Performance Optimization Techniques

📌 How to do Prioritization as a Product Manager. Product Managers face a problem of plenty. You have so many things to do, many problems, many solutions, and many suggestions, but are always limited by time, bandwidth, and resources. Now you need to obsessively prioritize and filter ideas before you put them in the roadmap. But how do you prioritize? The simplest yet most powerful framework that most PMs rely on is the Impact v/s Effort Framework. The impact is determined by: - Potential revenue estimate, - Customer value, - Alignment with company goals, - Demand from the market, or - Any other relevant metrics that align with product goals. Impact estimation is mostly the responsibility of the product manager. The effort is determined by: - Development complexity, - Engineering efforts, - The time required & cost, - Operations complexity, etc. Effort estimation is mostly done by the delivery teams like engineers, design, ops, etc. This is a collaborative exercise. The next step is to visualize this through an impact v/s effort matrix. Provided that the estimations are done correctly, the low efforts & high impact items are picked at the earliest, & other things are prioritized in a logical order. 📌 3 Tips to take your prioritization game to the next level: 1. Consider tradeoffs at every step: Some high efforts ideas could be of high strategic importance, similarly some low-impact ideas could be critical for customer experience. Understand the situation from all angles. 2. Look out for red flags: All ideas look high impact, or the backlog is completely filled with low effort low impact ideas. This indicates either the PM is not competent at impact estimation or is not considering enough ideas during product discovery before deciding on the best one. 3. Validate high-effort ideas by first converting them into low efforts experiments. For example: Rather than converting your whole website into all Indian languages, try to convert the most popular pages into 3 popular languages, observe the results and then decide to roll back or go all in. 📌 Other frameworks for prioritization: There will be times when you'll need more detailed frameworks to prioritize, some of the other helpful frameworks are: 1. KANO: Puts customer satisfaction at the center and distinguishes between basic expectations, performance attributes, and delighters. 2. MOSCOW: categorizes requirements into four priority levels: Must have, Should have, Could have, and Won't have. 3. RICE: adds to more dimensions of Reach and Confidence to make Impact v/s Effort more reliable and exhaustive. ✨ Prioritization is a supercritical and useful skill for product managers, during their work, stakeholder management, and also during interviews. Do you think this would be helpful for you? I share helpful insights for product managers almost every day, consider connecting here 👉🏽 Ankit Shukla to not miss out. #productmanagement #prioritization

Study: Generators May Provide a Faster Path to Power A new study by energy researchers suggests that data centers could get faster access to power by adopting load flexibility, agreeing to briefly curtail utility usage and shift to generator power. In an in-depth analysis of the U.S. power grid, researchers at Duke University estimate that this approach could tap existing headroom in the system to more quickly integrate at least 76 gigawatts of new loads, arguing that even a small reduction in peak demand could reduce the need for new investments in transmission and generation capacity - as well as the need to pass on those investments to ratepayers. Data centers are all about uptime, and thus have been resistant to innovations that create additional risk around reliability. But current power constraints in key markets, along with growing demand for AI training workloads (which may be more interruptible than cloud or colocation) has prompted the industry to explore load flexibility options. Last year the Electric Power Research Institute (EPRI) launched the DCFlex project to work with utilities and a number of data center operators - including Compass Datacenters, QTS Data Centers, Google and Meta - on pilot projects for load flexibility. The Duke study, titled "Rethinking Load Growth," puts some interesting numbers on the upside potential. Their findings: - 76 gigawatts of new load could be enabled by a annual load curtailment rate of 0.25% of maximum uptime, equivalent to 1.7 hours per year operating on backup generators. - An annual curtailment rate of 0.5% (2.1 hours annually) could enable 98 GWs of new load, while a rate of 1.0% (2.5 hours) could boost that to 126 GWs. - A 0.5% curtailment could enable 18GWs in the PJM and 10 GWs in ERCOT, the research finds. At least one hyperscaler seems open to the idea. “This is a promising tool for managing large new energy loads without adding new generating capacity and should be part of every conversation about load growth,” said Michael Terrell, Senior Director of Clean Energy and Carbon Reduction at Google, in a LinkedIn post. With the acceleration of the AI arms race, speed-to-market is now a top priority, along with a competitive opportunity cost for companies that are unable to deploy new capacity. There are tradeoffs to consider (including more emissions), but the Duke paper will likely advance the conversation. Duke study: https://lnkd.in/eS3s_pvk Background on DCFlex: https://lnkd.in/euK746Zy

