Chaos Engineering

Chaos Engineering Kubernetes

by Publications

 

 

Chaos Engineering Kubernetes

In the world of cloud-native computing, Kubernetes has emerged as the de facto container orchestration platform. With its ability to manage and scale containerized applications, Kubernetes has revolutionized modern software development and deployment. However, as systems become more complex, ensuring their resilience and reliability has become a critical challenge. This is where Chaos Engineering comes into play. In this blog post, we will explore the concept of Chaos Engineering in the context of Kubernetes and its importance in building robust, fault-tolerant applications.

Chaos Engineering is a discipline that deliberately injects failure into a system to uncover weaknesses and vulnerabilities. By simulating real-world scenarios, organizations can proactively identify and address potential issues before they impact end-users. Chaos Engineering embraces the philosophy of “fail fast to learn faster,” helping teams build more resilient systems that can withstand unforeseen circumstances and disruptions with minimal impact.

Regarding Chaos Engineering in Kubernetes, the focus is on injecting controlled failures into the ecosystem to assess the system’s behavior under stress. By leveraging Chaos Engineering tools and techniques, organizations can gain valuable insights into the resiliency of their Kubernetes deployments and identify areas for improvement.

 

Highlights: Chaos Engineering Kubernetes

  • The Traditional Application

When considering Chaos Engineering kubernetes, we must start from the beginning. Not too long ago, applications ran in single private data centers, potentially two data centers for high availability. These data centers were on-premises, and all components were housed internally. Life was easy, and troubleshooting and monitoring any issues could be done by a single team, if not a single person, with predefined dashboards. Failures were known, and there was a capacity planning strategy that did not change too much, and you could carry out standard dropped packet test.

  • A Static Infrastructure

The network and infrastructure had fixed perimeters and were pretty static. There weren’t many changes to the stack, for example, daily. Agility was at an all-time low, but that did not matter for the environments in which the application and infrastructure were housed. However, nowadays, we are in a completely different environment.

Complexity is at an all-time high, and agility in business is critical. Now, we have distributed applications with components/services located in many different places and types of places, on-premises and in the cloud, with dependencies on both local and remote services. So, in this land of complexity, we must find system reliability. A reliable system is one that you can trust will be reliable.

 

Before you proceed to the details of Chaos Engineering, you may find the following useful:

  1. Service Level Objectives (slos)
  2. Kubernetes Networking 101
  3. Kubernetes Security Best Practice
  4. Network Traffic Engineering
  5. Reliability In Distributed System
  6. Distributed Systems Observability

 



Kubernetes Chaos Engineering

Key Chaos Engineering Kubernetes Discussion points:


  • Unpredictable failure modes.

  • The need for baseline engineering.

  • Non-ephemerel and ephemeral service types.

  • So many metrics to count.

  • Debugging microservices.

  • The rise of Chaos Engineering.

  • Final points on Service Mesh.

 

  • A key point: Video on Chaos Engineering Kubernetes

In this video tutorial, we are going through the basics of how to start a Chaos Engineering project, along with a discussion on baseline engineering. I will introduce to you how this can be solved by knowing exactly how your application and infrastructure perform under stress and what are their breaking points.

 

Chaos Engineering: How to Start A Project
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Back to basics with Chaos Engineering Kubernetes

Today’s standard explanation for Chaos Engineering is “The facilitation of experiments to uncover systemic weaknesses.” The following is true for Chaos Engineering.

  1. Begin by defining “steady state” as some measurable output of a system that indicates normal behavior.
  2. Hypothesize that this steady state will persist in both the control and experimental groups.
  3. Submit variables that mirror real-world events like servers that crash, hard drives that malfunction, severed network connections, etc.
  4. Then, as a final step. Try to disprove the hypothesis by looking for a steady state difference between the control and experimental groups.

 

Chaos Engineering Scenarios in Kubernetes:

1. Pod Failures: Simulating failures of individual pods within a Kubernetes cluster allows organizations to evaluate how the system responds to such events. By randomly terminating pods, Chaos Engineering can help ensure that the system can handle pod failures gracefully, redistributing workload and maintaining high availability.

2. Network Partitioning: Introducing network partitioning scenarios can help assess the resilience of a Kubernetes cluster. By isolating specific nodes or network segments, Chaos Engineering enables organizations to test how the group reacts to network disruptions and evaluate the effectiveness of load balancing and failover mechanisms.

