Load Capacity Utilization Strategies

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. 

⚡ Why Two Factories Pay Different Electricity Bills — Even with the Same Energy Use 🤔 Here’s a surprising truth most overlook: 👉 It’s not just how much energy you use that determines your bill — 👉 It’s how efficiently you use it. 🎯 The secret? Load Factor. Think of Load Factor as your system’s energy discipline — smooth, consistent demand = lower costs and a healthier grid. 🧠 What Is Load Factor? ⤷ Load Factor (LF) measures how efficiently your system uses its installed capacity over time. ⤷ A higher LF = steadier usage → lower peak demand charges → optimized costs. 📐 Formula: Load Factor (LF) = Average Load ÷ Peak Load × 100% 🏭 Real-Life Example — Same Energy, Different Bills 🏭 Factory A ⤷ Peak Load: 50 kW ⤷ Operating Hours: 24 h/day × 10 days ⤷ Total Energy: 12,000 kWh ⤷ Average Load: 12,000 ÷ 240 = 50 kW ⤷ Load Factor: 50 ÷ 50 × 100 = 100% ✅ Steady usage, no spikes, lower costs. 🏭 Factory B ⤷ Peak Load: 200 kW ⤷ Operating Hours: 12 h/day × 10 days ⤷ Total Energy: 12,000 kWh ⤷ Average Load: 12,000 ÷ 120 = 100 kW ⤷ Load Factor: 100 ÷ 200 × 100 = 50% ⚠️ Same energy, but higher peak → higher demand charges → higher bills. ⚙️ Why Load Factor Is Crucial 💸 Cost Optimization → High LF reduces peak demand charges & spreads fixed costs. ⚡ Grid Reliability → Smoother loads = less stress on transformers & lines. 🌍 Sustainability → Less waste, more efficient energy use. 📈 Asset Utilization → Maximizes efficiency of transformers, generators & switchgear. 🧠 Pro Tips to Improve Load Factor ✔️ Shift flexible loads to off-peak hours. ✔️ Use energy storage or demand response. ✔️ Automate load management with smart controls. ✔️ Monitor load curves and address peaks early. 📢 A better Load Factor isn’t just an engineering KPI — it’s a direct path to lower costs, better reliability, and greener operations. 💬 Have you improved Load Factor in your facility or projects? Share your strategy or lessons learned below. 👇 ♻️ Repost to share with your network if you find this useful. 🔗 Follow Ashish Shorma Dipta for posts like this! #PowerSystems #EnergyEfficiency #LoadOptimization #LoadFactor

GPU utilization, on average, hovers around 20-40% for many organizations. What is the cause of the low utilization? GPU use cases for AI can be broken down into inference (40%) and training (60%). Training suffers less from low utilization, because it’s predictable. Organizations often implement central planning around how and when the GPUs are allocated for long-running jobs. As long as you are organized, utilization can be kept high. Inference, on the other hand, is unpredictable and suffers significantly from low utilization. The requests to the AI models come in bursts, so the capacity needs to be able to adjust dynamically according to unpredictable demand. However, this ‘dynamic adjustment’ causes low utilization because of ‘scaling latency’. Requesting more GPUs according to the demand takes time. Initializing the GPU node by downloading models and setting up the virtualization environment also takes time. All of this forces organizations to ‘over provision’ such that there are always excess GPUs available. For traditional workloads on the CPU, this dynamic adjustment is easier to handle because containers make it easy to run multiple workloads on a single machine, and each workload is lightweight (50MB-1GB) so it’s fast to initialize. For GPU workloads, this becomes much harder due to the large size of AI models and difficulty with virtualization. So what is the solution to low utilization? The first solution is fine-tuning. By using a smaller model that is fine-tuned to the specific use case the model is meant for, the startup time can be reduced. Another solution is hot-swapping AI models. Instead of building & scaling services individually per model, you can make an endpoint that serves multiple models & can switch between models very quickly. This can surprisingly save up to 40% in provisioning costs. There are lots of other optimizations that can be done to improve utilization; at Outerport we are working on comprehensive solutions at the systems level to solve this, starting with hot-swapping. What are some of your strategies for increasing GPU utilization?