As Product Managers it’s so easy to loose trust if features on the roadmap are not prioritised correctly. Here are 5 prioritization frameworks and when to actually use them: 1. RICE (Reach, Impact, Confidence, Effort) ✅ Use when: You have multiple ideas/features and want to prioritize based on expected impact. 📌 Best for: Growth experiments, new features, MVP ideas 💡Tip: Confidence % is often biased calibrate with data! 2. MoSCoW (Must have, Should have, Could have, Won’t have) ✅ Use when: You’re working with tight deadlines and multiple stakeholders. 📌 Best for: Sprint planning, product launches 💡Tip: Don’t let every stakeholder label everything as “Must have.” 3. Kano Model ✅ Use when: You want to balance delight with functionality. 📌 Best for: Customer-facing products 💡Tip: A feature that delights today might be expected tomorrow. 4. ICE (Impact, Confidence, Ease) ✅ Use when: You want a quicker version of RICE for fast decision-making. 📌 Best for: Rapid prototyping, early-stage prioritization 💡Tip: Use ICE when you don’t have a ton of data but still need to move. 5. Value vs. Effort Matrix ✅ Use when: You want to visualize trade-offs with stakeholders. 📌 Best for: Roadmap discussions, stakeholder alignment 💡Tip: Plot features on a 2×2: * Quick Wins (High value, low effort) * Strategic Bets (High value, high effort) * Time Wasters (Low value, high effort) * Fillers (Low value, low effort) So which one should you pick? Use RICE when you’re in a data-driven company. Use MoSCoW when time is tight and alignment is tough. Use ICE when you need speed > accuracy. Use Kano when delight matters. Use the Value/Effort Matrix when people keep asking, “Why this first?” 📌 Save this for your next prioritization war. 💬 Tried any of these at work? Drop your go-to framework in comments! #productmanager #job #PMjobs #learning #frameworks

Making “AI” work in the field I enjoy posting on my ideas for sequential decision analytics, but boy do I love it when it actually works in the field. Below are the results of the planning system by Optimal Dynamics running at two truckload carriers. The tools include optimal bidding, load acceptance, and real-time dispatch. Within weeks of implementation, we are getting bumps of 23 percent and 13 percent in revenue per driver!   These are not simulations – these are the benefits in the field. These are numbers that can change an industry.   It starts with using the right analytical technologies, and the planning systems are all built around the universal framework that I have been posting about.    But there is much more to this success than just analytics: data engineering, communications, performance monitoring, user interface, working with dispatchers, business process change, … “AI” is not the magic that we read about in the press – there is a lot of work that goes into making it work in the field.

Cloud computing infrastructure costs represent a significant portion of expenditure for many tech companies, making it crucial to optimize efficiency to enhance the bottom line. This blog, written by the Data Team from HelloFresh, shares their journey toward optimizing their cloud computing services through a data-driven approach. The journey can be broken down into the following steps: -- Problem Identification: The team noticed a significant cost disparity, with one cluster incurring more than five times the expenses compared to the second-largest cost contributor. This discrepancy raised concerns about cost efficiency. -- In-Depth Analysis: The team delved deeper and pinpointed a specific service in Grafana (an operational dashboard) as the primary culprit. This service required frequent refreshes around the clock to support operational needs. Upon closer inspection, it became apparent that most of these queries were relatively small in size. -- Proposed Resolution: Recognizing the need to strike a balance between reducing warehouse size and minimizing the impact on business operations, the team developed a testing package in Python to simulate real-world scenarios to evaluate the business impact of varying warehouse sizes -- Outcome: Ultimately, insights suggested a clear action: downsizing the warehouse from "medium" to "small." This led to a 30% reduction in costs for the outlier warehouse, with minimal disruption to business operations. Quick Takeaway: In today's business landscape, decision-making often involves trade-offs.  By embracing a data-driven approach, organizations can navigate these trade-offs with greater efficiency and efficacy, ultimately fostering improved business outcomes. #analytics #insights #datadriven #decisionmaking #datascience #infrastructure #optimization https://lnkd.in/gubswv8k