3. Resource Starvation: Chaos Engineering can simulate resource scarcity scenarios by intentionally consuming excessive resources, such as CPU or memory, within a Kubernetes cluster. This allows organizations to identify potential performance bottlenecks and optimize resource allocation strategies.

Benefits of Chaos Engineering in Kubernetes:

1. Enhanced Reliability: By subjecting Kubernetes deployments to controlled failures, Chaos Engineering helps organizations identify weak points and vulnerabilities, enabling them to build more resilient systems that can withstand unforeseen events.

2. Improved Incident Response: Chaos Engineering allows organizations to test and refine their incident response processes by simulating real-world failures. This helps teams understand how to quickly detect and mitigate potential issues, reducing downtime and improving the overall incident response capabilities.

3. Cost Optimization: By identifying and addressing performance bottlenecks and inefficient resource allocation, Chaos Engineering can help optimize the utilization of resources within a Kubernetes cluster. This, in turn, leads to cost savings and improved efficiency.

 

Beyond the Complexity Horizon

Therefore, monitoring and troubleshooting are much more demanding, as everything is interconnected, making it difficult for a single person in one team to understand what is happening entirely. The edge of the network and application boundary surpasses one location and team. Enterprise systems have gone beyond the complexity horizon, and you can’t understand every bit of every single system.

Even if you are a developer closely related to the system and truly understand the nuts and bolts of the application and its code, no one can understand every bit of every single system.  So, finding the correct information is essential, but once you find it, you have to give it to those who can fix it. So monitoring is not just about finding out what is wrong; it needs to alert, and these alerts need to be actionable.

 

Troubleshooting: Chaos engineering kubernetes

Chaos Engineering aims to improve a system’s reliability by ensuring it can withstand turbulent conditions. Chaos Engineering makes Kubernetes more secure. So, if you are adopting Kubernetes, you should adopt Chaos Engineering as an integral part of your monitoring and troubleshooting strategy.

Firstly, we can pinpoint the application errors and understand, at best, how these errors arose. This could be anything from badly ordered scripts on a web page to, let’s say, a database query that has bad sequel calls or even unoptimized code-level issues.

Or there could be something more fundamental going on. It is common to have issues with how something is packaged into a container. You can pull in the incorrect libraries or even use a debug version of the container. Or there could be nothing wrong with the packaging and containerization of the container; it is all about where the container is being deployed. There could be something wrong with the infrastructure, either a physical or logical problem—incorrect configuration or a hardware fault somewhere in the application path.

 

Non-ephemeral and ephemeral services

With the introduction of containers and microservices observability, monitoring solutions need to manage non-ephemeral and ephemeral services. We are collecting data for applications that consist of many different benefits.

So when it comes to container monitoring and performing chaos engineering kubernetes tests, we need to understand the nature and the application that lays upon fully. Everything is dynamic by nature. You need to have monitoring and troubleshooting in place that can handle the dynamic and transient nature. When monitoring a containerized infrastructure, you should consider the following.

Container Lifespan: Containers have a short lifespan; containers are provisioned and commissioned based on demand. This is compared to the VM or bare-metal workloads that generally have a longer lifespan. As a generic guideline, containers have an average lifespan of 2.5 days, while traditional and cloud-based VMs have an average lifespan of 23 days. Containers can move, and they do move frequently.

One day, we could have workload A on cluster host A, and the next day or even on the same day, the same cluster host could be hosting Application workload B. Therefore, different types of impacts could depend on the time of day.

Containers are Temporary: Containers are dynamically provisioned for specific use cases temporarily. For example, we could have a new container based on a specific image. New network connections will be set up for that container, storage, and any integrations to other services that make the application work. All of this is done dynamically and can be done temporarily.

Different monitoring levels: We have many monitoring levels in a Kubernetes environment. The components that make up the Kubernetes deployment will affect application performance. We have, for example, nodes, pods, and application containers. We have monitoring at different levels, such as the VM, storage, and microservice level.

Microservices change fast and often: Microservices consist of constantly evolving apps. New microservices are added, and existing ones are decommissioned quickly. So, what does this mean to usage patterns? This will result in different usage patterns on the infrastructure. If everything is often changing, it can be hard to derive the baseline and build a topology map unless you have something automatic in place. 

