ephemeral and move across your infrastructure as needed.

Hence why monitoring the state of all resources is key to understanding if Kubernetes is doing its job.

It’s hard to see what happens inside of containers. Once a container dies the data inside it can never be recovered. You can’t see the logs after the fact making troubleshooting incredibly complicated. Monitoring tools need to be able to gather all the metrics and logs and store them in a safe, centralized location so you can access them at any point of time and troubleshoot issues.

What can we monitor ?

  • Docker based pod specific metric – CPU, Memory, Throttling, restart count etc.
  • Pod Related Metrics – Pod Status, container per pod, Container state, ready state etc
  • Project level metrics – Total Namespaces, Total Nodes, Total Deployments, Total Pods etc
  • Deployment metrics – Available Replicas, Deployment Health etc
  • Node level metrics – Cpu Allocatable, Cpu Capacity, Disk Pressure, Memory Allocatable Etc

Kubernetes Dashboard

Kub-Dash-1536x755

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Importance of Containerization https://euphoricthought.com/importance-of-containerization/ Thu, 16 Mar 2023 01:20:13 +0000 https://www.euphoricthought.com/?p=1068 The post Importance of Containerization appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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What is Containerization?

How is it to have a ready to eat food which has all the ingredients packaged and all we need to do is mix them and cook which does not need any cooking knowledge or much of effort. We need to consider the same thing when it comes to software. Software containerization draws on a similar idea. By containerizing, developers bundle a program’s code, runtime engine, tools, libraries and settings into one portable package. That way, the software requires fewer resources to run and is much easier to deploy in new environments.

We need to make it easy for the software engineers to run any applications in any kind of environment. It can happen by packaging the code and keeping it handy, so a lot of the complexity of installing and configuring an application is taken away. Containers let a developer abstract all of that and make a very simple package that’s easy to consume.

Containers made it a lot easier to build software with service-oriented architecture, like microservices. Each piece of business logic — or service — could be packaged and maintained separately, along with its interfaces and databases. The different microservices communicate with one another through a shared interface like an API or a REST interface.

With microservices, developers can adjust one component without worrying about breaking the others, which makes for easier fixes and faster responses to market conditions. Microservices also improve security, as compromised code in one component is less likely to open back doors to the others. Containers and microservices are two of the most important cloud-native tech terms.

Benefits of Containers

Application Development- Development and deployment of application can be made very easy and is very true with containerized microservices. Breaking all the large components into smaller parts helps in managing without complexity. Those discrete components (or microservices) can then be deployed, updated or retired independently, without having to update and redeploy the entire application.

Portability- Building an application to run in any environment hassle free is possible in containerization. We can move from different cloud platforms without having rewrite large amount of code to execute properly. This also boosts developers productivity, since they can write code in a consistent manner without worrying about its execution when deployed to different environments.

Resource utilization- Very fewer resources are utilized as they are lightweight. We can run many containers in a single machine.

Consistency-  As the container image defines the base dependencies, there is a high level of guarantee that what runs on a developer machine also runs in a variety of production environment and runs on any host operating system in the same way. This leads to development teams spending less time debugging and fixing personal infrastructure issues and more time working on improving applications.

Scalability- Thanks to their low overheads and boot speed, containers introduce the ability to adapt applications in ways not possible before. By writing basic scripts or complex orchestrators we can quickly recover crashed application components, add new instances to meet increased demand, or perform rolling upgrades to update application or dependency versions without any downtime.

What is container orchestration ?

Container orchestration is all about managing the lifecycles of containers, especially in large, dynamic environments. Software teams use container orchestration to control and automate many tasks like

  • Allocation of resources between containers
  • Scaling up or removing containers
  • Load balancing
  • Monitoring
  • Cluster management

Container orchestration encompasses a variety of methodologies depending on the container orchestration tool used. Generally, container orchestration tools communicate with a user-created YAML or JSON file that describes the configuration of the application.

The configuration file directs the container orchestration tool on how to retrieve container images, how to create a network between containers, and where to store log data or mount storage volumes.

ORCH

What is Kubernetes container orchestration?