Unlocking Focus: A Simple Framework To Prioritise The Initiatives That Matter I facilitated a workshop with the leadership team of one of my technology clients yesterday, where we focused on a critical challenge: how do we prioritise outcomes over hours to maximise effectiveness? The solution? A simple but powerful tool I've relied on for years, which I learned during my time at General Electric (GE) - the Ease/Impact Matrix. Here's why it works so brilliantly: We often gravitate toward quick wins without considering their actual value. This matrix forces the team to evaluate everything through two critical lenses: ✅ High Impact + High Ease = Quick Wins (do immediately, gain momentum) ✅ High Impact + Low Ease = Long-term Bets (worth the investment) ❌ Low Impact + Low Ease = Avoid at All Costs ❓ Low Impact + High Ease = Question Why (just because we can, should we?) By reorienting around impact, we focused on what will truly benefit their business both immediately and in the long run. Sometimes the simplest tools create the most profound shifts. What frameworks have you found most valuable for prioritisation? #OutcomesOverHours

Controversial take: Stop trying to do more marketing. Start eliminating the 60% of activities draining your resources. Here's the prioritisation framework I use with my clients to make every marketing dollar count: 1. For Strategic Direction: Impact/Effort Matrix Stop treating all marketing activities equally. Plot everything on this grid: → High Impact, Low Effort: Growth Accelerators (Must prioritise NOW) → High Impact, High Effort: Strategic Investments (Schedule with dedicated resources) → Low Impact, Low Effort: Quick Wins (Batch process when possible) → Low Impact, High Effort: Resource Drains (Eliminate or automate) The most successful CMOs spend 80% of their time on high-impact activities. Yet most marketing teams spread resources evenly across all quadrants. 2. For Campaign Selection: The 3C Framework Before launching any campaign, run it through these filters: → Check alignment with business goals: Does this directly support our primary objective? → Calculate potential ROI: Estimate returns using: Reach × Conversion × Value → Consider resource constraints: Rate campaigns by resources needed vs. available I've watched founders chase trendy channels with terrible ROI while ignoring proven channels simply because they weren't exciting enough. 3. For Budget Allocation: The 70/20/10 Rule Smart marketers divide their budget following this simple ratio: → 70%: Core marketing activities with proven returns → 20%: Emerging channels showing early success → 10%: Experimental initiatives with learning potential If you are just getting started, flip this model, pour all resources into experiments until you find green shoots. 4. For Daily Execution: The Eisenhower Matrix for CMOs Your time is your most valuable marketing asset. Protect it fiercely: → Urgent & Important: Campaign emergencies, key stakeholder requests aligned with objectives  → Important, Not Urgent: Strategy development, team coaching → Urgent, Not Important: Most emails, status meetings (Delegate these!) → Neither Urgent Nor Important: Vanity metrics, unfocused competitor research (Eliminate) The best marketing leaders I know spend most of their time in the "Important, Not Urgent" quadrant. The struggling ones live in "Urgent, Not Important." The startups I've seen scale fastest don't have bigger budgets or better tools. They're just ruthlessly disciplined about prioritisation. Which of these frameworks would have the biggest impact on your marketing efforts? Share below 👇 ♻️ Found this helpful? Repost to share with your network. ⚡ Want more content like this? Hit follow Maya Moufarek.

Excellent new report from The Brattle Group and Clean Air Task Force, "Optimizing Grid Infrastructure & Proactive Planning to Support Load Growth and Public Policy Goals." The report is a treasure trove of actionable ideas, but two stand out in particular relevant to our research: 𝟭) 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱 𝗳𝗼𝗿 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝘂𝗽𝗴𝗿𝗮𝗱𝗲𝘀 𝗯𝘆 𝗳𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗶𝗻𝗴 𝗰𝗼-𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗻𝗲𝘄 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗹𝗼𝗮𝗱 𝗶𝗻 “𝗲𝗻𝗲𝗿𝗴𝘆 𝗽𝗮𝗿𝗸𝘀”: Co-locating new load with new on-site generation in controllable “energy parks” (i.e., large microgrids) can minimize or avoid entirely the need for transmission upgrades, increasing speed to market while reducing system and customer costs and potentially providing emissions reduction benefits. 𝟮) 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗻𝗼𝗻-𝗳𝗶𝗿𝗺, 𝗲𝗻𝗲𝗿𝗴𝘆-𝗼𝗻𝗹𝘆 (𝗘𝗥𝗜𝗦) 𝗶𝗻𝘁𝗲𝗿𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗼𝗻 𝘁𝗼 𝘂𝗽𝗴𝗿𝗮𝗱𝗲 𝘁𝗼 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 (𝗡𝗥𝗜𝗦, 𝗼𝗿 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆) 𝗹𝗮𝘁𝗲𝗿: Simplifying energy-only interconnection criteria for new POIs to reflect the non-firm (i.e., dispatchable down or curtailable) nature of resources would avoid such time-consuming network upgrades and dramatically speed up interconnection timelines by relying on market-based congestion management to avoid network overloads, as illustrated in a recent Duke University study. Well done Johannes Pfeifenberger Long Lam Kailin Graham Natalie Northrup Ryan Hledik and Nicole Pavia Kasparas Spokas! Summary: https://lnkd.in/eaUmHvgi Full report: https://lnkd.in/eJx-zGzt