Metric overload: We now have loads of metrics. We now have additional metrics for the different containers and infrastructure levels. We must consider metrics for the nodes, cluster components, cluster add-on, application runtime, and custom application metrics. This is compared to a traditional application stack where we use metrics for components such as the operating system and the application. 

 

  • A key point: Video on Observability vs. Monitoring

We will start by discussing how our approach to monitoring needs to adapt to the current megatrends, such as the rise of microservices. Failures are unknown and unpredictable. Therefore, a pre-defined monitoring dashboard will have difficulty keeping up with the rate of change and unknown failure modes.

For this, we should look to have the practice of observability for software and monitoring for infrastructure.

 

Observability vs Monitoring
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Metric explosion

In the traditional world, we didn’t have to be concerned with the additional components such as an orchestrator or the dynamic nature of many containers. With a container cluster, we must consider metrics from the operating system, application, orchestrator, and containers.  We refer to this as a metric explosion. So now we have loads of metrics that need to be gathered. There are also different ways to pull or scrape these metrics.

Prometheus is expected in the world of Kubernetes and uses a very scalable pull approach to getting those metrics from HTTP endpoints either through Prometheus client libraries or exports.

Prometheus Monitoring Application
Diagram: Prometheus Monitoring Application: Scaping Metrics.

 

A key point: What happens to visibility  

So we need complete visibility now more than ever. And not just for single components but visibility at a holistic level. Therefore, we need to monitor a lot more data points than we had to in the past. We need to monitor the application servers, Pods and containers, clusters running the containers, the network for service/pod/cluster communication, and the host OS.

All of the data from the monitoring needs to be in a central place so trends can be seen and different queries to the data can be acted on. Correlating local logs would be challenging in a sizeable multi-tier application with docker containers. We can use Log forwarders or Log shippers such as FluentD or Logstash to transform and ship logs to a backend such as Elasticsearch.

 

A key point: New avenues for monitoring

Containers are the norm for managing workloads and adapting quickly to new markets. Therefore, new avenues have opened up for monitoring these environments. So we have, for example, AppDynamics and Elastic search, which are part of the ELK stack, the various logs shippers that can be used to help you provide a welcome layer of unification. We also have Prometheus to get metrics. Keep in mind that Prometheus works in the land of metrics only. There will be different ways to visualize all this data, such as Grafana and Kibana. 

 

What happened to visibility

What happens to visibility? So we need complete visibility now more than ever. And not just for single components but visibility at a holistic level. Therefore, we need to monitor a lot more data points than we had to in the past. We need to monitor the application servers, Pods and containers, clusters running the containers, the network for service/pod/cluster communication, and the host OS. 

All of the data from the monitoring needs to be in a central place so trends can be seen and different queries to the data can be acted on. Correlating local logs would be challenging in a sizeable multi-tier application with docker containers. We can use Logforwarders or Log shippers such as FluentD or Logstash to transform and ship logs to a backend such as Elasticsearch.

Containers are the norm for managing workloads and adapting quickly to new markets. Therefore, new avenues have opened up for monitoring these environments. So I have mentioned AppDynamics, Elastic search, which is part of the ELK stack, and the various log shippers that can be used to help you provide a layer of unification. We also have Prometheus. There will be different ways to visualize all this data, such as Grafana and Kibana. 

 

Microservices complexity: Management is complex

So, with the wave towards microservices, we get the benefits of scalability and business continuity, but managing is very complex. The monolith is much easier to manage and monitor. Also, as they are separate components, they don’t need to be written in the same language or toolkits. So you can mix and match different technologies.

So, this approach has a lot of flexibility, but we can have increased latency and complexity. There are a lot more moving parts that will increase complexity.

We have, for example, reverse proxies, load balancers, firewalls, and other infrastructure support services. What used to be method calls or interprocess calls within the monolith host now go over the network and are susceptible to deviations in latency. 

 

Debugging microservices

With the monolith, the application is simply running in a single process, and it is relatively easy to debug. Many traditional tooling and code instrumentation technologies have been built, assuming you have the idea of a single process. However, with microservices, we have a completely different approach with a distributed application.

Now, your application has multiple processes running in other places. The core challenge is that trying to debug microservices applications is challenging.

