A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management

2025-11-20

AI Summary

Traditional cloud-edge collaborative resource management relies on static thresholds and reactive mechanisms, leading to resource wastage or performance degradation. To address this, this paper proposes a proactive resource management framework integrating time-series forecasting with multi-agent deep reinforcement learning (MADRL). Specifically, CNN-LSTM forecasting outputs are embedded into the MADRL state space, endowing scheduling policies with foresight. Each agent jointly optimizes computation offloading, elastic scaling, and load balancing to achieve a multi-objective trade-off among cost, latency, and system stability. Experimental results demonstrate that the proposed method reduces average resource expenditure by 18.7% and improves task completion rate by 23.4% compared to baseline approaches, while significantly enhancing system robustness under dynamic workloads.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Old cloud edge workload resource management is too reactive. The problem with relying on static thresholds is that we are either overspending for more resources than needed or have reduced performance because of their lack. This is why we work on proactive solutions. A framework developed for it stops reacting to the problems but starts expecting them. We design a hybrid architecture, combining two powerful tools: the CNN LSTM model for time series forecasting and an orchestrator based on multi agent Deep Reinforcement Learning In fact the novelty is in how we combine them as we embed the predictive forecast from the CNN LSTM directly into the DRL agent state space. That is what makes the AI manager smarter it sees the future, which allows it to make better decisions about a long term plan for where to run tasks That means finding that sweet spot between how much money is saved while keeping the system healthy and apps fast for users That is we have given it eyes in order to see down the road so that it does not have to lurch from one problem to another it finds a smooth path forward Our tests show our system easily beats the old methods It is great at solving tough problems like making complex decisions and juggling multiple goals at once like being cheap fast and reliable

Problem

• Developing a hybrid proactive framework to replace reactive edge cloud resource management
• Optimizing resource allocation to balance cost savings with performance and reliability
• Integrating predictive forecasting with deep reinforcement learning for long-term planning

Innovation

• Hybrid proactive framework combining CNN-LSTM and DRL
• Embedding predictive forecasts into DRL agent state space
• Multi-agent DRL orchestrator for long-term resource optimization