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Maritime distributed computation offloading in Space-Air-Ground-Sea Integrated Networks

IEEE COMMUNICATIONS LETTERS 2024年第7期28卷 1614-1618页

作者： Lin, Zhijian Yang, Jianjie Chen, Yingyang Xu, Chen Zhang, Xiaoqing Fuzhou Univ Dept Elect & Informat Engn Fuzhou 350108 Fujian Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Ocean Univ China Coll Elect Engn Qingdao 266100 Peoples R China

The sharp development of maritime activities fosters maritime Internet-of-Things (M-IoT) applications. However, the computation resources of vessels are limited, which cannot adequately satisfy high-quality services in M-IoT. With this consideration, space-air-ground-sea integrated networks (SAGSINs) are introduced in this work. Besides, a maritime distributed computation offloading framework is designed. Furthermore, an energy consumption minimization problem is formulated to jointly optimize the offloading strategy and the bandwidth allocation. For solution, a double-iteration penalty-limited simulated annealing-based particle swarm optimization algorithm is proposed. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

关键词： Task analysis Satellites Energy consumption Bandwidth Transmitting antennas Satellite communications Receiving antennas Maritime Internet of Things (M-IoT) distributed computation offloading space-air-ground-sea integrated network (SAGSIN) energy consumption minimization

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Bandit Learning-Based distributed computation in Fog Computing Networks: A Survey

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IEEE ACCESS 2023年 11卷 104763-104774页

作者： Tran-Dang, Hoa Kwon, Ki-Hyup Kim, Dong-Seong Kumoh Natl Inst Technol Dept Elect Engn Gumi Si 39177 South Korea Kumoh Natl Inst Technol Ind Acad Cooperat Fdn Gumi Si 39177 South Korea

Fog computing is a decentralized computing infrastructure that extends the capabilities of cloud computing closer to the edge of the network. In a fog computing network (FCN), computing resources, such as processing power, storage, and networking, are distributed at various points in the network, including edge devices, fog nodes (FNs) such as access points, gateways, and local servers. This architecture allows resource-limited end devices to offload part of their computational tasks to nearby FNs to achieve the reduced response delay services and energy efficiency. However, the high dynamics and complicated heterogeneity of fog computing environment in many application scenarios result in the uncertainty of network information that is raised as a critical challenge to design efficient computation offloading strategies. Meanwhile, existing solutions such as centralized optimization, matching and game theory-based decentralized offloading are inadequate to be adopted in this context because they require the perfect knowledge of system parameters. Considering as a promising approach to deal with the information uncertainty issues, bandit learning has used recently to develop distributed computation offloading (DCO) algorithms for the FCNs. In this paper, we aim at reviewing such of these state-of-the-art DCO solutions and elaborate their advantages and limitations. Additionally, we identify open research challenges and provide future directions for research in this area. Our survey shows that bandit learning is a promising approach for efficient computation offloading in fog computing, and we expect that future research will continue to explore its potential for improving the performance and energy efficiency of fog computing-enabled systems.

关键词： Fog computing network distributed computation offloading multi-armed bandit (MAB) learning reinforcement learning non-stationary bandit contextual bandit non-contextual bandit resource allocation

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Self-Learning Based computation offloading for Internet of Vehicles: Model and Algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第9期20卷 5913-5925页

作者： Luo, Quyuan Li, Changle Luan, Tom H. Shi, Weisong Wu, Weigang Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Xidian Univ Res Inst Smart Transportat Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China

With the fast development of Internet of Vehicles (IoV), various types of computation-intensive vehicular applications pose significant challenges to resource-constrained vehicles. The emerging Vehicular Edge Computing (VEC) and Edge Intelligence (EI) can alleviate this situation by offloading the computation tasks of vehicles to the roadside edge servers. However, with many vehicles contending for the communication and computation resources at the same time, how to quickly and efficiently make an optimal computation offloading decision for individual vehicles represents a fundamental research issue. In this paper, we propose a self-learning based distributed computation offloading scheme for IoV. Note that without any centralized controller, a fully distributed algorithm is necessary. The proposed scheme is devised based on a game-theoretic model. Specifically, through establishing an offloading framework with communication and computation for IoV, the computation offloading problem is first formulated as a distributed offloading decision-making game, in which each vehicle as a player makes its best response decision to minimize its joint cost (including latency and offloading cost). The existence of Nash Equilibrium can be proved. We then propose a self-learning based distributed computation offloading (DISCO) algorithm to reach the Nash Equilibrium, where a mutually satisfactory solution among vehicles is obtained and no vehicle is willing to change its decision. Using extensive simulations, we verify that DISCO can outperform the counterparts and achieve at least an order-of-magnitude improvement on time overhead and 88% performance gain on message overhead, only at up to 12% performance loss on joint cost over the centralized scheme.

