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Joint computation offloading and Bandwidth Assignment in Cloud-Assisted Edge Computing

IEEE TRANSACTIONS ON CLOUD COMPUTING 2022年第1期10卷 451-460页

作者： Guo, Kai Yang, Mingcong Zhang, Yongbing Cao, Jiannong Univ Tsukuba Grad Sch Syst & Informat Engn Tsukuba Ibaraki 3058577 Japan Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China

offloading computation based on mobile edge computing paradigms can augment the computational capabilities of resource-scarce mobile devices. However, the capacity limitations of edge servers constrain the performance improvement achieved through computation offloading. In this article, we consider a three-tier computation offloading schema with multiple users, edge servers, and cloud servers. computation can be offloaded from mobile devices to edge servers or can be further offloaded to remote cloud servers if necessary. Since a number of mobile devices connected to edge servers will share a common wireless communication network, which may contain both uplink and downlink channels, the assignment of bandwidth assignment the channels also constrains the performance improvement of computation offloading. In this paper, the problems of how to determine the offloading strategy and of how to assign the bandwidth are jointly studied and formulated as a programming problem to minimize the average application response time. We analyze the joint problem and further transform it into a piecewise convex programming problem. We propose an efficient algorithm that can find the optimal solution. Extensive experiments demonstrate that our algorithm significantly outperforms previous algorithms. The experimental results also show that the performance of our algorithm is highly robust.

关键词： Mobile edge computing computation offloading bandwidth assignment

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Discontinuous computation offloading for Energy-Efficient Mobile Edge Computing

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第2期6卷 1242-1257页

作者： Merluzzi, Mattia di Pietro, Nicola Di Lorenzo, Paolo Strinati, Emilio Calvanese Barbarossa, Sergio Univ Grenoble Alpes CEA Leti F-38000 Grenoble France Athonet Syst Solut & Innovat I-36050 Bolzano Vicentino Italy Sapienza Univ Dept Informat Engn Elect & Telecommun I-00184 Rome Italy

We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks. The goal is to minimize the energy consumption of the overall system, comprising multiple User Equipment (UE), an access point (AP), and an edge server (ES), under constraints on the end-to-end service delay and the packet error rate performance over the wireless interface. To reduce the energy consumption, we exploit low-power sleep operation modes for the users, the AP and the ES, shifting the edge computing paradigm from an always on to an always available architecture, capable of guaranteeing an on-demand target service quality with the minimum energy consumption. To this aim, we propose an online algorithm for dynamic and optimal orchestration of radio and computational resources called Discontinuous computation offloading (DisCO). In such a framework, end-to-end delay constraints translate into constraints on overall queueing delays, including both the communication and the computation phases of the offloading service. DisCO hinges on Lyapunov stochastic optimization, does not require any prior knowledge on the statistics of the offloading traffic or the radio channels, and satisfies the long-term performance constraints imposed by the users. Several numerical results illustrate the advantages of the proposed method.

关键词： Delays Energy consumption Optimization Power demand 5G mobile communication Reliability Uplink Edge computing beyond 5G green networking computation offloading energy efficiency

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A Probabilistic Approach for Cooperative computation offloading in MEC-Assisted Vehicular Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第2期23卷 899-911页

作者： Dai, Penglin Hu, Kaiwen Wu, Xiao Xing, Huanlai Teng, Fei Yu, Zhaofei Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Inst Digital Media Beijing 100871 Peoples R China

Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive applications by offloading resources at network edge. Especially in vehicular networks, the MEC server, is deployed as a small-scale computation server at the roadside and offloads computation-intensive task to its local server. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous capacities of MEC servers, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this article, we investigate a novel scenario of computation offloading in MEC-assisted architecture, where task upload coordination between multiple vehicles, task migration between MEC/cloud servers and heterogeneous computation capabilities of MEC/cloud severs, are comprehensively investigated. On this basis, we formulate cooperative computation offloading (CCO) problem by modeling the procedure of task upload, migration and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CCO problem, we propose a probabilistic computation offloading (PCO) algorithm, which enables MEC server to independently make online scheduling based on the derived allocation probability. Specifically, the PCO transforms the objective function into augmented Lagrangian and achieves the optimal solution in an iterative way, based on a convex framework called Alternating Direction Method of Multipliers (ADMM). Last but not the least, we implement the simulation model. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.

