Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices

Abstract

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm, namely, the Lyapunov optimization-based dynamic computation offloading (LODCO) algorithm is proposed, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of the computation task request, the wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

33 pages, 11 figures, submitted to IEEE Journal on Selected Areas in Communications

References in corpus (4)

• Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems
• Distributed Real-Time Power Balancing in Renewable-Integrated Power Grids with Storage and Flexible Loads
• Grid Energy Consumption and QoS Tradeoff in Hybrid Energy Supply Wireless Networks
• ARQ with Adaptive Feedback for Energy Harvesting Receivers

Cited by in corpus (72)

• Mobile Edge Computing: A Survey on Architecture and Computation Offloading
• Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
• A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
• Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems
• Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization
• UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization
• Neuro-memristive Circuits for Edge Computing: A review
• Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
• Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems
• Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach
• NOMA-aided Joint Communication, Sensing, and Multi-tier Computing Systems
• Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration
• Edge Federation: Towards an Integrated Service Provisioning Model
• Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
• Data Age Aware Scheduling for Wireless Powered Mobile-Edge Computing in Industrial Internet of Things
• Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things
• Energy Efficient Federated Learning Over Wireless Communication Networks
• Intelligent Reflecting Surface Meets Mobile Edge Computing: Enhancing Wireless Communications for Computation Offloading
• Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing
• Graph Federated Learning for CIoT Devices in Smart Home Applications
• Mobile Edge Intelligence and Computing for the Internet of Vehicles
• A Joint Communication and Computation Design for Semantic Wireless Communication with Probability Graph
• Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
• Peer Offloading in Mobile Edge Computing with Worst-Case Response Time Guarantees
• Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
• QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
• Optimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC
• EDOS: Edge Assisted Offloading System for Mobile Devices
• Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach
• Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing
• DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing
• Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey
• Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
• Resource Scheduling in Edge Computing: A Survey
• Multiuser Resource Allocation for Mobile-Edge Computation Offloading
• LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples
• Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading (Extended Version)
• Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
• Exploiting the Solar Energy Surplus for Edge Computing
• Energy-Efficient Resource Allocation for Multi-User Mobile Edge Computing
• Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications
• Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing
• Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks
• Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach
• DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
• Dynamic NOMA-Based Computation Offloading in Vehicular Platoons
• Bandwidth Gain from Mobile Edge Computing and Caching in Wireless Multicast Systems
• Analysis on Computation-Intensive Status Update in Mobile Edge Computing
• A Pre-Allocation Design for Cost Minimization and Delay Constraint in Vehicular Offloading System
• Towards Green Mobile Edge Computing Offloading Systems with Security Enhancement
• Stable Online Computation Offloading via Lyapunov-guided Deep Reinforcement Learning
• Offloading Optimization with Delay Distribution in the 3-tier Federated Cloud, Edge, and Fog Systems
• HyEdge: Optimal Request Scheduling in Hybrid Edge Computing Environment
• An Efficient Mechanism for Computation Offloading in Mobile-Edge Computing
• MDP-Based Scheduling Design for Mobile-Edge Computing Systems with Random User Arrival
• Dynamic Offloading Loading Optimization in distributed Fault Diagnosis system with Deep Reinforcement Learning Approach
• Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems
• Scheduling for Mobile Edge Computing with Random User Arrivals: An Approximate MDP and Reinforcement Learning Approach
• Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems
• Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI
• Cooperative Job Dispatching in Edge Computing Network with Unpredictable Uploading Delay
• Optimal Resource Allocation for Wireless Powered Mobile Edge Computing with Dynamic Task Arrivals
• Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control
• Modeling and Trade-off for Mobile Communication, Computing and Caching Networks
• Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks
• Delay Outage Probability of Multi-relay Selection for Mobile Relay Edge Computing Systems
• Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
• Computation Rate Maximization for Multiuser Mobile Edge Computing Systems With Dynamic Energy Arrivals
• Communications-Caching-Computing Tradeoff Analysis for Bidirectional Data Computation in Mobile Edge Networks
• Computation Offloading in Heterogeneous Mobile Edge Computing with Energy Harvesting
• Optimal Design of SWIPT-Aware Fog Computing Networks