Load Balancing Algorithms Developers Should Know. Effective load balancing is crucial in system design, providing high availability and optimizing resource utilization. Let's look at how some of the most popular load balancing algorithms work. 🔹 𝗦𝘁𝗮𝘁𝗶𝗰 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝟭) 𝗥𝗼𝘂𝗻𝗱 𝗿𝗼𝗯𝗶𝗻 It distributes requests sequentially between servers, ensuring equitable distribution. Despite its simplicity, it does not account for server load, which might be a drawback when demand changes significantly. 𝟮) 𝗥𝗮𝗻𝗱𝗼𝗺 Implements a simple way of distributing requests regardless of server load or capability. This form of load distribution is basic, less precise, and suitable for less complicated applications. 𝟯) 𝗜𝗣 𝗵𝗮𝘀𝗵 Uses a consistent hashing method depending on the client's IP address to route requests. This technique is one way to ensure session persistence by consistently directing requests from the same client to the same server. 𝟰) 𝗪𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝗿𝗼𝘂𝗻𝗱 𝗿𝗼𝗯𝗶𝗻 Improves round robin by assigning requests based on server capacity, aiming to better utilize resources by allocating more requests to higher-capacity servers. This approach seeks to optimize resource use, though actual results can vary with request complexity and system conditions. 🔹 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝟱) 𝗟𝗲𝗮𝘀𝘁 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Intelligently sends requests to the server with the fewest active connections, adapting to changing loads. This technique aims to better reflect current server utilization, potentially leading to more efficient resource consumption. 𝟲) 𝗟𝗲𝗮𝘀𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝘁𝗶𝗺𝗲 Targets performance by routing requests to the server with the quickest response time. By considering both current server load and performance, this technique supports faster processing, potentially reducing response times for users. While these are some of the most popular load-balancing strategies, there are other algorithms that also address specific needs and challenges. Choosing the right algorithm is very important to ensuring your application remains scalable, reliable, and efficient. 💬 What other algorithms would you add? 💭

As grid operators and planners deal with a wave of new large loads on a resource-constrained grid, we need fresh approaches beyond just expecting reduced electricity use under stress (e.g. via recent PJM flexible load forecast or via Texas SB 6). While strategic curtailment has become a popular talking point for connecting large loads more quickly and at lower cost, this overlooks a more flexible, grid-supportive strategy for large load operators. Especially for loads that cannot tolerate any load curtailment risk (like certain #datacenters), co-locating #battery #energy storage systems (BESS) in front of the load merits serious consideration. This shifts the paradigm from “reduce load at utility’s command” to “self-manage flexibility.” It’s BYOB – Bring Your Own Battery and put it in front of the load. Studies have shown that if a large load agrees to occasional grid-triggered curtailment, this unlocks more interconnection capacity within our current grid infrastructure. But a BYOB approach can unlock value without the compromise of curtailment, essentially allowing a load to meet grid flexibility obligations while staying online. Why do this? For data centers (DC’s), it’s about speed to market and enhanced reliability. The avoidance of network upgrade delays and costs, along with the value of reliability, in many cases will justify the BESS expense. The BYOB approach decouples flexibility from curtailment risk with #energystorage. Other benefits of BYOB include: -Increasing the feasible number of interconnection locations. -Controlling coincident peak costs, demand charges, and real-time price spikes. -Turning new large loads into #grid assets by improving load shape and adding the ability to provide ancillary services. No solution is perfect. Some of the challenges with the BYOB approach include: -The load developer bears the additional capital and operational cost of the BESS. -Added complexity: Integrating a BESS with the grid on one side and a microgrid on the other is more complex than simply operating a FTM or BTM BESS. -Increased need for load coordination with grid operators to maintain grid reliability. The last point – large loads needing to coordinate with grid operators - is coming regardless. A recent NERC white paper shows how fast-growing, high intensity loads (like #AI, crypto, etc.) bring new #electricty reliability risks when there is no coordination. The changing load of a real DC shown in the figure below is a good example. With more DC loads coming online, operators would be severely challenged by multiple >400 MW loads ramping up or down with no advanced notice. BYOB’s can manage this issue while also dealing with the high frequency load variations seen in the second figure. References in comments.