How can you use problem-solving skills to optimize system performance?

One thing I do when there is a problem or incident is to tell myself, "every problem has a solution". Then I get working by trying to identify the problem. I do this by: 1. Try and see if I can isolate the problem. 2. If I cannot isolate the problem, I try and replicate the problem somewhere else. 3. If isolating the problem will be problematic, I create a war room with the respective stake holders, where we share ideas and brainstorm.

“Defining the problem…” is usually the issue. “Problems” can be formalized with 3 specific things: 1. What’s wrong 2. What you’d like to happen instead 3. A plan for how to get there If you don’t have all 3, what you have is a complaint. You’ll come across a lot of people who will tell you what’s wrong. But very few are effective at actually providing a path towards a solution.

Aquí tienes los 4 pasos para optimizar ¡cualquier cosa! 😅 Identifica el problema: Haz preguntas como quién está involucrado, qué está sucediendo, dónde ocurrió, cuándo comenzó y por qué afecta los flujos de trabajo. Definir claramente el problema es fundamental. Analiza la situación: Examina las causas raíz y evalúa cómo afecta la productividad del equipo. Comprender el contexto es esencial para encontrar soluciones adecuadas. Busca soluciones: Utiliza técnicas comunes de resolución de problemas. Encuentra la herramienta adecuada para el trabajo y para la solución más apropiada. Implementa y evalúa: Aplica la solución y monitorea su impacto. Aprende de cada experiencia para mejorar continuamente el rendimiento del sistema.

By system performance, I assume, here we mean the OS level performance (ex: Linux/Kernel, etc.). The system performance can be divided into 4 parts: a) CPU Work Load b) Memory Pressure c) I/O throughput d) Network throughput So, to analyze the overall system performance, our first job is to find out which category we fall in. Then reduce the problem area into smaller parts (apply dynamic programming techniques). and solve each part individually. For example, for Linux system performance, remove all network connectivity, boot only the Kernel by dropping the Kernel to a mini ramdisk shell (root=/dev/ram init=/bin/sh) then analyze the performance of Kernel alone. Here also you can breakdown into further tasks. Similarly, move further...

I consider myself fortunate to have encountered Gerald M. Weinberg's "Are Your Lights On?" early in my career. This book profoundly influenced my approach to problem-solving by stressing the importance of understanding the true nature of a problem before attempting to solve it. Weinberg's insights into effective communication, teamwork, and decision-making have been invaluable, shaping my professional growth and fostering a proactive and collaborative mindset. "Are Your Lights On?" continues to serve as a guiding beacon, providing timeless wisdom that transcends the realms of software development and extends into various aspects of life.

Enhancing system performance involves efficient problem-solving. For instance, an online store's sluggishness may stem from slow database queries. Tools like New Relic or Prometheus aid in monitoring, pinpointing issues, and taking quick action. Looking ahead, AIops holds promise by using machine learning to predict and prevent issues. It streamlines tasks like performance monitoring, anomaly detection, and root cause analysis, ensuring seamless operations.

Defining the problem and gathering metrics could really be two distinct points. Gathering existing metrics is great, but sometimes they don't exist. Part of the problem solving process, between defining the problem and starting analysis may be about getting the right measuring sticks into your equation and recreating the problem to work with better data for analysis. I use this as an early part of my problem solving process as often you just need the right clues to get to the bottom of a problem and it may not already exist.

It also can be interesting to see how others approached the problem and build solutions. Be open to solutions which differ from what you are used to and look into everything.

In system optimization, defining the problem is an art of intellect; it’s the distillation of complexity into clarity. We must discern with forensic acuity the delta between expected and actual performance. Metrics are our lexicon, from latency to load, CPU spikes to memory lapses. A masterful definition captures the essence, charts the malaise, and sets the stage for surgical intervention. It’s not mere measurement, but a narrative wrought in data, demanding resolution with unequivocal precision.

We need to identify the symptoms - like what is indicating the poor performance. ex: slow response times, high resource usage or crashes. We will have to quantify the impact of this on users. Need to identify what is our goal with respect to optimization ex: faster processing, reduced resource consumption etc.,

Analysis is not just about data and metrics, but should also be first principles architecture analysis. Understand the architecture, the overall flow of data, the sequence of computations and the components involved. Explain it out loud to someone else too. This is a great start point. So, when you start looking at metrics, you know what you are looking for, and understand whether the hypothesis works.

It is important to develop a experimental mindset. Make a hypothesis, and the do experiments to prove or disprove the problem. Just looking at mountains of data rarely yields any insight.

Problems are inherent in life, requiring analysis for effective resolution. Take, for instance, the challenge of urban congestion. Rapid population growth strains infrastructure, leading to traffic jams and pollution. By dissecting this issue, we can identify root causes like inadequate public transport and urban planning. Such analysis facilitates targeted solutions like expanding public transit and implementing congestion pricing. By applying this approach to diverse problems, from environmental degradation to social inequality, we pave the way for impactful change.

