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Optimizing Energy-Latency Tradeoff for computation offloading in SDIN-Enabled MEC-based IIoT

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第12期16卷 4081-4098页

作者： Zhang, Xinchang Xia, Changsen Ma, Tinghuai Zhang, Lejun Jin, Zilong Nanjing Univ Informat Sci & Technol Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Software Nanjing Peoples R China Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices ( EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

关键词： SDIN Edge computing computation offloading Evolution computation

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Learn to Coordinate for computation offloading and Resource Allocation in Edge Computing: A Rational-Based Distributed Approach

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第5期9卷 3136-3151页

作者： Liu, Zhicheng Zhao, Yunfeng Song, Jinduo Qiu, Chao Chen, Xu Wang, Xiaofei Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

In response to the growing demand for user experience on innovative applications, computation offloading can migrate computation-intensive tasks from users to the network edge, which is closely coupled with resource allocation. For the rational motivations behind these issues, centralized decision-making may seriously compromise individuals' rationality, and the complexity of this problem is gradually beyond the comfort zone of traditional methods. Therefore, realizing decentralized and intelligent resource management becomes an emerging technical issue in edge computing, which has not been much explored. This paper proposes a learning-based distributed resource coordination framework, L6C, which enables each decision-maker to pursue its benefit in a socializing and rational manner. We first formulate a two-timescale problem of computation offloading and resource allocation, then exploit game theory to discuss the rational properties of users and edges. Specifically, each user wants to minimize the execution cost of its task, and edges try to maximize the experience of task execution cooperatively. Further, we design two explicit information interaction mechanisms based on multi-agent deep reinforcement learning, where interactive contents can be generated dynamically along with resource decisions. Experimental results on a real-world dataset show that L6C achieves the superior performance of edges and users compared with various baselines.

关键词： Resource management Edge computing Task analysis Game theory computational modeling Cloud computing Reinforcement learning computation offloading resource allocation edge computing multi-agent deep reinforcement learning

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A survey on computation offloading and service placement in fog computing-based IoT

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JOURNAL OF SUPERCOMPUTING 2022年第2期78卷 1983-2014页

作者： Gasmi, Kaouther Dilek, Selma Tosun, Suleyman Ozdemir, Suat Higher Inst Appl Languages & Comp Sci Beja Tunis Tunisia Hacettepe Univ Grad Sch Sci & Engn Ankara Turkey Hacettepe Univ Dept Comp Engn Ankara Turkey

In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to the network edge. This enables IoT user equipment (UE) to profit from the fog computing paradigm by mainly offloading their intensive computation tasks to fog resources. Thus, computation offloading and service placement mechanisms can overcome the resource constraints of IoT devices, and improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay. In this paper, we survey the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner.

关键词： Fog computing computation offloading Internet of Things (IoT) Service placement Optimization algorithms

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A survey of research on computation offloading in mobile cloud computing

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WIRELESS NETWORKS 2022年第4期28卷 1563-1585页

作者： Jin, Xiaomin Hua, Wenqiang Wang, Zhongmin Chen, Yanping Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China

Mobile devices (MDs), represented by smartphones, have been widely used in various industries. However, MDs are constrained by their limited resources and cannot execute computation-intensive applications. Mobile cloud computing (MCC), which provides MDs with a rich pool of resources that can be accessed through wireless networks, is proposed to extend MDs' capacity by computation offloading. MCC helps MDs breakthrough their resource constraints, frees them from heavy local workloads, and allows them to take more responsibility for connecting mobile users (MUs) and the information domain. MCC computation offloading has attracted wide attention because of its tremendous potential, and a lot of related research has been done. In this paper, we provide a survey of the research on computation offloading in MCC so that readers can spend less time to have a comprehensive understanding of this field, and know its key technologies and future directions. We first summarize the MCC architecture and offloading granularity, which are the most fundamental concepts of MCC. The computation offloading system is decomposed into three basic components: MUs, application service operators, and cloud operators. We then conducted a comprehensive literature review on offloading decision, admission control, resource management, and edge equipment deployment, which are four key technologies for these components. Wireless network connection and heterogeneity are the basic features of MCC, which increase the possibility of failure and privacy leakage during the computation offloading process. We also review the auxiliary technologies for computation offloading in terms of fault tolerance and privacy protection. Finally, we present the research outlook of the systematic prototype and other technologies from the perspective of "device-pipe-cloud".

关键词： Mobile cloud computing computation offloading offloading decision Resource management Edge equipment deployment Fault tolerance Privacy protection

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Toward Mobility-Aware computation offloading and Resource Allocation in End-Edge-Cloud Orchestrated Computing

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IEEE INTERNET OF THINGS JOURNAL 2022年第19期9卷 19450-19462页

作者： Dai, Bin Niu, Jianwei Ren, Tao Atiquzzaman, Mohammed Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Yuhang Hangzhou 310023 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Sch Informat Engn Res Inst Ind Technol Zhengzhou 450001 Peoples R China Univ Oklahoma Sch Comp Sci Norman OK 73019 USA

Mobile devices (MDs) have undergone a booming development, yet are still capacity limited in computation and energy resources and thus could face troubles when serving computation-intensive and delay-sensitive applications. Mobile-edge computing (MEC) has been proposed to accommodate MDs with both satisfactory latency and acceptable resources, by offloading MDs' tasks to near-deployed edge servers (ESs). Whereas, offloading tasks solely to ESs are difficult to meet distinct requirements of various applications, which leads to the emergence of end-edge-cloud orchestrated computing (EECOC). However, studies on EECOC are still insufficient, most existing of which do not consider MDs' dynamical movements partly due to the intractability of associating optimal ESs with moving MDs. To address the issue, a novel deep reinforcement learning (DRL)-based mobility-aware (MA) EECOC scheduling approach is proposed in this article. With the goal of minimizing maximal task latency, we first formulate and transform the optimization problem into a Markov decision problem (MDP). Then, we enable DRL with elaborately designed reward functions and integrate it with NoisyNet to obtain near-optimal solutions. Furthermore, a MA component based on ConvLSTM is developed to extract MDs' temporal-spatial distribution features and predict their movements, which are further utilized to facilitate the decision making of computation-offloading and resource-allocation actions. Extensive experimental results indicate the promising performance improvements of our approach against the state-of-the-art approaches in various scenarios.

