Minimization of VANET execution time based on joint task offloading and resource allocation

Article Computer Science, Information Systems

QoS-aware resource allocation in mobile edge computing networks: Using intelligent offloading and caching strategy

Mohammad Jalilvand Aghdam Bonab et al.

Summary: This research investigates connection management in multi-access edge computing systems, aiming to improve energy efficiency and network throughput using intelligent offloading and caching strategy.

PEER-TO-PEER NETWORKING AND APPLICATIONS (2022)

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Article Engineering, Electrical & Electronic

Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-Based Collaborative Edge Computing

Lin Tan et al.

Summary: Mobile edge computing (MEC) is an emergent architecture that brings computation and storage resources to the edge of the mobile network, providing rich services and applications for end users. This paper investigates the problem of task offloading and resource allocation in a multi-user collaborative MEC network based on OFDMA. A two-level alternation method framework is proposed to solve the problem, with a heuristic algorithm used in the upper level and deep reinforcement learning in the lower level. Simulation results demonstrate excellent performance in energy efficiency and task completion rate for different network parameter settings.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2022)

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Article Computer Science, Hardware & Architecture

Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach

Sadoon Azizi et al.

Summary: This paper addresses the challenge of effectively deploying fog computing resources for executing heterogeneous and delay-sensitive IoT tasks by mathematically formulating the task scheduling problem and proposing two semi-greedy algorithms. The proposed algorithms improve the percentage of tasks meeting their deadline requirement and reduce the total deadline violation time compared to existing algorithms, while optimizing the energy consumption of fog resources and makespan of the system.

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2022)

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Article Computer Science, Information Systems

Revenue and Energy Efficiency-Driven Delay-Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach

Xinyu Huang et al.

Summary: This article proposes a joint task type and vehicle speed-aware task offloading and resource allocation strategy for in-vehicle applications. By establishing a delay constraint model and calculating the delay, energy cost, and revenue for task execution, the utility level of the vehicles is maximized. The JORA-MADDPG algorithm is proposed to obtain a near-optimal solution and simulation results show superior performance.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Computer Science, Hardware & Architecture

Computation offloading and content caching and delivery in Vehicular Edge Network: A survey

Rudzidatul Akmam Dziyauddin et al.

Summary: The paper discusses the adoption of Cloud Computing in automotive industries, the challenges of end-to-end latency, and the consideration of computing capability at the edge of vehicular network. It outlines the aspects of Vehicular Edge Network, particularly Vehicular Edge Computing, and reviews existing approaches in solving ComOf and CachDel problems. Security aspects in ComOf and CachDel are also critically discussed, along with key challenges, open issues, and future works in VEC.

COMPUTER NETWORKS (2021)

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Article Engineering, Electrical & Electronic

Energy-Efficient D2D-Assisted Computation Offloading in NOMA-Enabled Cognitive Networks

Yuxia Cheng et al.

Summary: The paper proposes a D2D-assisted computation offloading scheme for NOMA-enabled CRNs, optimizing offloading decision and power control of PU and SU to minimize energy consumption while meeting task deadline and maximum transmit power constraints. The solution is obtained using block coordinate descent method and successive convex approximation, showing improvement in energy consumption and computing performance compared to other methods according to simulation results.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Article Computer Science, Information Systems

Joint Resource Allocation for Device-to-Device Communication Assisted Fog Computing

Changyan Yi et al.

Summary: This paper examines joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing, presenting a complex resource optimization problem and its solution.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2021)

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Article Computer Science, Information Systems

Computing tasks assignment optimization among edge computing servers via SDN

Chao Bu et al.

Summary: Edge computing, as an extension of cloud computing, has become important for dealing with novel service scenarios in the Internet of Everything (IoE) under 5G. This paper introduces the novel networking idea of Software Defined Network (SDN) into the edge computing pattern to optimize task assignment, minimize task processing delay, and improve task assignment efficiency.

PEER-TO-PEER NETWORKING AND APPLICATIONS (2021)

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Article Computer Science, Information Systems

Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization

Jing Bi et al.

Summary: This work proposes a partial computation offloading method to minimize total energy consumption by jointly optimizing task offloading ratio, CPU speeds, bandwidth allocation, and transmission power. The hybrid metaheuristic algorithm GSP achieves joint optimization of computation offloading between cloud data centers and the edge, showing lower energy consumption and faster convergence compared to typical peers in real-life based experiments.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Telecommunications

Delay-Aware and Energy-Efficient Computation Offloading in Mobile-Edge Computing Using Deep Reinforcement Learning

Laha Ale et al.