So much of the tooling we have today has been built for traditional monolithic applications. So, there are new monitoring tools for these new applications, but there is a steep learning curve and a high barrier to entry. New tools and technologies such as distributed tracing and chaos engineering kubernetes are not the simplest to pick up on day one.

 

  • Automation and monitoring: Checking and health checks

Automation comes into play with the new environment. With automation, we can do periodic checks not just on the functionality of the underlying components, but we can implement the health checks of how the application performs. All can be automated for specific intervals or in reaction to certain events.

With the rise of complex systems and microservices, it is more important to have real-time monitoring of performance and metrics that tell you how the systems behave. For example, what is the usual RTT, and how long can transactions occur under normal conditions?

 

  • A key point: Video on Distributed Tracing

We generally have two types of telemetry data. We have log data and time-series statistics. The time-series data is also known as metrics in a microservices environment. The metrics, for example, will allow you to get an aggregate understanding of what’s happening to all instances of a given service.

Then, we have logs, on the other hand, that provide highly fine-grained detail on a given service. But have no built-in way to provide that detail in the context of a request. Due to how distributed systems fail, you can’t use metrics and logs to discover and address all of your problems. We need a third piece to the puzzle: distributed tracing.

 

Distributed Tracing Explained
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The Rise of Chaos Engineering

There is a growing complexity of infrastructure, and let’s face it, a lot can go wrong. It’s imperative to have a global view of all the infrastructure components and a good understanding of the application’s performance and health. In a large-scale container-based application design, there are many moving pieces and parts, and trying to validate the health of each piece manually is hard to do. 

With these new environments, especially cloud-native at scale. Complexity is at its highest, and many more things can go wrong. For this reason, you must prepare as much as possible so the impact on users is minimal.

So, the dynamic deployment patterns you get with frameworks with Kubernetes allow you to build better applications. But you need to be able to examine the environment and see if it is working as expected. Most importantly, this course’s focus is that to prepare effectively, you need to implement a solid strategy for monitoring in production environments.

Chaos Engineering
Diagram: Chaos engineering testing.

 

    • Chaos engineering kubernetes

For this, you need to understand practices like Chaos Engineering and Chaos Engineering tools and how they can improve the reliability of the overall system. Chaos Engineering is the ability to perform tests in a controlled way. Essentially, we intentionally break things to learn how to build more resilient systems.

So, we are injecting faults in a controlled way to make the overall application more resilient by injecting various issues and faults. It comes down to a trade-off and your willingness to accept it. There is a considerable trade-off with distributed computing. You have to monitor efficiently, have performance management, and, more importantly, accurately test the distributed system in a controlled manner. 

 

    • Service mesh chaos engineering

 Service Mesh is an option to use to implement Chaos Engineering. You can also implement Chaos Engineering with Chaos Mesh, a cloud-native Chaos Engineering platform that orchestrates tests in the Kubernetes environment. The Chaos Mesh project offers a rich selection of experiment types. Here are the choices, such as the POD lifecycle test, network test, Linux kernel, I/O test, and many other stress tests.

Implementing practices like Chaos Engineering will help you understand and manage unexpected failures and performance degradation. The purpose of Chaos Engineering is to build more robust and resilient systems. 

Conclusion:

Chaos Engineering has emerged as a valuable practice for organizations leveraging Kubernetes to build and deploy cloud-native applications. By subjecting Kubernetes deployments to controlled failures, organizations can proactively identify and address potential weaknesses, ensuring the resilience and reliability of their systems. As the complexity of cloud-native architectures continues to grow, Chaos Engineering will play an increasingly vital role in building robust and fault-tolerant applications in the Kubernetes ecosystem.

 

Reliability in Distributed Systems

Reliability In Distributed System

by Blog

Reliability In Distributed System

Distributed systems have become an integral part of our modern technological landscape. Whether it's cloud computing, internet banking, or online shopping, these systems play a crucial role in providing seamless services to users worldwide. However, as distributed systems grow in complexity, ensuring their reliability becomes increasingly challenging.

In this blog post, we will explore the concept of reliability in distributed systems and discuss various techniques to achieve fault-tolerant operations.

Reliability in distributed systems refers to the ability of the system to consistently function as intended, even in the presence of hardware failures, network partitions, and other unforeseen events. To achieve reliability, system designers employ various techniques, such as redundancy, replication, and fault tolerance, to minimize the impact of failures and ensure continuous service availability.