关键词： Task analysis Resource management Processor scheduling Job shop scheduling Cloud computing Optimization Dynamic scheduling computation offloading end-edge-cloud orchestrated mobile-edge computing (MEC) mobility aware (MA) resource allocation

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On the Design of Federated Learning in Latency and Energy Constrained computation offloading Operations in Vehicular Edge Computing Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第2期71卷 2041-2057页

作者： Shinde, Swapnil Bozorgchenani, Arash Tarchi, Daniele Ni, Qiang Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon I-40126 Bologna Italy Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). computation offloading has emerged as a viable option allowing to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption.

关键词： Servers Costs Delays Edge computing Task analysis Resource management Optimization Vehicular Edge Computing computation offloading Federated Learning latency energy consumption Genetic Algorithm

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Efficient Multi-Channel computation offloading for Mobile Edge Computing: A Game-Theoretic Approach

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2022年第3期10卷 1738-1750页

作者： Chu, Shuhui Fang, Zhiyi Song, Shinan Zhang, Zhanyang Gao, Chengxi Xu, Chengzhong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China CUNY Comp Sci Dept New York NY 10017 USA Univ Macau Fac Sci & Technol State Key Lab IoTSC Macau 999078 Peoples R China

Mobile edge computing is emerging to provide cloud-computing capabilities to mobile users, so that they can offload computation intensive tasks to close proximity for execution. However, most existing works imply that a transmission-finished task still occupies the channel until all users on the same channel finish the transmission, leading to severe channel resource waste. To solve this problem, we propose an efficient computation offloading mechanism which releases the channel resources of transmission-finished tasks for transmission-unfinished tasks, and aims to minimize the response time and energy consumption for each user. Specifically, we formulate the computation offloading problem as a game, analyze its structural properties and show how it possesses a Nash equilibrium and admits the finite improvement property, in the cases of elastic cloud and non-elastic cloud respectively. We then propose a Distributed Multi-channel computation offloading (DMCO) algorithm, which can converge to a Nash equilibrium, and find the upper bound of the convergence time. We further evaluate the performance of DMCO using the price of anarchy. Numerical results show that DMCO scales well with the number of users, and outperforms existing works, for example, benefits 13.3 percent more users and reduces cost by 23.7 percent than CO, one of the best existing works.

关键词： computation offloading game theory mobile edge computing multi-channel communications Nash equilibrium

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Multi-device Multi-task computation offloading in Device to Device Communication

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期123卷 1883-1896页

作者： Ali, Eslam B. Kishk, Sherif Abdelhay, Ehab H. Mansoura Univ Fac Engn Mansoura Egypt

In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigned to offload the tasks of end devices in multi-device multi-task D2D communication. The proposed algorithms offload each task to single device, which enhance the performance by 4x at their maximum performance. To enhance the performance, the next fit algorithm is redesigned to offload the task to multiple devices, which enhances the performance to 88x at its maximum performance. The proposed algorithms are evaluated for different cell scenarios (i.e. femto, pico, micro and macro cells). Simulation results demonstrates that utilizing computation offloading minimizes the latency.

关键词： Device to Device computation offloading IoT Mobile Edge Computing Wireless Sensor Networks

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NOMA-Based Multi-User Mobile Edge computation offloading via Cooperative Multi-Agent Deep Reinforcement Learning

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第1期8卷 350-364页

作者： Chen, Zhao Zhang, Lei Pei, Yukui Jiang, Chunxiao Yin, Liuguo Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Mobile edge computing (MEC) is a promising solution to enable resource-limited mobile devices to offload computation-intensive tasks to nearby edge servers. In this paper, dynamic computation offloading in a non-orthogonal multiple access (NOMA) based multi-user network is investigated by considering stochastic task arrivals and wireless channels. Specifically, a cooperative multi-agent deep reinforcement learning (CMADRL) based framework is proposed to learn decentralized offloading policies to allocate task offloading and local execution powers at each user, which minimizes long-term average network computation cost in terms of power consumption and buffering delay. By leveraging a centralized training and distributed execution strategy, the proposed framework can not only learn efficient decentralized policies, but also relieves users' computational pressure and works well in coordinating the interference of NOMA-based networks. To reduce the training complexity, it is further improved to train one parameter shared policy network that can be exploited by all users with slightly sacrificed performance. Numerical simulations demonstrate that the proposed CMADRL based framework can learn efficient dynamic offloading policies at each user, which significantly outperforms the conventional independent Q-learning based framework as well as some greedy strategies with lower network computation cost.

关键词： Mobile edge computing computation offloading NOMA deep reinforcement learning

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Joint Device Association, Resource Allocation, and computation offloading in Ultradense Multidevice and Multitask IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第19期9卷 18695-18709页

作者： Zhou, Tianqing Yue, Yali Qin, Dong Nie, Xuefang Li, Xuan Li, Chunguo East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices' latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices' latency constraints.

关键词： Task analysis Energy consumption Resource management Multitasking Interference Servers Internet of Things computation offloading device association Internet of Things (IoT) networks mobile-edge computing (MEC) multidevice multitask resource allocation

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