🚨 Would you willingly 𝐟𝐚𝐢𝐥 𝐨𝐯𝐞𝐫 𝟓𝟎% of your traffic if it meant keeping your critical systems running at 𝟗𝟗.𝟒% 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 during a 𝟏𝟐𝐱 traffic spike? My Netflix colleagues revealed how we handle 12x traffic spikes by intelligently choosing which requests to drop! 🚀 Here's the inside scoop: 🎮 During a recent infrastructure outage, our Android devices hit us with a massive 12x spike in prefetch requests. Our response? We deliberately dropped non-essential requests while maintaining 99.4% availability for critical user playback! Here's how we make the magic happen: 🎯 𝐒𝐦𝐚𝐫𝐭 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: We categorize requests into critical (user-initiated) and non-critical (prefetch) traffic, ensuring users can always hit play when they want! ⚖️ 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐲-𝐁𝐚𝐬𝐞𝐝 𝐋𝐨𝐚𝐝 𝐒𝐡𝐞𝐝𝐝𝐢𝐧𝐠: Our system uses four priority levels (Critical, Degraded, Best Effort, Bulk) to dynamically allocate capacity, ensuring 100% throughput for critical requests while utilizing excess capacity for lower priority traffic. 💻 𝐂𝐏𝐔-𝐁𝐚𝐬𝐞𝐝 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Our system starts shedding low-priority requests when CPU utilization exceeds target thresholds, preserving resources for critical operations. 💾 𝐈𝐎-𝐁𝐚𝐬𝐞𝐝 𝐆𝐮𝐚𝐫𝐝𝐬: For IO-bound services, we've added latency-based shedding to protect backing services and datastores from overload. ⚠️ Dive into the full article to learn crucial anti-patterns: preventing congestive failure and avoiding shedding load too early or too late. These insights could save your system during the next traffic surge! https://lnkd.in/gy8YSsbP 🛠️ Want to try this yourself? Check out our open-source adaptive concurrency limiters at https://lnkd.in/gZ89ZsKF Big shoutout to Anirudh Mendiratta, Zeyu (Kevin) Wang, Joseph Lynch, Javier Fernandez-Ivern, and Benjamin Fedorka for sharing these insights 👏 💭 What keeps you up at night when thinking about handling unexpected traffic spikes? How do you prioritize requests in your system? #NetflixEngineering #SystemDesign #SoftwareArchitecture #Scalability #TechnicalLeadership #LoadShedding

How I Used Load Testing to Optimize a Client’s Cloud Infrastructure for Scalability and Cost Efficiency A client reached out with performance issues during traffic spikes—and their cloud bill was climbing fast. I ran a full load testing assessment using tools like Apache JMeter and Locust, simulating real-world user behavior across their infrastructure stack. Here’s what we uncovered: • Bottlenecks in the API Gateway and backend services • Underutilized auto-scaling groups not triggering effectively • Improper load distribution across availability zones • Excessive provisioned capacity in non-peak hours What I did next: • Tuned auto-scaling rules and thresholds • Enabled horizontal scaling for stateless services • Implemented caching and queueing strategies • Migrated certain services to serverless (FaaS) where feasible • Optimized infrastructure as code (IaC) for dynamic deployments Results? • 40% improvement in response time under peak load • 35% reduction in monthly cloud cost • A much more resilient and responsive infrastructure Load testing isn’t just about stress—it’s about strategy. If you’re unsure how your cloud setup handles real-world pressure, let’s simulate and optimize it. #CloudOptimization #LoadTesting #DevOps #JMeter #CloudPerformance #InfrastructureAsCode #CloudXpertize #AWS #Azure #GCP