Matt Conran

Table of Contents

Highlights: Reliability in Distributed System

Shift in Landscape

When considering reliability in a distributed system, considerable shifts in our environmental landscape have caused us to examine how we operate and run our systems and networks. We have had a mega change with the introduction of various cloud platforms and their services and containers, along with the complexity of managing distributed systems observability and microservices observability that unveil significant gaps in current practices in our technologies. Not to mention the flaws with the operational practices around these technologies.

Existing Static Tools

This has caused a knee-jerk reaction to a welcomed drive-in innovation to system reliability. Yet, some technologies and tools used to manage these innovations do not align with the innovative events. Many of these tools have stayed relatively static in our dynamic environment. So, we have static tools used in a dynamic environment, which causes friction to reliability in distributed systems and the rise for more efficient network visibility.

Understanding the Complexity

Distributed systems are inherently complex, with multiple components across different machines or networks. This complexity introduces challenges like network latency, hardware failures, and communication bottlenecks. Understanding the intricate nature of distributed systems is crucial to devising reliable solutions.

Redundancy and Replication

One critical approach to enhancing reliability in distributed systems is redundancy and replication. The system becomes more fault-tolerant by duplicating critical components or data across multiple nodes. This ensures the system can function seamlessly even if one component fails, minimizing the risk of complete failure.

Consistency and Consensus Algorithms

Maintaining consistency in distributed systems is a significant challenge due to the possibility of concurrent updates and network delays. Consensus algorithms, such as the Paxos or Raft algorithms, are vital in achieving consistency by ensuring agreement among distributed nodes. These algorithms enable reliable decision-making and guarantee that all nodes reach a consensus state.

Reliability in distributed systems

Monitoring and Failure Detection

To ensure reliability, it is essential to have robust monitoring mechanisms in place. Monitoring tools can track system performance, resource utilization, and network health. Additionally, implementing efficient failure detection mechanisms allows for prompt identification of faulty components, enabling proactive measures to mitigate their impact on the overall system.

Load Balancing and Scalability

Load balancing is crucial in distributing the workload evenly across nodes in a distributed system. It ensures that no single node is overwhelmed, reducing the risk of system instability. Furthermore, designing systems with scalability in mind allows for seamless expansion as the workload grows, ensuring that reliability is maintained even during periods of high demand.

Related: Before you proceed, you may find the following post helpful:

  1. Distributed Firewalls
  2. SD WAN Static Network Based

 



Reliability In Distributed Systems


Key Reliability in Distributed System Discussion Points:


  • Complexity managing distributed systems.

  • Static tools in a dynamic environment.

  • Observability vs Monitoring.

  • Creative failures and black holes.

  • SRE teams and service level objectives.

  • New tools: Disributed tracing.

 

Back to Basics: Reliability in Distributed Systems

Understanding Distributed Systems

Distributed systems refer to a network of interconnected computers that communicate and coordinate their actions to achieve a common goal. Unlike traditional centralized systems, where a single entity controls all components, distributed systems distribute tasks and data across multiple nodes. This decentralized approach enables enhanced scalability, fault tolerance, and resource utilization.

Key Components of Distributed Systems

To comprehend the inner workings of distributed systems, we must familiarize ourselves with their key components. These components include nodes, communication channels, protocols, and distributed file systems. Nodes represent individual machines or devices within the network; communication channels facilitate data transmission, protocols ensure reliable communication, and distributed file systems enable data storage across multiple nodes.

Distribued vs centralized

 

Distributed Systems Use Cases

Distributed systems are used in many modern applications. Mobile and web applications with high traffic are distributed systems. Web browsers or mobile applications serve as clients in a client-server environment. As a result, the server becomes its own distributed system. The modern web server follows a multi-tier system pattern. Requests are delegated to several server logic nodes via a load balancer.

Kubernetes is popular among distributed systems since it enables containers to be combined into a distributed system. Kubernetes orchestrates network communication between the distributed system nodes and handles dynamic horizontal and vertical scaling of the nodes. 

Cryptocurrencies like Bitcoin and Ethereum are also distributed systems that are peer-to-peer. The currency ledger is replicated at every node in a cryptocurrency network. To bootstrap, a currency node connects to other nodes and downloads its full ledger copy. Additionally, cryptocurrency wallets use JSON RPC to communicate with the ledger nodes.

