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Energy-Efficient computation offloading in Mobile Edge Computing Systems With Uncertainties

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第8期21卷 5717-5729页

作者： Ji, Tianxi Luo, Changqing Yu, Lixing Wang, Qianlong Chen, Siheng Thapa, Arun Li, Pan Case Western Reserve Univ Dept Elect Comp & Syst Engn Cleveland OH 44106 USA Virginia Commonwealth Univ Dept Comp Sci Med Coll Virginia Campus Richmond VA 23284 USA Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Tuskegee Univ Dept Elect Engn Tuskegee AL 36088 USA

computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an c-bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than (1 + epsilon) of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.

关键词： Mobile edge computing computation offloading network uncertainties energy efficiency

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Energy harvesting computation offloading game towards minimizing delay for mobile edge computing

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COMPUTER NETWORKS 2022年第0期204卷 108678-108678页

作者： Guo, Mian Li, Qirui Peng, Zhiping Liu, Xiushan Cui, Delong Guangdong Polytech Normal Univ Guangzhou Peoples R China Guangzhou Univ Guangzhou Guangdong Peoples R China Jiangmen Polytech Jiangmen Guangdong Peoples R China Guangdong Univ Petrochem Technol Maoming Guangdong Peoples R China

Mobile edge computing (MEC) has emerged for meeting the ever-increasing computation demands from mobile applications. Mobile users endowing with computation and energy harvesting (EH) capabilities, called EH devices, are desired in MEC systems. With computation capability, EH devices can switch between local computing and mobile-edge computing for better QoEs. With EH technologies, mobile users could survive perpetually for supporting long-term task processing. However, the heterogeneous computation resource among EH devices and edge servers, limited wireless resource as well as time-constrained harvestable energy challenge the computation offloading. This paper investigates a green MEC with EH devices and develops an effective EH computation offloading scheme towards minimizing delay for green MEC. We formulate and analyze the EH computation offloading problem with game theory. Then, an EH computation offloading game scheme, including the Lyapunov drift-based energy harvesting and computation offloading best response algorithms, is designed to find out optimal energy harvesting and computation offloading solutions to address the problem. The simulation results have been conducted to demonstrate the efficiency of the proposal.

关键词： Mobile edge computing computation offloading Energy harvesting

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Energy-Minimized Partial computation offloading for Delay-Sensitive Applications in Heterogeneous Edge Networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2022年第4期10卷 1941-1954页

作者： Bi, Jing Yuan, Haitao Zhang, Kaiyi Zhou, MengChu Beijing Univ Technol Fac Informat Technol Sch Software Engn Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100190 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA St Petersburg State Marine Tech Univ Dept Cyber Phys Syst Lotsmanskaya St 3 St Petersburg 198262 Russia

Mobile Devices (MDs) support various delay-sensitive and computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed to provide additional computation, storage, and networking resources for MDs. Servers in MEC are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. To tackle these challenges, this work proposes a novel computation offloading approach for delay-sensitive applications with multiple separable tasks in hybrid networks including MDs, SBSs, and an MBS. To achieve it, this work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called Particle swarm optimization based on Genetic Learning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs, and MBS, users' connection to SBSs, MDs' CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption of an entire system.

关键词： computation offloading mobile edge computing energy optimization resource allocation particle swarm optimization genetic algorithm

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Dynamic Task Software Caching-Assisted computation offloading for Multi-Access Edge Computing

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第10期70卷 6950-6965页

作者： Chen, Zhixiong Yi, Wenqiang Alam, Atm S. Nallanathan, Arumugam Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

关键词： Task analysis Software Servers Energy consumption Delays Resource management Wireless communication computation offloading deep reinforcement learning game theory multi-access edge computing software caching

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Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

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PERVASIVE AND MOBILE COMPUTING 2022年 85卷

作者： Kok, Ibrahim Ozdemir, Suat Pamukkale Univ Dept Comp Engn Denizli Turkey Hacettepe Univ Dept Artificial Intelligence Engn Ankara Turkey

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Next Generation Internet of Things (NGIoT) Deep reinforcement learning FogOrch Fog computing Data offloading computation offloading

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Caching-Enabled computation offloading in Multi-Region MEC Network via Deep Reinforcement Learning

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IEEE INTERNET OF THINGS JOURNAL 2022年第21期9卷 21086-21098页

作者： Yang, Song Liu, Jintian Zhang, Fei Li, Fan Chen, Xu Fu, Xiaoming Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Acad Mil Sci Unmanned Syst Res Ctr Natl Inst Def Technol Innovat Beijing 100071 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Gottingen Inst Comp Sci D-37077 Gottingen Germany

With the rapid development of the Internet, more and more computing-intensive applications with high requirements on computing delay and energy consumption have emerged. Recently, the use of mobile-edge computing servers for auxiliary computing is considered as an effective way to reduce latency and energy consumption. In addition, applications such as autonomous driving will generate a large number of repetitive tasks. Using a cache to store the computational results of popular tasks can avoid the overhead caused by repetitive processing. In this article, we study the problem of computation offloading for users in multiple regions. The optimization goal is expressed as choosing offloading strategies and caching strategies to minimize the total delay and energy consumption of all regions. We first use the deep reinforcement learning (DRL) deep deterministic policy gradient (DDPG) framework to solve the problem of computational offloading in a single region. We also show the inefficiency of existing collaborative caching approaches in multiple regions, and propose a new collaborative caching algorithm (CCA) to improve the overall cache hit rate of the system. Finally, we integrate the DDPG and CCA algorithms to form a holistic efficient caching and offloading strategy for all regions. The simulation results show that the proposed algorithm can significantly improve the cache hit rate, and has an excellent performance in reducing the total system overhead.