All traffic isn’t equal. Treat it like it is, and your system will collapse under pressure. You can’t build a resilient system without a Load Balancer. It’s the invisible layer that keeps your services online, responsive, and scalable. What is Load Balancing? Load balancing is a core technique in computing that distributes incoming traffic across multiple servers or resources. It optimizes resource use, ensures uptime, improves performance, and prevents overloads. Why it matters: - High Availability - Fault Tolerance - Scalability - Performance - Security There’s no one-size-fits-all. Choosing the right strategy depends on traffic patterns, server capacity, and application needs. The following are some common Load Balancing Strategies: 𝗥𝗼𝘂𝗻𝗱 𝗥𝗼𝗯𝗶𝗻 Distributes requests to servers one by one in a circular sequence. Every server gets the same number of requests, regardless of capacity. ✅ Use Cases: - Environments with identical servers - Stateless apps or static content - Predictable, even workloads - Testing and simple setups ⚠️ Limitations: - Doesn’t consider server performance - Predictable pattern may raise security concerns - Poor with uneven or long-running requests - No session stickiness 𝗪𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝗥𝗼𝘂𝗻𝗱 𝗥𝗼𝗯𝗶𝗻 Extends Round Robin by assigning weights to servers. More powerful servers get more requests. ✅ Use Cases: - Servers with different capacities - Prioritizing certain workloads - Gradual traffic ramp-up on new servers ⚠️ Limitations: - Weights must be updated manually - Can’t react to real-time changes or failures 𝗟𝗲𝗮𝘀𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Sends traffic to the server with the fewest active connections. Adjusts dynamically to current traffic. ✅ Use Cases: - Apps with long-lived connections (e.g., streaming) - Traffic spikes and changing workloads - When the request load varies ⚠️ Limitations: - Assumes all connections are equal - Lacks built-in session stickiness 𝗪𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝗟𝗲𝗮𝘀𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Balances traffic using both active connections and server capacity (via weights). Requests go to the server with the lowest load-to-weight ratio. ✅ Use Cases: - Mixed-capacity server clusters - Resource-heavy or variable workloads - Cloud or hybrid deployments ⚠️ Limitations: - More CPU-intensive than basic strategies - Still relies on manually set weights - Setup is more complex Building something at scale? These choices matter more than you think. Pick the right one before traffic picks you apart. Follow Lahiru Liyanapathirana for more posts like this.

Want to slash your EC2 costs? Here are practical strategies to help you save more on cloud spend. Cost optimization of applications running on EC2 can be achieved through various strategies, depending on the type of applications and their usage patterns. For example, is the workload a customer-facing application with steady or fluctuating demand, or is it for batch processing or data analysis? It also depends on the environment, such as production or non-production, because workloads in non-production environments often don't need EC2 instances to run 24x7. With these considerations in mind, the following approaches can be applied for cost optimization: 1. Autoscaling: In a production environment with a workload that has known steady demand, a combination of EC2 Savings Plans for the baseline demand and Spot Instances for volatile traffic can be used, coupled with autoscaling and a load balancer. This approach leverages up to a 72% discount with Savings Plans for predictable usage, while Spot Instances offer even greater savings, with up to 90% savings for fluctuating traffic. Use Auto Scaling and Elastic Load Balancing to manage resources efficiently and scale down during off-peak hours. 2. Right Sizing: By analyzing the workload—such as one using only 50% memory and CPU on a c5 instance—you can downsize to a smaller, more cost-effective instance type, such as m4 or t3, significantly reducing costs. Additionally, in non-production environments, less powerful and cheaper instances can be used since performance requirements are lower compared to production. Apply rightsizing to ensure you're not over-provisioning resources, incurring unnecessary costs. Use AWS tools like AWS Cost Explorer, Compute Optimizer, or CloudWatch to monitor instance utilization (CPU, memory, network, and storage). This helps you identify whether you’re over-provisioned or under-provisioned. 3. Downscaling: Not all applications need to run 24x7. Workloads like batch processing, which typically run at night, can be scheduled to shut down during the day and restart when necessary, significantly saving costs. Similarly, workloads in test or dev environments don't need to be up and running 24x7; they can be turned off during weekends, further reducing costs. 4. Spot Instances: Fault-tolerant and interruptible workloads, such as batch processing, CI/CD, and data analysis, can be deployed on Spot Instances, offering up to 90% savings over On-Demand instances. Use Spot Instances for lower-priority environments such as DEV and Test, where interruptions are acceptable, to save costs significantly. Cost optimization is not a one-time activity but a continual process that requires constant monitoring and reviewing of workload and EC2 usage. By understanding how resources are being used, you can continually refine and improve cost efficiency. Love to hear your thoughts-what strategies have you used to optimize your EC2 costs?