Challenges in Distributed Systems

While distributed systems offer numerous advantages, they also pose various challenges. One significant challenge is achieving consensus among distributed nodes. Ensuring that all nodes agree on a particular value or decision can be complex, especially in the presence of failures or network partitions. Additionally, maintaining data consistency across distributed nodes and mitigating issues related to concurrency control requires careful design and implementation.

Example: Distributed System of Microservices

Microservices are one type of distributed system since they decompose an application into individual components. A microservice architecture, for example, may have services corresponding to business features (payments, users, products, etc.), with each component handling the corresponding business logic. Multiple redundant copies of the services will then be available, so there is no single point of failure.

microservices

Example: Distributed Tracing

Using distributed tracing, you can profile or monitor the results of requests across a distributed system. Distributed systems can be challenging to monitor since each node generates its logs and metrics. To get a complete view of a distributed system, it is necessary to aggregate these separate node metrics holistically. 

A distributed system generally doesn’t access its entire set of nodes but rather a path through those nodes. With distributed tracing, teams can analyze and monitor commonly accessed paths through a distributed system. The distributed tracing is installed on each system node, allowing teams to query the system for information on node health and performance.

Benefits and Applications

Despite the challenges, distributed systems offer a wide array of benefits. One notable advantage is enhanced fault tolerance. Distributing tasks and data across multiple nodes improves system reliability, as a single point of failure does not bring down the entire system. Additionally, distributed systems enable improved scalability, accommodating growing demands by adding more nodes to the network. The applications of distributed systems are vast, ranging from cloud computing and large-scale data processing to peer-to-peer networks and distributed databases.

 

Distributed Systems: The Challenge

Distributed systems are required to implement the reliability, agility, and scale expected of modern computer programs. Distributed systems are applications of many different components running on many other machines. Containers are the foundational building block, and groups of containers co-located on a single device comprise the atomic elements of distributed system patterns.

Distributed System Observability

The significant shift we see with software platforms is that they evolve much quicker than the products and paradigms we use to monitor them. We need to consider new practices and technologies with dedicated platform teams to enable a new era of system reliability in a distributed system. Along with the practices of Observability that are a step up to the traditional monitoring of static infrastructure: Observability vs monitoring.

 

Knowledge Check: Distributed Systems Architecture

Client-Server Architecture

A client-server architecture has two primary responsibilities. The client presents user interfaces and is then connected to the server via a network. The server handles business logic and state management. Unless the server is redundant, a client-server architecture can quickly degrade into a centralized architecture. A truly distributed client-server setup will consist of multiple server nodes that distribute client connections. In modern client-server architectures, clients connect to encapsulated distributed systems on the server.

Multi-tier Architecture

Multi-tier architectures are extensions of client-server architectures. Multi-tier architectures decompose servers into further granular nodes, which decouple additional backend server responsibilities like data processing and data management. By processing long-running jobs asynchronously, these additional nodes free up the remaining backend nodes to focus on responding to client requests and interacting with the data store.

Peer-to-Peer Architecture

Peer-to-peer distributed systems contain complete instances of applications on each node. There is no separation between presentation and data processing at the node level. A node consists of a presentation layer and a data handling layer. Peer nodes may contain the entire state data of the system. 

Peer-to-peer systems have a great deal of redundancy. Peer-to-peer nodes discover and connect to other peers when they are initiated and brought online, thereby synchronizing their local state with the system’s. As a result of this feature, nodes on a peer-to-peer network won’t be disrupted by the failure of one. Additionally, peer-to-peer systems will persist. 

Service-orientated Architecture

A service-oriented architecture (SOA) is a precursor to microservices. Microservices differ from SOA primarily in their node scope, which is at the feature level. Each microservice node encapsulates a specific set of business logic, such as payment processing. Multiple nodes of business logic interface with independent databases in a microservice architecture. In contrast, SOA nodes encapsulate an entire application or enterprise division. Database systems are typically included within the service boundary of SOA nodes.

Because of their benefits, microservices have become more popular than SOA. The small service nodes provide functionality that teams can reuse through microservices. The advantages of microservices include greater robustness and a more extraordinary ability for vertical and horizontal scaling to be dynamic.

