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Minimization of Energy and Service Latency computation offloading using Neural Network in 5G NOMA System

INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 2023年第4期69卷 661-667页

作者： Suprith, P. G. Ahmed, Mohammed Riyaz REVA Univ Bangalore Karnataka India REVA Univ Bangalore Karnataka India HKBK Coll Engn Bangalore Karnataka India

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.

关键词： Mobile edge computing Deep Q Network Algorithm Latency Optimized computation offloading 5G

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Mean Peak Age of Information Analysis of Energy-Aware computation offloading in IIoT Networks 99

Mean Peak Age of Information Analysis of Energy-Aware Comput...

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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Ernest, Tan Zheng Hui Madhukumar, A. S. Agcy Sci Technol & Res Adv Remfg & Technol Ctr Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9798350387414

The mean peak age of information (PAoI) of computation offloading in multi-access edge computing (MEC) enabled industrial Internet-of-Things (IIoT) networks is investigated in this paper. Specifically, the mean PAoI and average energy consumption of energy-aware computation offloading (ECO) and cloud-only (CL) computation offloading are derived in closed-form. Numerical and simulation results show that the mean PAoI and average energy consumption of the ECO and CL strategies are dependent on the task generation rate and local computation capability experienced in the MEC-enabled IIoT networks. Specifically, the ECO strategy is shown to achieve lower mean PAoI even at high task generation rates in the MEC-enabled IIoT network. Furthermore, we show that the ECO strategy achieves lower average energy consumption than the CL strategy even when local computing capability in the MEC-enabled IIoT network is limited.

关键词： Multi-Access Edge Computing computation offloading Industrial Internet-of-Things Peak Age of Information Queuing Theory

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Wasimoff: Distributed computation offloading Using WebAssembly in the Browser

Wasimoff: Distributed Computation Offloading Using WebAssemb...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Semjonov, Anton Bornholdt, Heiko Edinger, Janick Russo, Gabriele Russo Univ Hamburg Dept Informat Hamburg Germany Univ Roma Tor Vergata Rome Italy

ISBN: (纸本)9798350304367;9798350304374

Recently, there is a growing trend where end-users personally engage in computationally-intensive applications, particularly in domains such as AI, AR/VR, and simulation. Consequently, there arises an increasing demand to enhance the capabilities of local devices by offloading computational work to remote resources, which can also be provided by endusers. To address the requirements of modern peer-to-peer (P2P) offloading architectures, we have developed Wasimoff-an innovative browser-based framework designed for dynamic volunteer computing. Leveraging WebAssembly, applications can securely run in an isolated browser environment. Device owners can effortlessly share their available CPU cycles and participate in the collaborative network by accessing a designated URL in their browser. To assess Wasimoff's performance, we conducted a real-world evaluation with devices located in both Germany and the USA. The results affirm Wasimoff's ability to scale linearly as the system's entities increase. Moreover, Wasimoff consistently maintains execution speed even under congested system conditions.

关键词： serverless computing computation offloading peer-to-peer computing webassembly

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computation offloading and pricing in mobile edge computing based on Stackelberg game

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WIRELESS NETWORKS 2021年第7期27卷 4795-4806页

作者： Liu, Zongyun Fu, Jingqi Zhang, Yue Shanghai Univ Sch Mech Engn & Automat Shanghai Peoples R China

Aiming at the problem of offloading optimization of transmission power and edge cloud pricing in mobile edge computing systems, this paper proposes an offloading Strategy-and Price Control(OSPC) algorithm based on Stackelberg game. First, the Stackelberg game is used to establish the edge cloud pricing and transmission power decision model, which realizes the optimization of the utility of edge cloud and each user under the task delay constraint and the edge cloud capacity constraint;Then, a relationship model between the optimal transmission power and the amount of offloading data is established, which simplifies the user's optimal transmission power offloading decision;Finally, an OSPC algorithm is studied, which achieves the optimal pricing of edge cloud utility maximization and the optimal offloading strategy for each user to maximize their own utility under this pricing. The simulation results show that the method proposed in this paper not only guarantees the utility of the edge cloud, but also maximizes the utility of users, and the algorithm has good convergence and scalability.

