Smart Load Allocation Algorithms

This is how companies like Google & Meta handle peak traffic and protect shared resources. It all comes down to implementing rate limiting that prevents services from crashing under massive user loads, and for this purpose, the Token Bucket Algorithm is quite commonly used. ► What is the Token Bucket Algorithm? A rate-limiting algorithm that controls how requests are processed by a system, ensuring that they don't exceed a specified limit. It’s used to manage traffic and prevent system overloads. ► How It Works: 1. Token Bucket Initialization:    - A bucket is created with a fixed capacity to hold tokens. 2. Token Generation:    - Tokens are added to the bucket at a constant rate (e.g., 1 token per second). 3. Request Arrival:    - When a request is made, the system checks the bucket for available tokens. 4. Token Consumption:    - If a token is available, it is removed from the bucket, and the request is processed. 5. Request Limitation:    - If the bucket is empty (no tokens available), the request is denied or delayed until new tokens are added. ►Large-Scale Application (e.g., Instagram, Facebook, Google):   - Instagram: When users engage with content by liking posts or commenting, the Token Bucket Algorithm ensures that these actions are spread out over time. This prevents spikes that could overwhelm servers, maintaining consistent performance.   - Google APIs: For services like Google Maps API, the algorithm limits the number of API calls a user can make in a given time frame. This protects the system from abuse and ensures fair resource allocation across millions of users.   - Meta (Facebook): During high-traffic events, such as live streaming or viral posts, the algorithm manages the request rate to ensure that servers remain responsive, avoiding downtime. ► Why It’s Crucial for Large-Scale Systems:   - Scalability: The Token Bucket Algorithm scales with user demand, handling millions of requests by evenly distributing load over time.   - Fair Usage: It ensures that all users have equitable access to system resources by limiting the request rate per user or client.   - Performance: By controlling the flow of requests, the algorithm prevents system overloads, ensuring that services remain fast and reliable even under heavy load. ► Real-World Example:  When you click "like" on Instagram, Your request is checked against the token bucket. If tokens are available, your like is processed immediately. If not, you might experience a slight delay, preventing the system from being overwhelmed by too many likes at once.

Monta's new technical white paper on Load Management, got my inner electrical engineer buzzing! 🤓 We model the entire site as a tree of Load Balancing Groups, each node defined by a per-phase current vector I = (I_L1, I_L2, I_L3). This is a proper graph-based abstraction of the electrical topology. What’s impressive is the dynamic current allocation engine. This isn’t naive load sharing. It’s real-time, phase-aware rebalancing with priority ranking, driven by live MeterValues. When an EV draws less than allocated, the system reclaims and redistributes the excess—amp by amp. And yes, the 6A floor is baked in to maintain IEC 61851-1 compliance and avoid charging session failures on low-capacity branches. It supports AC, DC, and mixed environments with a unified logic layer. Whether it’s single-phase chargers or beefy three-phase DC stations, the system adapts allocation dynamically based on hardware capability, site constraints, and configured priorities. It already integrates with some external meters, but there is a lot more to come. During Q3, we will open APIs and MQTT streams, adding many more options. We will also combine smart charging and load balancing on large sites 🤯 This is the kind of system design that shifts the ROI equation—from upgrading infrastructure to orchestrating it smarter. Seriously worth a read if you’re into grid-constrained EV charging, real-time control systems, or the future of distributed energy logic. Link in comments

“Joint scheduling of cascading reservoir schemes … “ In complex cascade reservoir schemes, hydropower scheduling has become increasingly challenging as uncertainties across many operational facets (e.g., inflow hydrology, peak demands, shared beneficial water uses, operational constraints, etc.) continue to grow.  In fact, many traditional scheduling models, find themselves increasingly struggling to meet demand needs based on older scheduling schemes. In a recent study, a deep reinforcement learning approach was proposed to improve the accuracy and efficiency of optimal load allocation and flood management.  The Pubugou-Shenxigou-Zhentouba cascade hydropower reservoir system in the Dadu River basin in China was used as the case study. In this approach, the scheduling optimization problem is first transformed into a model-free multi-step decision problem based on the Markov decision process.  The Soft Actor-Critic algorithm was then combined with the Evolutionary Hindsight Experience Replay sampling framework to “learn” the relationship between scheduling policies and various power station states. Results from multi-objective scheduling demonstrate that the proposed deep reinforcement learning approach has the ability to enable precise scheduling of a cascade hydropower reservoir system, achieving a total load deviation rate of no more than 3% (in this study, through 300 Monte Carlo simulations). For complete study details, please see Luo et al. (2025) in Journal of Hydrology, “A deep reinforcement learning approach for joint scheduling of cascade reservoir system”