关键词： Task analysis Games Heuristic algorithms Servers Optimization Nash equilibrium computational modeling Vehicular edge computing distributed computation offloading game theory Nash equilibrium self-learning

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SyRoC: Symbiotic robotics for QoS-aware heterogeneous applications in IoT-edge-cloud computing paradigm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 150卷 202-219页

作者： Zhu, Anqi Lu, Huimin Guo, Songtao Zeng, Zhiwen Ma, Mingfang Zhou, Zongtan Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Natl Univ Def Technol Coll Sci Changsha 410073 Peoples R China

Smart Robots, as an advanced domain of widespread concern, can be applied in diverse fields to perform compute-intensive Internet of Things (IoT) applications. However, their onboard resource constraints necessitate using the computation offloading technique to extend their capabilities. Additionally, the distributed locations of robots and the diversified quality of service (QoS) requirements of different applications pose challenges to the existing offloading works. To address these concerns, we put forward a collaborative offloading scheme for QoS-aware utility optimization for multi-robot system, and the optimization problem is formulated based on multi-attribute utility theory. Our methodology consists of three critical components: (1) we construct a symbiotic collaboration model that leverages the advantages of multiple computation modes across IoT-edge-cloud infrastructure and tap the potential of multi-hop relay transmission technology to enable efficient collaborative offloading;(2) we develop a QoS-aware preference assessment framework based on analytic hierarchy process to effectively learn the preference weights of various applications executed and prioritizations for key QoS parameters affecting strategy-making of robots;and (3) we apply the game-theoretic approach to devise a symbiotic robotics computation offloading algorithm named SyRoC, which operates in a distributed manner to solve the optimization problem and determine the most effective strategies. To validate our scheme, we conduct a series of experiments on the software simulator, which demonstrate that our proposed scheme not only realizes stable performance on guaranteeing convergence and system utility when robots move dynamically, but also outperforms five typical algorithms in key metrics such as energy consumption (13.43% 4,), delay (14.22% 4,), and average QoS utility of robots corresponding to the four types of applications executed (12.80% f, 7.30% f, 7.90% f, and 12.71% f) in scenari

关键词： Collaborative robots distributed computation offloading Game theory Internet of things Quality of service

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An Efficient Bandit Learning for Matching based distributed Task offloading in Fog Computing Network 16

An Efficient Bandit Learning for Matching based Distributed ...

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16th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)

作者： Tran-Dang, Hoa Kim, Dong-Seong Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi South Korea

ISBN: (纸本)9798350393613

To seek for an efficient distributed offloading solution to the multi-task multi-helper (MTMH) problem in the fog computing networks, we model it as a matching game between a set of task nodes (TNs) having task computation needs and a set of helper nodes (HNs) having available computing resources. However, the uncertainty of computing resource availability of HNs as well as dynamics of QoS requirements of tasks result in the lack of preferences of TN side that mainly poses a critical challenge to obtain a stable and reliable matching outcome. To address this challenge, we apply a multi-armed bandit (MAB) learning using Thomson sampling (TS) mechanism to acquire better exploitation and exploration trade-off, allowing TNs to match with their corresponding HNs efficiently. Based on these, this paper proposes an efficient bandit learning based matching (BLM) for distributed task offloading in the fog computing networks. Extensive simulation results demonstrate the potential advantages of the TS-type algorithm over the epsilon-greedy and UCB based offloading algorithms.

关键词： Fog computing network multi-armed bandit exploitation and exploration dilemma Thompson sampling distributed computation offloading stable matching

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Online Learning based Matching for Decentralized Task offloading in Fog-enabled IoT Systems 28

Online Learning based Matching for Decentralized Task Offloa...