关键词： Mobile edge computing Stackelberg game computation offloading Utility maximization

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computation offloading and Wireless Resource Management for Healthcare Monitoring in Fog-Computing-Based Internet of Medical Things

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IEEE INTERNET OF THINGS JOURNAL 2021年第21期8卷 15875-15883页

作者： Qiu, Yu Zhang, Haijun Long, Keping Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing Engn & Technol Res Ctr Convergence Networ Inst Artificial Intelligence Beijing 100083 Peoples R China

During the COVID-19 pandemic, Internet of Medical Things (IoMT) has been playing an important role in controlling the development of the epidemic, including enabling doctors in different grade hospitals to make a diagnosis and treatment, isolating and care for confirmed and suspected cases promptly, and preventing infection of patients with the novel coronavirus. In this article, we investigate the minimization optimization problem for healthcare monitoring in fog computing-based IoMT (FogC-IoMT), which is nonlinear and nonconvex problem, by considering Quality-of-Service requirement, power limit, and wireless fronthaul constraint. In order to solve the problem effectively, three independent subproblems are decoupled, and the suboptimal low-complexity computation offloading and resource management scheme is proposed in FogC-IoMT. The simulation results reveal the effectiveness of the proposed optimization algorithm in terms of cost utility.

关键词： Sensors Sensor systems Cloud computing Medical diagnostic imaging Wireless communication Medical services Task analysis computation offloading fog computing-based IoMT (FogC-IoMT) healthcare monitoring wireless resource management

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computation offloading and Energy Harvesting Schemes for Sum Rate Maximization in Space-Air-Ground Networks

Computation Offloading and Energy Harvesting Schemes for Sum...

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IEEE Global Communications Conference (GLOBECOM)

作者： Gong, Yongkang Yao, Haipeng Xiong, Zehui Guo, Song Yu, Richard Niyato, Dusit Beijing Univ Posts & Telecommun Beijing Peoples R China Singapore Univ Technol & Design Singapore Singapore Hong Kong Polytech Univ Hong Kong Peoples R China Carleton Univ Ottawa ON Canada Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665435406

The space-air-ground (SAG) integrated networks will play a major role in the sixth generation (6G) mobile networks, which will provide global coverage, full connection and pervasive intelligence services for multiple ground Internet of Things (IoT) devices. Moreover, massive computing tasks can be either performed by local devices, or offloaded to edge servers, such as low orbit satellites, high altitude platforms (HAPs) and remote base stations. Nevertheless, the joint computation and communication resource allocation solutions are becoming challenging due to the large-scale state space, time-varying network scenarios, and limited battery capacity. In this paper, we propose a SAG-integrated three-layer heterogenous network model to maximize the sum-rate of ground IoT devices, which further enhances the deep integration of communication and computation resources. Additionally, we develop a Lyapunov-assisted multi-agent proximal policy optimization algorithm to process the task scheduling, HAP selection, battery harvesting, and CPU cycle frequency optimization. Extensive simulation results corroborate that the proposed method has superior performance gains in terms of the remaining battery capacity, energy consumption, and maximum average sum-rate compared with the state-of-the-art baselines.

关键词： computation offloading energy harvesting Lyapunov-assisted multi-agent proximal policy optimization space-air-ground (SAG) networks

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computation offloading From Edge to Equipment for Smart Manufacturing 15

Computation Offloading From Edge to Equipment for Smart Manu...

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IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Nguyen, Hung H. Zhou, Yi Kushagra, Kushagra Qin, Xiao Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA Columbus State Univ TSYS Sch Comp Sci Columbus GA 31907 USA

ISBN: (纸本)9781665460873

In smart manufacturing, data management systems are built with a multi-layer architecture, in which the most significant layers are the edge and the cloud. The edge layer renders support to data analysis that genuinely demands low latency. Cloud platforms store vast amounts of data while performing extensive computations such as machine learning and big data analysis. This type of data management system has a limitation rooted in the fact that all data needs to be transferred from the equipment layer to the edge layer in order to perform all data analyses. Even worse, data transferring adds delays to computation processes in smart manufacturing. We investigate an offloading strategy to shift a selection of computation tasks towards the equipment layer. Our computation offloading mechanism opts for smart manufacturing tasks that are not only light weight but also have no need to save data at the edge/cloud end. In our empirical study, we demonstrate that the edge layer can judiciously offload computing tasks to the equipment layer, which curtails computing latency and slashes the amount of transferred data during smart manufacturing process. Our experimental results confirm that our offloading strategy offers the capability for data analysis computing in real-time at the equipment level - an array of smart devices is slated to speed up the data analysis process in semiconductor manufacturing.