Using a coworker in the relevant area of the performance issue could be a great thing for uncovering the issue by having a broader image of how to address and start analyzing it. Furthermore, debugging, logging, and testing tools are a must because they are the tools to analyze the problem, but the plan to analyze the problem is far more important. The plan should have a concise plan and tests that each one has steps to re-produce and results. Once following those steps, after each test, by having a very specific way to describe a test, we would be able to narrow down where the core issue resides, and then create further test or modify our plan accordingly.

Start by distancing yourself from the problem to get an objective perspective first. Be aware of your biases so you don’t cloud the problem. Once you have achieved this neutral position, then tap on to your system thinking and decompose the problem into tractable pieces - this is a big part of the analysis. Logically approaching this with your innate ability to understand the cause and effect makes it more likely for you and the team to analyze the problem. Be persistent and keep track of different hypotheses. Tap into domain experts as needed but remember you are the quarterback!

When the ability to solve problems is present, the performance of the system is optimized. It is not necessary to deploy who knows what knowledge, the human factor is sufficient. The interventions of those with a problem-solving attitude will be of this type: - will address the problems that degrade performance the most first; - will choose to solve the easiest problems to solve first; - rather than leaving the system as it is, at least one problem, even if small, will be solved. If someone who has problem-solving skills intervenes, there will be an improvement in the system's performance.

Para analizar el rendimientos de sistemas, enfocado en modelos actuales de nube específicamente, hay que familiarizarse con las herramientas nativas que ofrecen los proveedores, muchas veces mejores y más completas que productos de terceros que recopilan el mismo tipo de información. Si bien hay muchas herramientas no nativas, conviene apoyarse en estas cuando son transversales a diferentes proveedores. Asimismo nos disponibilizan generalmente las API que permiten, con cierto esfuerzo inicial, recopilar los registros y diagnósticos de seguimiento de diferentes proveedores para tener una visión global en ese tipo de escenarios complejos.

From my experience, too often engineers and developers get entrenched in a "tunnel vision" approach, focusing intensely on the immediate symptoms of a problem rather than its underlying causes or the system as a whole. This can lead to temporary fixes rather than long-term solutions. The significance of stepping back to view the system from a broader perspective cannot be overstated. It's about understanding that the system is more than just the sum of its parts; it's about the interactions between those parts.

You need to use the profiler in step one. Once you see where the issues are you can then see what caused the problem and analyze the problem to determine the solution. Premature optimization is caused by people reversing these two steps and adding unneeded complexity to unrelated areas.

Dive deeper into the problem by examining the collected data to understand the root causes of the performance discrepancy. Use tools and techniques like profilers to identify performance bottlenecks. Cloud vendors provide good tools for this.

Given that system performance is the targeted problem, one may choose to target improvements at both the application layer and the infrastructure. - Analyse and optimize database interactions by avoiding repeated queries and adding indexes for frequently accessed data - Optimize inter-service communications by skipping redundant network calls, reducing system latency, and minimizing failure points - Choose idempotency to gracefully handle retries in case of failures - Follow a proper logging and monitoring framework to observe the future behaviour of the system and detect issues early

A crucial aspect here is to balance creativity and effectiveness. You definitely need to think out of the box to come to the best solution sometimes, but also you can't test dozens of different ones. With time, there will be a better ability to skim solutions, so that only the feasible and most likely effective ones can be proposed and tested out.

In confronting challenges, the ability to generate solutions is paramount. Consider the issue of renewable energy adoption. By fostering innovation, we can develop advanced solar panel technology, enhancing efficiency and affordability. Similarly, collaborative efforts can lead to breakthroughs in wind and hydroelectric power. Through strategic partnerships and research investment, we cultivate a sustainable energy landscape. By applying this approach across sectors, from healthcare to education, we unlock transformative solutions, shaping a brighter future for generations to come.

Once you have identified and analyzed the actual real performance bottle necks of your program can you come up with a solution. Often you will have to make some dramatic changes to program architectures to fix problems in the original design of the program. Often you have to refactor large sections of the code to organize the code before you can fix it. It is best to do this in two steps. Refactor but keep the code working the same, once this is working and correct, then introduce the changes to the code to fix the performance issues. Trying to do this in one step can create hard to identify problems where you don't know if errors were caused by your refactor or the solution.

Based on the analysis, brainstorm potential solutions to address the identified bottlenecks. Consider various approaches, including optimizing code, upgrading hardware, or re-configuring system settings. Forums, communities and working with your vendor and other customers in the same industry that have had a similar problem. People like to talk about their problems and how they solved them.