Kubernetes is widely appreciated for its motility and  is a popular choice among large enterprises that emphasize a DevOps approach and is supported by various cloud service providers like AWS, Azure etc

Workloads can be moved without redefining the application or infrastructure since its starting point is the cluster, which increases its portability.

Kubernetes creates an abstract hardware layer that allows DevOps teams to deliver a Platform-as-a-Service (PaaS), and makes it easier to request additional resources to scale an application without the need for more physical machines.

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Cloud Migration Best Practices https://euphoricthought.com/cloud-migration-best-practices/ Thu, 16 Mar 2023 01:12:52 +0000 https://www.euphoricthought.com/?p=1054 The post Cloud Migration Best Practices appeared first on Euphoric Thought: IT Solutions and Services Provider in India.

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Cloud Migration best practices

A cloud migration strategy is defined as a plan that an organization formulates to move all the resources in its infrastructures, such as data, services, and applications, to the cloud. It is important to validate the most efficient way to prioritize and migrate applications before going live. A systematic, documented strategy is crucial.

The Cloud Migration Process

1. Planning your migration

The first step to consider before migrating data to the cloud is to determine the use case that the public cloud will serve. It is also important to assess the environment and determine the factors that will govern the migration. A successful enterprise cloud migration strategy will include prioritizing workloads for migration, determining the correct migration plan for each individual workload, developing a pilot, testing, and adjusting the strategy based on the results of the pilot. A cloud migration strategy document should be created to guide teams through the process and facilitate roll-back if necessary.

2. Choose your cloud environment

Now that you have the visibility you need to achieve success, you are ready to decide what kind of cloud model you want to adopt. Whether you choose public cloud, hybrid cloud, private cloud, or multi-cloud (or services like Google, Azure, or AWS) depends on which best serves your current and future needs.

It is also important to have an application performance management solution in place before the entire migration process starts.

3. Migrating your apps and data

Planned accurately, your actual migration should be plain sailing. Still, keep in mind cloud security concerns, such as complying with security policies and planning for data backup and recovery. If your data becomes inaccessible to users during migration, you risk impacting your business operations. The same is true as you continue to sync and update your systems after the initial migration takes place. We need to find a way to Synchronize changes that are made to the source data while the migration is ongoing.

4. Validating Success

We need to validate the success in a low risk test environment. We cannot declare a cloud migration successful without evidence that it works as expected. With a proper APM solution we can check the comparison of pre and post-move performace.

Types of Cloud Migrations

  1. Rehosting( Lift and Shift) The most general path is rehosting (or lift-and-shift), which implements as it sounds. It holds our application and then drops it into our new hosting platform without changing the architecture and code of the app. Also, it is a general way for enterprises unfamiliar with cloud computing, who profit from the deployment speed without having to waste money or time on planning for enlargement.
  2. Replatforming: As a variation on the lift and shift, re-platforming involves making a few further adjustments to optimize your landscape for the cloud. Again, the core architecture of applications stays the same. This, too, is a good strategy for conservative organizations that want to build trust in the cloud while achieving benefits like increased system performance.
  3. Re-Factoring: It means to rebuild our applications from leverage to scratch cloud-native abilities. A potential disadvantage is vendor lock-in as we are re-creating the cloud infrastructure. It is the most expensive and time-consuming route as we may expect. But, it is also future-proof for enterprises that wish to take benefit from more standard cloud features. It covers the most common three approaches for migrating our existing infrastructure.
  4. Re-Purchasing: It means replacing our existing applications with a new SaaS-based and cloud-native platform (such as a homegrown CRM using Salesforce). The complexity is losing the existing training and code’s familiarity with our team over a new platform. However, the profit is ignoring the cost of the development.
  5. Retiring: Once you have assessed your application portfolio for cloud readiness, you might find some applications are no longer useful. In this case, simply turn them off. The resulting savings might even boost your business case for applications that are ready for migration.
  6. Retaining: For some organizations, cloud adoption does not yet make sense. Are you unable to take data off-premises for compliance reasons? Perhaps you are not ready to prioritize an app that was recently upgraded. In this case, plan to revisit cloud computing at a later date. You should only migrate what makes sense for your business.

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