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Introduction: What Happens When Computers Break?

In this day and age, I personally think it is safe to say we cannot do without the internet and technology, that is coming from someone whose daily life is deeply intertwined and quite dependent on tech.

Technology today powers our news, banking and numerous applications that are valuable to our daily lives so naturally people expect technology to work seamlessly around the clock.

Technical components and the internet at large in this time and age are powered by a complex network of servers, databases, and software components that can and do fail. They are generally referred to as Distributed Systems and I talked about them in detail in my last post which you can read here.

Computing environments are generally used to experiencing hardware malfunctions, software bugs, network outages, and unexpected spikes in traffic at scale, either small or large.

On a normal day, finding solutions to such anomalies would require the involvement of teams of engineers to monitor systems, diagnose problems, and implement fixes.

However, with the continuous rise of automation and the complexities that comes with it, a pivotal question comes to light, could it be possible for technical systems to consciously identify, diagnose, and repair their own problems without needing input from human engineers?

That question is the reason why in this article we are going to take a look at self-healing computing systems.

Instead of waiting on human engineers to implement fixes, self healing computing systems are actually designed to detect failures, respond automatically, and revert to their normal operation mode whenever possible.

Though, fully autonomous self-healing systems are still not in existence yet, many of applications in this current day and age are already exhibiting elements of self-healing behavior.

What Is a Self-Healing Computer System?

A computer system or program that is empowered with the ability to automatically detect, diagnose, and recover from failures with minimal or no human intervention can be considered a self healing computer system.

It is quite important for systems to maintain as much uptime as possible without hiccups and one of the optimal modern day solutions that is currently gaining wide adoption is giving the system the capacity to adapt to changing conditions and recover from disruptions in real time.

In simple terms, a self-healing system is designed to do three things. Firstly, it undergoes a continuous process of monitoring its own health through logs, metrics, and performance indicators. Secondly, it uses the data available through the logs to detect abnormalities and determine the likely cause of a problem when it exists. Lastly, it engages the issue as long as it is equipped to engage such issue and implements corrective measures in order to return the system to normalcy.

It is almost a compulsory requirement for modern distributed systems to be equipped with the ability to self heal, given the volume and scale at which they operate. Hiccups and downtimes are naturally expected in environments like that. Network connections might fail, servers may run into bottlenecks that may cause them to crash., there might be a bug in the code somewhere.

Modern distributed systems are widely known for incorporating automated recovery mechanisms that helps them maintain reliability and availability. While these mechanisms are not fully autonomous, they are a stepping stone towards the emergence of fully autonomous infrastructure that can maintain itself without constant human oversight.

Technologies That Already Make Systems Heal Themselves

A lot o applications today have been known to employ and demonstrate the presence of technologies that are designed to provide some form of self-healing for commonly identifiable problems.

Automatic Failover

Automatic failover comes into effect when primary systems fail and backup systems take over to keep the service or application alive. For example, if a server powering a website crashes, a distributed system with multiple backup instances could redirect traffic to a backup server, ensuring that there is little to no disruption.

Load Balancing

Load balancers help in spreading requests across multiple servers in order to prevent one single server from being overwhelmed from too many requests. In a situation where one server becomes unavailable or overloaded with requests, other similar servers running the same programs on the network can have traffic redirected to them. By so doing localized failures would have little negative impact on the service availability.

Auto Scaling

Auto scaling occurs when a system is designed to automatically add new instances or servers to balance the load on the network in that moment to prevent overwhelming the system or vice-versa when there are too many servers and not as much request to justify the need for that amount of servers. This allows systems to adapt dynamically to changing workloads.

Container Orchestration

Nowadays, devops tools like Kubernetes exist exist to continuously monitor running applications. In the event that a container crashes or becomes unhealthy, such orchestration platform can automatically trigger a restart it or spin up a new instance to take the place of the unhealthy one.

Monitoring and Observability

There are a number of tools now that are primarily built to collect information about system performance, resource utilization, and application health. These tools can help to identify irregularities in the systems before they become major problems or cause any damages.

Self healing systems do not exist merely in a theoretical sense as shown by the measures laid out above. Many aspects of modern applications already have a inbuilt ability to detect and respond to specific types of failure without having a human intervene.