关键词： Task analysis Cloud computing Reinforcement learning Servers Resource management Delays computational modeling Collaborative caching computation offloading deep reinforcement learning (DRL) edge computing user migration

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An Accurate Model for computation offloading in 6G Networks and a HAPS-Based Case Study

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 1963-1977页

作者： Ovatman, Tolga Kurt, Gunes Karabulut Yanikomeroglu, Halim Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey Poly Grames Res Ctr Dept Elect Engn Montreal PQ H3T 1J4 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

The undeniable potential of computation offloading has been attracting attention from researchers for more than a decade. With advances in multi-access edge computing (MEC), computation offloading has become a more critical issue because of the heterogeneity in the computational power of edge devices and the elevated importance of extending their lifespan. Due to the apparent advantages, the use of MEC in 6G networks, where a vertical heterogeneous network composed of space, air, and ground networks is only natural. The non-terrestrial networking elements constitute effective computational resources. However, recent research investigating the potential of computational offloading in 6G networks has involved models that do not adequately reflect the complexity of the underlying processes. In this study, we propose a realistic computation model for 6G networks that considers crucial properties of the offloaded job, including the inter-dependency of the job tasks and the decomposability of the job. Our model is based on the mature application domain of MEC, where proven solutions are already studied. We also investigate the potential of a high altitude platform station (HAPS)-aided MEC platform using this model. The proposed model allows us to design offloading strategies to enable adaptive computational offloading. Through numerical analyses, we show that the proposed model provides sufficient insight to reduce the total processing time significantly.

关键词： Task analysis computational modeling 6G mobile communication Adaptation models Numerical models Atmospheric modeling Satellites computation offloading high altitude platform station multi-access edge computing

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Zero-Setup computation offloading to Heterogeneous Volunteer Devices Using Web Browsers 25

Zero-Setup Computation Offloading to Heterogeneous Volunteer...

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25th ACM International Middleware Conference Demos, Posters and Doctoral Symposium, MIDDLEWARE 2024

作者： Semjonov, Anton Edinger, Janick University of Hamburg Hamburg Germany

ISBN: (纸本)9798400713545

In the evolving landscape of distributed computing frameworks, Wasimoff emerges as an innovative middleware with a browser-based execution environment designed to facilitate computation offloading to heterogeneous volunteer devices. By leveraging WebAssembly as an executable format, Wasimoff allows users to seamlessly contribute computational resources through a simple web interface, thereby eliminating complex setup requirements. This demonstration presents the zero-setup functionality of the Wasimoff provider, and showcases the ease of connecting personal devices to a dynamic resource pool, highlighting the scaling efficiency and multitasking capabilities of the framework. By enabling spontaneous, barrier-free participation, Wasimoff transforms idle device capacity into a collaborative computational resource. © 2024 Copyright held by the owner/author(s).

关键词： computation offloading

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Latency-aware Design for computation offloading and Content Caching in F-RANs 35

Latency-aware Design for Computation Offloading and Content ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Zhang, Xuewei Yan, Hongkun Ren, Yuan Jiang, Fan Wang, Junxuan Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9798350362244

This paper investigates the joint computation offloading and content caching scheme in fog radio access networks (F-RANs), where users and fog nodes (FNs) are all endowed with caching and computing capabilities. The Kuhn-Munkres (KM) algorithm is first used to make the user-and-FN matching decisions. We then comprehensively reveal the service modes when a user receives the requested data to perform a computation task. The optimization problem of minimizing the task response latency is formulated under the constraints of limited cache sizes for users and FNs, aiming to yield the optimal decisions for computation offloading and content caching. The formulated problem is NPhard, and transformed into the convex form by introducing the methods of difference of convex (DC) function, successive convex approximation (SCA), and arithmetic geometric mean (AGM) inequality. Simulation results demonstrate the fast convergence speed of the proposed algorithm, and show superior latency performance of our designed scheme to the benchmarks. © 2024 IEEE.

关键词： computation offloading

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Partial computation offloading in Parked Vehicle-Assisted Multi-Access Edge Computing: A Game-Theoretic Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 10220-10225页

作者： Pham, Xuan-Qui Huynh-The, Thien Huh, Eui-Nam Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Dept IT Convergence Gumi 39177 South Korea Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea

Parked vehicle-assisted multi-access edge computing (PVMEC) is a paradigm that exploits the under-utilized resources of parked vehicles (PVs) to assist MEC servers for offloaded task execution. This article investigates a partial offloading strategy for multi-user PVMEC, where each mobile device (MD)'s task can be partially offloaded to the MEC server or a nearby PV. We formulate the system utility maximization problem with joint consideration of offloading decisions, offloading ratio, and resource allocation. Considering the complexity and privacy issues of centralized scheme as well as the inherent resource contention among MDs, we adopt game-theoretic approach to devise a low-complexity distributed offloading scheme, in which the offloading decision-making problem is modeled as an exact potential game, and the optimal offloading ratio and resource allocation are determined using a subgradient method. Finally, simulation results show that our proposed approach can significantly improve the system utility over traditional baselines.

关键词： Servers Task analysis Games Resource management Costs Multi-access edge computing Heuristic algorithms computation offloading multi-access edge computing resource allocation vehicular network

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