Ming Chen

Ming Chen, Muhammed Tawfiqul Islam, Maria Rodriguez Read et al.

This paper presents iDynamics, a configurable emulation framework that exposes these dynamics as controllable experimental factors while running real microservice code on a Kubernetes-based cloud-edge cluster. iDynamics comprises three modular components. The Graph Dynamics Analyzer reconstructs application call graphs from service-mesh telemetry and quantifies bidirectional traffic between upstream-downstream microservice pairs. The Networking Dynamics Manager injects and measures realistic cross-node delay and bandwidth patterns via Linux traffic control primitives and distributed agents. The Scheduling Policy Extender offers a pluggable interface and utility library for implementing and evaluating arbitrary scheduling policies, expressed as pod placement and migration strategies. We use iDynamics to implement two representative policies -- a call-graph-aware policy and a hybrid policy that jointly considers traffic and latency -- as case studies demonstrating how the framework can be used to study SLA compliance under dynamic conditions. Experiments on a real cloud-edge cluster, running the DeathStarBench Social Network microservices, show that iDynamics can accurately emulate targeted network conditions, generate diverse call-graph and traffic patterns, and help quantify how different scheduling policies mitigate SLA violations under controllable and repeatable dynamics.

Xinyue Peng, Yanming Liu, Yihan Cang et al.

Solving non-convex resource allocation problems poses significant challenges in wireless communication systems, often beyond the capability of traditional optimization techniques. To address this issue, we propose LLM-OptiRA, the first framework that leverages large language models (LLMs) to automatically detect and transform non-convex components into solvable forms, enabling fully automated resolution of non-convex resource allocation problems in wireless communication systems. LLM-OptiRA not only simplifies problem-solving by reducing reliance on expert knowledge, but also integrates error correction and feasibility validation mechanisms to ensure robustness. Experimental results show that LLM-OptiRA achieves an execution rate of 96% and a success rate of 80% on GPT-4, significantly outperforming baseline approaches in complex optimization tasks across diverse scenarios.