Why Fully Self-Healing Systems Are So Difficult

Building fully functioning self-healing systems poses a big challenge in computer science and systems engineering.

Complexity is a major concern because self healing systems exist on different layers of solutions that span across monitoring, analysis and execution among other layers.

New age applications are commonly made up of thousands of components that are interconnected and distributed across multiple geographic regions. If a single component runs into issues, it can affect other components in the system in an unexpected fashion.

There's also a problem of handling unexpected and unknown failures. It's easy for a system to auto recover after experiencing failures if the issue is a known or expected issue. However, the reality is that it is quite difficult and bordering on impossible to identify where the next point of failure would be or the conditions that would lead to that failure, making them difficult to predict and resolve automatically.

Cascading failures tends to lead to further complications. In distributed systems, a minor issue can easily spread throughout the system and lead to major outages. Failure or disruption in service may cause backup servers to become overloaded, which can in turn cause further failures, creating a chain reaction that eventually renders the system unusable.

Context and judgment also comes into play. In many organizations, engineering decisions are usually affected by business priorities, customer impact, and operational trade-offs. Being able to recreate such factors in technical environments can be challenging for automated systems.

As a result, self-healing systems cannot just exist to fix technical faults alone, the underlying conditions that led to such faults must be traced, analyzed and understood.

The Role of Artificial Intelligence

As with many branches of modern technology today artificial intelligence is well positioned to play an important role in the realization of self-healing computing systems.

LLMs possess the ability to analyze huge quantities of operational data in the system logs to detect patterns that might not be easily detectable by humans. By identifying anomalies that are not easily noticed by humans, AI systems and agents can potentially identify problems before they result in full blown downtimes, report such issues to the appropriate quarters or even implement fixes for such issues autonomously in cases where such approach is employed.

Such concept is generally referred to as AIOps, or Artificial Intelligence for IT Operations. AIOps platforms employ the use of machine learning techniques and LLM models/agents to automate tasks such as anomaly detection, root-cause analysis, and predictive maintenance.

However, as useful as AI may be it is not the final solution. LLM based systems require a lot of data, so a consistent and accurate data feed has to be established for it to function effectively. Furthermore, the AI model has to be trained extensively to ensure that automated decisions align with organizational goals and operational requirements are met at every point in time.

Even so, AI continues to help move the industry further closer to the actualization of fully self-healing systems with increasing levels of autonomy.

Could We Ever Reach Fully Autonomous Computing?

Autonomous computing and self healing systems are not a new concept. For more than two decades, researchers have been examining the concept of autonomous computing, and trying to build systems that are capable of self-configuration, self-optimization, self-protection, and self-healing.

With recent advances in cloud computing, automation, and artificial intelligence that vision is getting closer to reality. Data centers are now increasingly able to allocate resources, orchestrate workloads, recover from failures, and optimize performance with little to no human involvement.

At the same time, reaching full autonomy is still somewhat of a challenge due to numerous unforeseen complexities involved. With each new discovery in technology today, we are attaining more complexity, not less. New technologies, evolving security threats, continuously changing user behavior and unpredictable interactions between applications brings about more uncertainty.

At this point, it is safe to say that in the nearest future humans will increasingly play a role of supervision and strategic guidance as opposed to total disappearance from system operations entirely.

Conclusion

The infrastructure that powers the internet is not flawless, so it is imperative that is designed with self-healing and auto recovery in mind. Employing measures such as automatic failover, load balancing, container orchestration, and AI-driven monitoring have already made a positive impact in terms of the reliability of modern computing infrastructure.

However, building fully autonomous self-healing computer systems is still quite challenging. Today's self healing systems function in a deterministic manner, where known problems already have provided solutions that can be triggered in the event that such problem arises, that being said, handling unexpected failures and making context-aware decisions still requires human expertise.

Nevertheless, the direction we are heading is obvious. Automation is the future and artificial intelligence will play a key role in it, in a few years from now computer systems will become more capable of managing themselves. We should be expecting future computing to ultimately resemble a living organism, constantly monitoring, adapting, and repairing itself to ensure survival in an ever-changing environment.