As AI coding tools become more sophisticated, engineers at leading AI companies are stopping writing code altogether ...

Abstract: Load balancers are critical infrastructure in modern distributed systems, and their main function is to evenly distribute client traffic to multiple servers for high availability and ...

School of Computer Science, Rocket Force University of Engineering, Xi'an, Shaanxi, China Load imbalance is a major performance bottleneck in training mixture-of-experts (MoE) models, as unbalanced ...

This project simulates a Round Robin load balancer in Python using Flask, distributing incoming requests to 6 backend servers. Each server is a standalone Flask app, and a central load balancer (also ...

For users, few things are more frustrating than encountering unavailable services or unexpected downtime. Load balancing significantly reduces these occurrences through its built-in redundancy and ...

router for data-driven, workload-aware scheduling. Our router distributes queries across LLM instances by using a trainable responselength predictor and a novel formulation for estimating the impact ...

Abstract: This paper presents a model-agnostic predictive smart load balancer for microservices environments, utilizing machine learning to optimize load distribution and resource utilization. The ...

Arista adds cluster load balancing to its flagship operating system and AI job management capabilities to its CloudVision network observability platform. Arista Networks has added load balancing and ...