Summary: This study investigates computation offloading in a dynamic MEC system with multiple edge servers, where tasks with various requirements are offloaded to MEC servers in a time-varying operating environment. The objective is to maximize completed tasks before deadlines and minimize energy consumption. The proposed end-to-end Deep Reinforcement Learning approach outperforms existing methods.

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (2021)

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Proceedings Paper Engineering, Electrical & Electronic

Joint Offloading Decision and Resource Allocation in MEC-enabled Vehicular Networks

Lintao Zhang et al.

Summary: This paper explores computation offloading and resource allocation in vehicular networks based on OFDMA, considering the impact of imperfect channel state information. A mixed integer nonlinear programming is formulated to minimize offloading latency, and the problem is divided into offloading decision and resource allocation subproblems to tackle the NP-hard issue. Numerical results show that the proposed scheme can significantly reduce offloading latency through a combination of different algorithms and optimization methods.

2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING) (2021)

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Article Computer Science, Hardware & Architecture

A Software-Defined-Networking-Enabled Approach for Edge-Cloud Computing in the Internet of Things

Minghui Dai et al.

Summary: The proliferation of smart devices in IoT has led to a large amount of data transmission, posing challenges for providing secure and intelligent services due to resource-limited devices. A novel SDN-enabled architecture, integrating cloud computing, edge computing, and IoT networks, along with blockchain and reinforcement learning techniques, is proposed to address these challenges and support secure and intelligent services in IoT.

IEEE NETWORK (2021)

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Article Computer Science, Information Systems

A Multi-User Mobile Computation Offloading and Transmission Scheduling Mechanism for Delay-Sensitive Applications

Changyan Yi et al.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2020)

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Article Engineering, Electrical & Electronic

Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

Fuhui Zhou et al.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2020)

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Article Engineering, Electrical & Electronic

Priority, network and energy-aware placement of IoT-based application services in fog-cloud environments

Hiwa Omer Hassan et al.

IET COMMUNICATIONS (2020)

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Article Computer Science, Information Systems

Joint Resource Allocation and Computation Offloading in Mobile Edge Computing for SDN based Wireless Networks

Nahida Kiran et al.

JOURNAL OF COMMUNICATIONS AND NETWORKS (2020)

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Article Engineering, Electrical & Electronic

Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing

Wanli Wen et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

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Article Engineering, Electrical & Electronic

Joint Optimization Strategy of Computation Offloading and Resource Allocation in Multi-Access Edge Computing Environment

Huilin Li et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

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Article Computer Science, Information Systems

Distributed task offloading strategy to low load base stations in mobile edge computing environment

Yihong Li et al.

COMPUTER COMMUNICATIONS (2020)

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Article Computer Science, Information Systems

Computation Offloading and Resource Allocation for the Internet of Things in Energy-Constrained MEC-Enabled HetNets

Liangrui Tang et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Virtual Machine Migration Planning in Software-Defined Networks

Huandong Wang et al.

IEEE TRANSACTIONS ON CLOUD COMPUTING (2019)

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Article Engineering, Electrical & Electronic

Cooperative Task Offloading in Three-Tier Mobile Computing Networks: An ADMM Framework

Yue Wang et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

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Article Engineering, Electrical & Electronic

Detour: Dynamic Task Offloading in Software-Defined Fog for IoT Applications

Sudip Misra et al.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2019)

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Article Computer Science, Information Systems

Energy-Efficient Cooperative Resource Allocation in Wireless Powered Mobile Edge Computing

Luyue Ji et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

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Article Computer Science, Information Systems

Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing

Xiaowen Cao et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

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Article Computer Science, Information Systems

Task Execution Cost Minimization-Based Joint Computation Offloading and Resource Allocation for Cellular D2D MEC Systems

Rong Chai et al.

IEEE SYSTEMS JOURNAL (2019)

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Article Engineering, Electrical & Electronic

Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing

Xinchen Lyu et al.

IEEE TRANSACTIONS ON COMMUNICATIONS (2018)

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Article Engineering, Electrical & Electronic

Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G

Lichao Yang et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2018)

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Article Engineering, Electrical & Electronic

Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems

Feng Wang et al.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2018)

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Article Computer Science, Information Systems

Price-Based Distributed Offloading for Mobile-Edge Computing With Computation Capacity Constraints

Mengyu Liu et al.

IEEE WIRELESS COMMUNICATIONS LETTERS (2018)

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Article Computer Science, Information Systems

A Survey on Mobile Edge Computing: The Communication Perspective

Yuyi Mao et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2017)

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Article Computer Science, Information Systems

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Pavel Mach et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2017)

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Article Computer Science, Artificial Intelligence

Biogeography-Based Optimization

Dan Simon

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2008)

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Minimization of VANET execution time based on joint task offloading and resource allocation

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