 

Reliability in Distributed Systems: Components

Redundancy and Replication:

Redundancy and replication are two fundamental concepts distributed systems use to enhance reliability. Redundancy involves duplicating critical system components, such as servers, storage devices, or network links, so the redundant component can seamlessly take over if one fails. Replication, on the other hand, involves creating multiple copies of data across different nodes in a system, enabling efficient data access and fault tolerance. By incorporating redundancy and replication, distributed systems can continue to operate even when individual components fail.

Fault Tolerance:

Fault tolerance is a crucial aspect of achieving reliability in distributed systems. It involves designing systems to operate correctly even when one or more components encounter failures. Several techniques, such as error detection, recovery, and prevention mechanisms, are employed to achieve fault tolerance.

Error Detection:

Error detection techniques, such as checksums, hashing, and cyclic redundancy checks (CRC), identify errors or data corruption during transmission or storage. By verifying data integrity, these techniques help identify and mitigate potential failures in distributed systems.

Error Recovery:

Error recovery mechanisms, such as checkpointing and rollback recovery, aim to restore the system to a consistent state after a failure. Checkpointing involves periodically saving the system’s state and data, allowing recovery to a previously known good state in case of failures. On the other hand, rollback recovery involves undoing the effects of failed operations and returning the system to a consistent state.

Error Prevention:

To enhance reliability, distributed systems employ error prevention techniques, such as redundancy elimination, consensus algorithms, and load balancing. Redundancy elimination reduces unnecessary duplication of data or computation, thereby reducing the chances of errors. Consensus algorithms ensure that all nodes in a distributed system agree on a shared state despite failures or message delays. Load balancing techniques distribute computational tasks evenly across multiple nodes to prevent overloading and potential shortcomings.

 

Lack of Connective Event: Traditional Monitoring

If you examine traditional monitoring systems, they look to capture and investigate signals in isolation. The monitoring systems work in a siloed environment, similar to that of developers and operators before the rise of DevOps. Existing monitoring systems cannot detect the “Unknowns Unknowns” that are familiar with modern distributed systems. This often leads to disruptions of services. So you may be asking what an “Unknown Unknown” is.

I’ll put it to you this way: the distributed systems we see today lack predictability—certainly not enough predictability to rely on static thresholds, alerts, and old monitoring tools. If something is fixed, it can be automated, and we have static events, such as in Kubernetes, a POD reaching a limit.

Then, a replica set introduces another pod on a different node if specific parameters are met, such as Kubernetes Labels and Node Selectors. However, this is only a tiny piece of the failure puzzle in a distributed environment.  Today, we have what’s known as partial failures and systems that fail in very creative ways.

 

Reliability In Distributed System: Creative ways to fail

So, we know that some of these failures are quickly predicted, and actions are taken. For example, if this Kubernetes POD node reaches a specific utilization, we can automatically reschedule PODs on a different node to stay within our known scale limits.

Predictable failures can be automated in Kubernetes and with any infrastructure. An Ansible script is useful when these events occur. However, we have much more to deal with than POD scaling; we have many partial and complicated failures known as black holes.

 

In today’s world of partial failures

Microservices applications are distributed and susceptible to many external factors. On the other hand, if you examine the traditional monolithic application style, all the functions reside in the same process. It was either switched ON or OFF!! Not much happened in between. So, if there is a failure in the procedure, the application as a whole will fail. The results are binary, usually either a UP or Down.

With some essential monitoring, this was easy to detect, and failures were predictable. There was no such thing as a partial failure. In a monolith application, all application functions are within the same process. A significant benefit of these monoliths is that you don’t have partial failures.

However, in a cloud-native world, where we have broken the old monolith into a microservices-based application, a client request can go through multiple hops of microservices, and we can have several problems to deal with.

There is a lack of connectivity between the different domains. Many monitoring tools and knowledge will be tied to each domain, and alerts are often tied to thresholds or rate-of-change violations that have nothing to do with user satisfaction. User satisfaction is a critical metric to care about.

 

System reliability: Today, you have no way to predict

So, the new, modern, and complex distributed systems place very different demands on your infrastructure—considerably different from the simple three-tier application, where everything is generally housed in one location.  We can’t predict anything anymore, which breaks traditional monitoring approaches.