The third step involves generating potential solutions to tackle the root cause of the problem and enhance system performance. Employ creative techniques like brainstorming, divergent thinking, lateral thinking, or analogies to explore various avenues. Assess different facets of the system architecture—design patterns, algorithms, data structures, protocols, frameworks, libraries, or platforms—to identify areas for modification, replacement, or enhancement. Evaluate each solution's feasibility, effectiveness, cost, and trade-offs, prioritizing based on impact and urgency.

To generate a solution for a performance-based problem, a deep understanding of the platform/product and the used tools(protocols, OS drivers, etc...) are crucially needed. Let's say we understood that a problem of an API that we have is delivering a result back in 10 seconds instead of a supposed 100ms. This means the code that handles the API has additional CPU commands executed or even long IO-bound operations, but with those in mind and the entire system's broad view, could lead to understanding how to solve the core issue. To achieve something like that, it involves a lot of self-learning, intuition, out-of-the-box thinking, and brainstorming with self and co-workers for defining a proper solution.

Bringing in a cross functional/domain experts and driving them towards root causing and solution is one of the coveted skills a systems lead must have. The more you tackle this - the better and elegant the solutions will be. In some cases you will have to make tradeoffs between various solutions - using effort, time to solution and robustness you can guide the team towards the appropriate solution.

In the crucible of innovation, step three demands we conjure solutions as a virtuoso plays with symphonies. We must canvas the architecture's vast landscape, wielding creativity as our guide. Brainstorming, divergent thinking—these are the tools to excavate profound enhancements from the bedrock of existing paradigms. Each algorithm, each protocol is reimagined, their potentials unfurled like a grand tapestry. It's a cerebral dance of what is with what could be, prioritizing not just on efficacy but on the alacrity of impact.

Problem solving is a very personnal skill, you may however use different social engineerings to sprout new ideas. -Design thinking -Agile method -Brainstorming -...

The fourth step entails implementing the chosen solutions and testing their outcomes, leveraging technical abilities like coding, debugging, testing, deploying, or configuring. Adhere to system architecture best practices and standards such as modularity, readability, maintainability, security, or reliability. Ensure compatibility and consistency with system requirements and specifications. Monitor and measure system performance post-implementation, comparing results with baselines and expectations for effectiveness assessment.

Implementing solutions marks the transition from planning to tangible change, a critical phase in problem-solving. Take, for instance, the challenge of plastic pollution. Implementing solutions involves enacting policies to reduce single-use plastics, incentivizing recycling, and promoting eco-friendly alternatives. Through government regulations, corporate responsibility initiatives, and community engagement, we can curb plastic waste, safeguarding our environment for future generations. Effective implementation transforms ideas into impactful outcomes, driving progress and fostering a sustainable world.

Si estás en la etapa de implementar la solución asegura que tu equipo o personal encargado de hacerlo tengan una comunicación clara respecto al avance o stoppers detectado durante este proceso. Generar confianza a exponer cualquier duda o inconveniente es esencial.

Start with a small change and gather results over a short/meaningful amount of time. Validate whether results indicate if the solution can be deployed on a larger scale.

Implement solutions by coding, debugging, testing, and deploying, adhering to best practices for modularity, readability, and security. Ensure compatibility with system requirements. After implementation, monitor performance, comparing it to baseline and expectations to gauge effectiveness. This step is crucial for validating the impact of the solutions on system performance.

Because the nature of solving performance-based challenges could take long and is uncertain in a way, the implementation of a solution could be implemented not as the final product, and rather as a POC of itself. Why? Because as with startups, we to develop a huge e2e solution that's not necessarily going to be used? Therefore, after previous steps, and understanding the core location of the problem, an iterative approach of trying to solve it, but rather as a POC perspective that leads to faster check times is gonna be crucial for many reasons - motivation to keep going and searching for a solution, faster response time probably, and a more focused on each iteration of a test/check for those small POCs.

Implementation is the crucible of system optimization. It's where the rubber meets the code. With meticulous precision, we encode solutions, debug with the tenacity of a detective, and deploy with the finesse of a maestro. We sculpt in the medium of technology, ever mindful of the architectural canon—modularity, efficiency, security. Each line of code is a commitment to elegance, each deployment a testament to functionality. We measure our success not just in benchmarks met, but in potential unlocked.

Thr models, instrumentation, and workload characterizations you have done are important because the set the goals. If the end product does not meet a goal you must determine exactly why. Sometimes development partners can give up too soon. Models let's you keep noses to the grindstone.

Reviewing solutions is pivotal to ensuring effectiveness and refining approaches for optimal outcomes. Consider healthcare reform. Through rigorous assessment, we gauge the impact of policy changes on accessibility and affordability. By analyzing data and soliciting feedback from stakeholders, we can identify areas of improvement and adapt strategies accordingly. This iterative process enhances healthcare delivery, leading to better outcomes for individuals and communities alike. Reviewing solutions fosters continuous improvement, driving progress in addressing complex challenges across various domains.