关键词： Servers Task analysis computational modeling Computer architecture Delays Vehicle dynamics Processor scheduling Vehicular networks mobile edge computing computation offloading queuing theory optimization

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Energy Minimized computation offloading With Popularity-Based Cooperation in 5G mMTC Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第5期9卷 3238-3250页

作者： Jung, Dongjun Kim, Junsung Chung, Jong-Moon Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea

Due to enhancements in Internet of Things (IoT) technology, users can now control numerous IoT devices that are integrated for a system (e.g., smart building management system, smart factory, etc.) through mobile user equipment (UE). As the number of controllable IoT devices increases, the amount of data that needs to be processed has increased along with the energy consumption. Since many IoT devices and most UEs are battery operated, minimizing the energy consumption is very important. One solution is to have a multiaccess edge computing (MEC) system conduct the computation instead of the IoT devices and UEs to help save their energy. In this article, an energy-optimized offloading approach that uses MEC computing support to process cooperative tasks is investigated. An optimization problem model is developed to minimize the energy consumption of IoT devices and UEs that have service delay limits. The numerical results demonstrate that the proposed IoT and UE popularity-based energy optimization (IPEO) scheme provides a better performance compared to the conventional MEC offloading methods.

关键词： Task analysis Servers Internet of Things Energy consumption Process control Delays Optimization computation offloading energy consumption Internet of Things (IoT) multiaccess edge computing (MEC)

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A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2022年第5期25卷 2049-2107页

作者： Kumar, Dinesh Baranwal, Gaurav Shankar, Yamini Vidyarthi, Deo Prakash Motilal Nehru Natl Inst Technol Dept Comp Sci & Engn Allahabad 211004 UP India Banaras Hindu Univ Inst Sci Dept Comp Sci Varanasi 221005 UP India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Internet of Things (IoT) aims to make an environment more innovative and productive by connecting physical things to the internet. Processing generated data from IoT devices and actuation warranted in real-time requires computational infrastructure near the edge to get the outcome without delay. Emerging edge technologies such as Fog computing, Multi-Access Edge Computing, and Cloudlet provide computing resources near the edge, i.e. closer to the IoT devices, where devices can place their services/applications or offload their computational job for processing. The utilization of computing resources provided by emerging edge technologies addresses the issue of delay in the outcome and increases the battery life of IoT devices/End-user devices. computational resources provided by the edge technologies, i.e. edge/fog nodes, can be heterogeneous, dynamic and mobile. Therefore, service placement and computation offloading on edge/fog nodes are challenging issues, and the problem to finding the best suitable fog/edge nodes is NP-Hard. Nature-inspired algorithms provide robust solutions to NP-Hard problems. Nowadays, nature-inspired algorithms have been widely applied for resource allocation for service placement and computation offloading in emerging edge technologies. In this work, we provide a detailed study on the applications of nature-inspired algorithms in emerging edge computing domains. We provide an overview of emerging edge technologies, related quality parameters and nature-inspired algorithms followed by the basic formulation of service placement and computation offloading in emerging edge computing systems. In this work, we classify the works in emerging edge computing applying nature-inspired algorithms into two categories: works related to service placement and works related to offloading. We provide a thorough review and comparison of the existing nature-inspired approaches in each category. We discuss various open issues at the end to set future research

关键词： Edge Computing Fog Computing Internet of Things (IoT) Nature-inspired Algorithms computation offloading Service Placement

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Joint Wireless Resource and computation offloading Optimization for Energy Efficient Internet of Vehicles

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第3期6卷 1468-1480页

作者： Pliatsios, Dimitrios Sarigiannidis, Panagiotis Lagkas, Thomas D. Argyriou, Vasileios Boulogeorgos, Alexandros-Apostolos A. Baziana, Peristera Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece Int Hellen Univ Dept Comp Sci Sch Sci Kavala 65404 Greece Kingston Univ London Dept Networks & Digital Media Kingston Upon Thames KT1 2EE Surrey England Univ Piraeus Dept Digital Syst Piraeus 18534 Greece Univ Thessaly Dept Comp Sci & Telecommun Lamia 35131 Greece

The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

关键词： Task analysis Resource management Energy consumption Optimization Wireless communication Reinforcement learning Internet of Vehicles 6G B5G block coordinate descent computation offloading energy efficiency Internet of Vehicles mobile edge computing

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A Review of Intelligent computation offloading in Multiaccess Edge Computing

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IEEE ACCESS 2022年 10卷 71481-71495页

作者： Jin, Hengli Gregory, Mark A. Li, Shuo RMIT Univ Sch Engn Melbourne Vic 3000 Australia