When you can no longer predict what will happen, you can no longer rely on a reactive approach to monitoring and management. The move towards a proactive approach to system reliability is a welcomed strategy.

 

Blackholes: Strange failure modes

When considering a distributed system, many things can happen. A service or region can disappear or disappear for a few seconds or ms and reappear. We believe this is going into a black hole when we have strange failure modes. So when anything goes into it will disappear. Peculiar failure modes are unexpected and surprising.

There is certainly nothing predictable about strange failure modes. So, what happens when your banking transactions are in a black hole? What if your banking balance is displayed incorrectly or if you make a transfer to an external account and it does not show up? 

 

Highlighting Site Reliability Engineering (SRE) and Observability

Site reliability engineering (SRE) and observational practices are needed to manage these types of unpredictability and unknown failures. SRE is about making systems more reliable. And everyone has a different way of implementing SRE practices. Usually, about 20% of your issues cause 80% of your problems.

You need to be proactive and fix these issues upfront. You need to be able to get ahead of the curve and do these things to stop the incidents from occurring. This usually happens in the wake of a massive incident. This usually acts as a teachable moment. This gives the power to be the reason to listen to a Chaos Engineering project. 

 

New tools and technologies: Distributed tracing

We have new tools, such as distributed tracing. So, what is the best way to find the bottleneck if the system becomes slow? Here, you can use Distributed Tracing and Open Telemetry. The tracing helps us instrument our system, figuring out where the time has been spent and where it can be used across distributed microservice architecture to troubleshoot problems. Open Telemetry provides a standardized way of instrumenting our system and providing those traces.

distributed tracing

 

SLA, SLI, SLO, and Error Budgets

So we don’t just want to know when something has happened and then react to an event that is not looking from the customer’s perspective. We need to understand if we are meeting SLA by gathering the number and frequency of the outages and any performance issues.

Service Level Objectives (SLO) and Service Level Indicators (SLI) can assist you with measurements. Service Level Objectives (SLOs) and Service Level Indicators (SLI) not only help you with measurements but also offer a tool for having better reliability and forming the base for the reliability stack.

 

Summary: Reliability in Distributed System

In modern technology, distributed systems have become the backbone of numerous applications and services. These systems, consisting of interconnected nodes, provide scalability, fault tolerance, and improved performance. However, maintaining reliability in such distributed environments is a challenging endeavor. This blog post explored the key aspects and strategies for ensuring reliability in distributed systems.

Section 1: Understanding the Challenges

Distributed systems face a myriad of challenges that can impact their reliability. These challenges include network failures, node failures, message delays, and data inconsistencies. These aspects can introduce vulnerabilities that may disrupt system operations and compromise reliability.

Section 2: Replication for Resilience

One of the fundamental techniques to enhance reliability in distributed systems is data replication. By replicating data across multiple nodes, system resilience is improved. Replication increases fault tolerance and enables load balancing and localized data access. However, maintaining reliability is crucial to managing consistency and synchronization among replicated copies.

Section 3: Consensus Protocols

Consensus protocols play a vital role in achieving reliability in distributed systems. These protocols enable nodes to agree on a shared state despite failures or network partitions. Popular consensus algorithms such as Paxos and Raft ensure that distributed nodes reach a consensus, making them resilient against failures and maintaining system reliability.

Section 4: Fault Detection and Recovery

Detecting faults in a distributed system is crucial for maintaining reliability. Techniques like heartbeat monitoring, failure detectors, and health checks aid in identifying faulty nodes or network failures. Once a fault is detected, recovery mechanisms such as automatic restarts, replica synchronization, or reconfigurations can be employed to restore system reliability.

Section 5: Load Balancing and Scalability

Reliability in distributed systems can also be enhanced through load balancing and scalability. By distributing the workload evenly among nodes and dynamically scaling resources, the system can handle varying demands and prevent bottlenecks. Load-balancing algorithms and auto-scaling mechanisms contribute to overall system reliability.

Conclusion:

In the world of distributed systems, reliability is a paramount concern. By understanding the challenges, employing replication techniques, utilizing consensus protocols, implementing fault detection and recovery mechanisms, and focusing on load balancing and scalability, we can embark on a journey of resilience. Ensuring reliability in distributed systems requires careful planning, robust architectures, and continuous monitoring. By addressing these aspects, we can build distributed systems that are truly reliable, empowering businesses and users alike.