An energy harvesting solution for computation offloading in Fog Computing networks

Bozorgchenani, Arash; Disabato, Simone; Tarchi, Daniele; Roveri, Manuel

doi:10.1016/j.comcom.2020.06.032

Cited by 21 publications

(10 citation statements)

References 41 publications

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“…Several existing systems aim to offload the task to the various cloud or fog resources effectively but fail to achieve their objective efficiently. In [21], smart energy management is utilized on a cluster of fog nodes to predict the exploitation of information by predicting energy conserved or acquired by Fog nodes(FN) to support offloading that increases network lifetime by introducing the clustering mechanism at the network edge for computational offloading. The clustering algorithm allows the cluster members in each cluster to offload their tasks to the cluster head for computation by considering both consumed and harvested energy.…”

Section: Fog Offloading Decision-making Approachesmentioning

confidence: 99%

Mobility-Aware Intelligent Sampling and Improved African Buffalo Optimization-assisted Offloading in 5G-Fog Environment

2024

Preprint

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IoT in healthcare is revolutionizing the industry by providing real-time monitoring, remote patient care, and improved diagnosis and treatment options. Healthcare applications are latency sensitive and have major issues such as real-time response delay, latency and bandwidth overuse. In emergency scenarios, patient status control and monitoring directly impact lives. This research work proposes a novel technique: Rigorous execution of healthcare APplication using grey wolf-Improved african buffalo optimization-based offloading Decision (RAPID) algorithm to reduce the latency in fog environment. The RAPID approach generates the immediate response and forwards it to the caretakers by performing an offloading decision using the proposed Fog manager and a novel hybrid Grey Wolf-Improved African Buffalo Optimization (GW-IABO) algorithm with Intelligent Sampling technique and 5G communication. The proposed algorithm, Grey wolf optimization (GWO), evaluates the fitness of the solution, and Improved African Buffalo (IABO) explores and exploits the solutions by enhancing inertia to find the best solution in the optimal area. Intelligent sampling finds the optimal area to search for optimal solution. The RAPID approach employs Intelligent Sampling to reduce response time significantly and hence improve the efficiency of the proposed hybrid GW-IABO algorithm. Thus, the proposed hybrid algorithm effectively offloads to reduce the response time.

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Section: Fog Offloading Decision-making Approachesmentioning

confidence: 99%

Mobility-Aware Intelligent Sampling and Improved African Buffalo Optimization-assisted Offloading in 5G-Fog Environment

2024

Preprint

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“…The work in [35] introduced a task offloading scheme based on D2D collaboration. It was shown in [36] that collaborative computation offloading among MDs in an energy harvesting scenario can prolong the network lifetime. Machine learning approaches have also been extensively proposed in the literature for computation offloading in MEC.…”

Section: Related Workmentioning

confidence: 99%

“…Proposition 3. Utilizing the optimal values of transmission powers (36)(37)(38), The objective function given by ( 34) is convex. Proof: The proof is given in Appendix C.…”

Section: B Joint Power Allocation and Task Assignmentmentioning

confidence: 99%

Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mmWave Mobile Edge Computing

Khazali,

Bozorgchenani,

Tarchi

et al. 2023

IEEE Access

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In this paper, we investigate the cooperation of idle computation resources of nearby mobile devices in mobile edge computing (MEC) systems, in which each mobile device jointly offloads computation tasks to a MEC node and a nearby mobile device by employing non-orthogonal multiple access (NOMA) in a millimeter-wave (mmWave) heterogeneous network. In this setup, the nearby device acts as a helper by performing local computation and offloading data simultaneously to the MEC system. We formulate an optimization problem for joint taSk assignmenT, poweR allOcation and Node Grouping (STRONG) aiming to minimize the energy consumption of devices (i.e., user and helper devices). To tackle this problem, we present a two-step solution. First, we adopt a low complexity search-based algorithm for both helper and MEC server selection. Next, considering the non-convex nature of the energy minimization problem, we develop algorithms that provide sub-optimal solutions for power allocation to the helper and MEC server, as well as the offloading task ratios between them. Numerical results are provided to validate the effectiveness of our proposed algorithms. The results not only validate the efficiency of our approach but also demonstrate the superiority of our cooperative NOMA-based MEC scenario compared to methods without cooperation and other cooperation-based scenarios.

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“…Finally, a heuristic algorithm is used to schedule tasks on mobile devices and remote cloud resources based on task priority. To evaluate the proposed method, the sources available in 43,44 and random data have been used. By evaluating the method, the amount of energy consumption decreases and the number of completed tasks increases.…”

Section: Heuristic Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

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The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

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An energy harvesting solution for computation offloading in Fog Computing networks

Cited by 21 publications

References 41 publications

Mobility-Aware Intelligent Sampling and Improved African Buffalo Optimization-assisted Offloading in 5G-Fog Environment

Mobility-Aware Intelligent Sampling and Improved African Buffalo Optimization-assisted Offloading in 5G-Fog Environment

Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mmWave Mobile Edge Computing

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

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