Go back to the original analysis you already did to find the performance bottleneck and run the same analysis again. If the performance is now increased, you fixed the issue. Go through a complete module test, unit test, and QA cycle to ensure it is all working correct and you didn't introduce other regressions to performance.

Embrace innovation and stay open to adopting new technologies and methodologies that can enhance system performance. This includes being agile and willing to experiment with new approaches.

It's very simple actually. If we're using the right tools to measure the performance issue in the first place, which means we're working with tools that help to identify the problem in a precise way, then after doing a possible solution, or rather the final solution, a simple re-run of our tools could be enough. That's of course for the core performance issue. I do recommend for doing re-runs on the entire executable if possible, or relevant areas that were changed for this solution, because sometimes the performance-based issues could go to another route by a mistake.

El seguimiento a la solución dada es primordial. Puedes encontrar un nuevo escenario que no habías contemplado y con ello regresar a hacer análisis de la nueva problemática. Tener un kit de casos de uso en pruebas es esencial.

A properly defined development should have defined validation conditions on the first step. These conditions should take the form of tests, manual or automated, to give reports on the effectiveness and validity of the solution. If there exist several solutions, these tests may serve to compare them exhaustively.

Don't solve non-existent problems. Optimizations should start with measurements. Some SW engineers mistakenly assume they know the bottleneck, and some engineers micro-optimize because it is fun. One indeed should choose the most optimized alternative whenever the alternatives have equal readability and clarity. But most of the time micro-optimizations hurt the readability. When the code doesn’t express the intent of its author, the maintenance is more costly and risky. Therefore, analyzing the performance before and after should be an integral part of SW optimizations.

For centuries, logic reigned supreme in optimizing systems. But is brute force analysis holding us back? Enter Generative AI, a game-changer. It bypasses traditional frameworks, unleashing hidden possibilities. Imagine AI concocting unique design solutions, exploring vast "what-if" scenarios for science, or generating efficient engineering marvels – all by breaking free from logic's constraints. Don't ditch reason, but embrace redirection – a walk, a doodle, anything to shift perspective and unlock AI's magic. Remember Darwin's eureka moment on a carriage ride? Redirect, experiment with AI tools, and see where it takes you. Unleash the power of Generative AI and unleash your potential. Let's optimize, together.

I often find the people living with these systems actually KNOW how to solve many of the problems, but can't proceed effectively for various non-technical reasons and are stuck. It's worth considering the following questions when implementing a solution or analyzing a problem: 1. How did things get to this point? What is the origin story of this situation? 2. How likely is this to recur or manifest in a related fashion? Is it the symptom or the cause that is being addressed? 3. Is the existing team capable of maintaining and improving upon this solution, once in place? Are stakeholders supportive? A bonus consideration would be: "How can we capture this process, lessons learned, etc. to save time for ourselves and others in the future?"

system performance depends on several components such as cpu, memory, storage and network and one has to determine where the bottlenecks are. Start with off-the-shelf unix or linux tools, there are tons of great guides to do so. Next, look into the applications. If this is relevant, use application profiling; if it is a parallel computing application use vampir or similar tools; if it is nvidia use nsight or/and dali to optimize i/o

Sometimes people invested in the original system will fight your proposed changes. Other times people will want to throw out the existing code and start over from scratch. Resist both these groups of people. It is common for code to undergo some massive changes and refactoring in its life. Use cases for the software can change, new tech can libraries can change the basic ways of doing things. And the thousands of bug fixes that took a week to figure out for some critical corner case will get thrown out with that complete rewrite and that work will have to be repeated in the new code base.

Understand that system optimization involves more than just technical adjustments. It requires a comprehensive view that includes understanding user needs, business goals, and the technological landscape.

Another aspect to consider optimizing system performance is an iterative process. So plan to apply problem-solving skills systematically and continuously, to keep the systems running smoothly and efficiently.

In my personal experience, the systems that have the most problems are those that are not clear about how to carry out the desired process, therefore any implementation, regardless of quality, will have deficiencies. The best way to avoid problems and be able to optimize the performance of a system is to know in detail the high-level process and the available infrastructure, to design a system architecture that optimizes processes based on resources and security, with the purpose of simplifying the system implementation and maintenance process.

Consider the importance of continuous improvement and feedback loops. Real-world examples show that iterative enhancements and user feedback are invaluable. Embrace failure as a learning tool; not all solutions will work as expected, but each attempt provides insights. Highlight the role of collaboration across teams—engineers, developers, and users can offer diverse perspectives that enrich the problem-solving process. Finally, remember that optimizing system performance is not a one-time task but an ongoing commitment to excellence.