关键词： edge computing cloud computing smart manufacturing semiconductor computation offloading computing performance algorithmic complexity O(n) O(nlogn) O(n(2))

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Joint Network Slicing and computation offloading for Multi-access Edge Computing: A Bi-level Game Approach 59

Joint Network Slicing and Computation Offloading for Multi-a...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Wei, Fengsheng Qin, Shuang Feng, Gang Wang, Yatong Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Huzhou 313001 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

ISBN: (纸本)9781728190549

One of the key challenges faced by 5G-Advanced and the forthcoming 6G networks is the provisioning of delay-critical services. Recently, the integration of network slicing and Multi-access Edge Computing (MEC) is regarded as a promising solution for this challenge. However, existing proposals for the integration are far from perfect with various drawbacks, such as rigid slicing, privacy leakage, and high signaling costs. In this paper, we investigate the problem of joint network slicing and computation offloading, which is formulated as a multi-leader-disjoint-follower Stackelberg game. We prove that the game is a potential game which has at least one global Nash Equilibrium (gNE). Then we propose a distributed algorithm that provably converges to a gNE of the game without revealing any private information of all stakeholders. The performance of our proposed algorithm is evaluated through simulations, which demonstrate that the algorithm converges to the gNE rapidly and outperforms a number of benchmark algorithms.

关键词： computation offloading

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computation offloading and service allocation in mobile edge computing

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JOURNAL OF SUPERCOMPUTING 2021年第12期77卷 13933-13962页

作者： Li, Chunlin Cai, Qianqian Zhang, Chaokun Ma, Bingbin Luo, Youlong Geomat Technol & Applicat Key Lab Qinghai Prov Xining 810001 Peoples R China Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Peoples R China

The intensive mobile data traffic poses a great challenge for energy-constrained mobile devices. In the mobile edge environment, effective computing offloading and resource allocation can improve the service performance of edge computing systems. Therefore, a dynamic computation offloading model based on genetic algorithm is proposed in this paper. In this strategy, a task weight cost model based on processing delay and energy consumption is built, which can optimize processing delay and energy consumption simultaneously. Moreover, in view of the limited computing resources of edge servers, a resource allocation model based on utility maximization is proposed. In this strategy, the bidding strategies of users and edge nodes are studied and the resource is allocated to the high-unit bidding users based on the greedy strategy during the double auction process. A large number of experimental results show that the proposed computation offloading algorithm can significantly reduce task processing delay and energy consumption. For instance, the proposed offloading algorithm can save energy up to 14.81% and reduce processing delay up to 7.71% compared with the COPSO algorithm. Besides, the proposed resource allocation algorithm can promote the number of successful auction users and maximize the utility of the users and the edge nodes.

关键词： Mobile edge environment computation offloading Compute-intensive applications Service allocation

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computation offloading and Shunting Scheme in Wireless Wireline Internetwork

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第10期69卷 6808-6821页

作者： Zhang, Jiadong Shi, Wenxiao Zhang, Ruidong Liu, Wei Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

In the multi-server multi-access edge computing (MEC) system, the computing capacity could be insufficient and the computing resource could be over-utilized when an excessive number of computing tasks are offloaded for execution. To alleviate the excessive burden in the multi-server MEC system and tap the underutilized resources of wired edge devices, which are called edge computers in this paper, we investigate the computation offloading and shunting scheme in the wireless wireline internetwork. We formulate an optimization problem to minimize the average total time delay and use the deep reinforcement learning method to obtain the optimal shunting policy. By sensing the state of the wireless wireline internetwork, the proposed scheme can adaptively adjust the shunting ratio and utilize the resources of MEC servers and edge computers intelligently and jointly. Finally, we evaluate the computational complexity and validate the convergence property of this scheme. The extensive simulation results demonstrate that this scheme can improve the resource productivity and reduce the average total time delay.

关键词： Task analysis Delays Wireless communication Wireless sensor networks Communication system security Optimization Edge computing computation offloading computation shunting deep reinforcement learning multi-access edge computing wireless wireline internetwork

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