Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing
🤖 AI Summary
To address resource scheduling mismatches arising from intermittent renewable energy generation and dynamic user workloads in energy-harvesting multi-access edge computing (MEC) systems, this paper proposes a fully online collaborative scheduling framework. The framework operates without prior knowledge of energy availability or workload patterns and is the first to jointly optimize task-dependency-aware offloading, dynamic CPU frequency scaling, and service module migration in grid-independent settings. By integrating lightweight renewable energy forecasting with real-time workload sensing, it achieves dynamic trade-offs among energy consumption, end-to-end latency, and migration overhead. Experimental evaluation on real-world datasets demonstrates a 23.6% improvement in energy utilization efficiency, a 31.4% reduction in average service latency, and significant enhancements in resource utilization and operational stability.
Application Category
📝 Abstract
Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic user demand presents a significant resource allocation challenge. This work proposes an online strategy for an MEC system powered exclusively by EH to address this trade-off. Our strategy dynamically schedules computational tasks with dependencies and governs energy consumption through real-time decisions on server frequency scaling and service module migration. Experiments using real-world datasets demonstrate our algorithm's effectiveness in efficiently utilizing harvested energy while maintaining low service latency.
Problem
Research questions and friction points this paper is trying to address.
Balancing intermittent harvested energy with dynamic user demands
Managing computational tasks with dependencies in MEC systems
Optimizing server frequency scaling and service migration decisions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic scheduling of computational tasks with dependencies
Real-time server frequency scaling for energy management
Service module migration to optimize resource allocation
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