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28th Asia-Pacific Conference on Communications (APCC)

作者： Tran-Dang, Hoa Kim, Dong-Seong

ISBN: (纸本)9798350382617

Matching theory has been applied to design efficient offloading solutions to the multi-task multi-helper (MTMH) problem in the fog computing networks, which is modeled as a matching game between a set of task nodes (TNs) having task computation needs and a set of helper nodes (HNs) having available computing resources. However, the uncertainty of computing resource availability of HNs as well as dynamics of QoS requirements of tasks result in the lack of preferences of TN side that mainly poses a critical challenge to obtain a stable and reliable matching outcome. To address this challenge, we apply a multi-armed bandit (MAB) learning using Thomson sampling (TS) mechanism to acquire better exploitation and exploration trade-off, allowing TNs to match with their corresponding HNs efficiently. Based on these, this paper proposes online learning based matching (OLM) algorithm for decentralized task offloading to reduce the offloading delay in Fog-enabled IoT Systems. Extensive simulation results demonstrate the potential advantages of the TS-type algorithm over the epsilon-greedy and UCB based offloading algorithms.

关键词： Fog computing network multi-armed bandit exploitation and exploration dilemma Thompson sampling distributed computation offloading stable matching

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CCOS: A Coded computation offloading Strategy for Satellite-Terrestrial Integrated Networks 17

CCOS: A Coded Computation Offloading Strategy for Satellite-...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Pang, Bo Gu, Shushi Zhang, Qinyu Zhang, Ning Xiang, Wei Harbin Inst Technol Shenzhen Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518052 Peoples R China Univ Windsor Windsor ON N9B 3P4 Canada La Trobe Univ Melbourne Vic 3086 Australia

ISBN: (纸本)9781728186160

Ultra-dense computation services are widely distributed in various application scenarios with the rapid development of artificial intelligence and machine learning. Relying on the existing ground cellular networks, it is challenging to satisfy the 6G vision of full coverage and massive machine connectivity. Satellite-terrestrial integrated network (STIN) has abundant computation resources and seamless coverage ability, which can be served as an effective supplementary for the task allocating in cellular networks. Nevertheless, STIN has the characteristic of architecture complexity, unavoidable stragglers and high economic costs. The rational computation resource allocation among distributed on-orbit satellites becomes an urge problem, synthesizing these drawbacks in STINs. In this paper, to address these issues, we attempt to design a coded computation offloading strategy (CCOS) to migrate ground ultra-dense computing tasks to distributed satellite constellations in space. Considering the effect of unpredictable computation resource occupation on satellites, we investigate two coded computation methods, i.e., maximum distance separable (MDS) code and rateless code, to resist the random stragglers occurring on satellite nodes. Then, we formulate the optimization problem about minimizing the delay-energy tradeoff cost with different CCOSs under the tolerant time constraints, and obtain the optimal task offloading decisions (i.e., executing locations and coding parameters) using a proposed low-cost offloading decision searching algorithm (LODSA). Numerical simulation results show that, our coded computation strategies can significantly eliminate the effect of stragglers, and improve the cost performance obviously compared with the un-coded strategies in typical application cases.

关键词： satellite-terrestrial integrated network distributed computation offloading coded computation delay-energy cost optimization

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Decentralized adaptive resource-aware computation offloading & caching for multi-access edge computing networks

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2021年 30卷 100555-100555页

作者： Tefera, Getenet She, Kun Shelke, Maya Ahmed, Awais Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Symbiosis Int Univ Dept Comp Sci & Informat Technol Pune Maharashtra India

Decentralized computation offloading and caching in Multi-Access Edge Computing (MEC) is a promising approach to evolve the forthcoming network generation. MEC is the emerging technology that provides adaptive micro cloud services to the edge of proximity resource-constrained smart communication and Internet of Everything (IoE) devices for cellular subscribers. Nowadays, Massive IoE devices are exponentially connected to the global ecosystem. As a result, the backhaul network traffic grows enormously and users' ultra-reliable low latency communications are challenging as well. In this paper, we explored decentralized adaptive resourceaware communication, computing, & caching framework which can orchestrate the dynamic network environments based on Deep Reinforcement Learning (DRL). Subsequently, the framework can perform augmented decision-making capabilities to enhance users' connectivity and resource utilization requirements. Basically, every IoE device user are attempting to capitalize their own utilities. Hence, the problem is formulated using Non-cooperative game theory which is non-deterministic polynomial to solve the structural property of the MEC networks. We analyze and show that the game admits a Nash Equilibrium. Moreover, we have introduced a decentralized cognitive scheduling algorithm by exploiting DRL technology to leverage the utility of IoE & smart communication devices. Therefore, numerical results and theoretical analysis revealed that the proposed algorithm outperform, ultra-reliable low latency, and scalable than the baseline schemes.

关键词： Multi-access edge computing Cloud computing distributed computation offloading Radio-access networks Game theory Deep reinforcement learning distributed algorithm

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