Multi-Access Edge Computing (MEC) is a standardized architecture that enables cloud computing capabilities at the edge of heterogeneous networks. The concept is to reduce network congestion by running applications and network services closer to end-users. MEC is designed to be implemented at key locations on the network edge, including co-location with cellular base stations. MEC aims to facilitate computation intensive and delay sensitive applications, such as vehicular networks, face recognition, augmented reality and virtual reality. The service requirements for MEC are stochastic and time varying. Coupled with advances in artificial intelligence, a vast number of computation offloading approaches have been developed based on intelligent algorithms. This article provides a comprehensive review of intelligent computation offloading with critical issues, metrics and future directions.

关键词： Task analysis Artificial intelligence Optimization Servers Measurement Resource management Wireless communication Multi-access edge computing computation offloading artificial intelligence computer networking

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Latency-Aware Horizontal computation offloading for Parallel Processing in Fog-Enabled IoT

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 2537-2544页

作者： Deb, Pallav Kumar Misra, Sudip Mukherjee, Anandarup Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

In this article, we propose a two-step distributed horizontal architecture for computation offloading in a fog-enabled Internet of Things environment-HD-Fog-to minimize the overall energy consumption, and latency while executing hard real-time applications. HD stands for the horizontal distribution of the tasks in the fog layer. Each sensor in the user devices independently captures data of varying formats. Parallel execution on these data is possible based on its directed acyclic task graph (DATG), and the corresponding results facilitate the ease of decision-making. Toward this, in HD-Fog, the sensor nodes in user devices offload their tasks to a nearby fog node based on a greedy selection criterion. This fog node then further offloads the smaller subtasks, based on the DATG, among other fog nodes for parallel execution. Through extensive real-life metric-based emulation and comparison against traditional Fog and Cloud computing schemes, we observe that our approach 1) reduces the overall operational delays by 29% and 96%, and 2) offers promising speedup values. The proposed HD-Fog scheme also indicates a reduction in energy consumption by 30% compared to traditional fog computing schemes.

关键词： Task analysis Reliability Real-time systems Computer architecture Optimization Internet of Things Delays computation offloading distributed and parallel computing Fog computing Internet of Things (IoT) queueing theory

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Content Caching-Enhanced computation offloading in Mobile Edge Service Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第1期71卷 872-886页

作者： Dong, Yifan Guo, Songtao Wang, Quyuan Yu, Shui Yang, Yuanyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia SUNY Stony Brook Dept Elect & Comp Gineering Stony Brook NY 11794 USA

Cache enhanced computation offloading as a novel offloading paradigm in mobile edge computing (MEC) can reduce more task execution latency than traditional computation offloading by reusing of computation offloading data. However, existing works only focus on the enhancement between computation offloading and data caching but ignore the competition for cache resources between them. To this end, in this paper, we propose a caching enhanced computation offloading algorithm in mobile edge service networks (MESN), by considering the cache resources competition. We formulate a joint optimization problem of content caching and cache-enhanced computation offloading. Furthermore, we give the optimal caching strategy to achieve the equilibrium between the resources competition. By our offloading algorithm caching strategy, the average response time of computation and content request tasks can get further reduction. In addition, we design two low time complexity algorithms, i.e., mixed caching algorithm and enhanced offloading algorithm, to solve the sub-problems, i.e., smart base station (SBS) caching sub-problem and computation offloading sub-problem, transformed by the original optimization problem. The simulation results show that our algorithms can quickly converge and our scheme can reduce 20.52% average response time of all tasks at most compared with other schemes.

关键词： Task analysis Time factors Optimization Servers Collaboration System performance computational modeling Data reusing computation offloading content caching mobile edge computing

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A computation offloading Method for Delay Optimization in F-RAN 2

A Computation Offloading Method for Delay Optimization in F-...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Zeng, Haoheng Zhang, Lincong Liang, Ying Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350368888

To address the problem of delay optimization, a computation offloading scheme is proposed within a NOMA-based Fog Radio Access Network (F-RAN) system. This scheme involves selecting the offloading method with the lowest delay for user tasks, which may involve offloading tasks to a Fog Access Point (F-AP) for computation or forwarding tasks via the F-AP or Remote Radio Head (RRH) to the cloud for processing. Through the reasonable allocation of computational resources, multiple F-APs can share the same Resource Block (RB), thereby enhancing transmission efficiency and reducing computation offloading delay. © 2024 IEEE.

关键词： computation offloading

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