A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Goudarzi, Mohammad; Zamani, Mehran; Haghighat, Abolfazl Toroghi

doi:10.1002/dac.3241

Cited by 32 publications

(19 citation statements)

References 26 publications

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“…After development completion, the algorithm strategy will no longer change. For example, the problems studied in [5][6][7][8] are static offloading decisions.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

et al. 2019

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In recent years, unmanned surface vehicles (USVs) have made important advances in civil, maritime, and military applications. With the continuous improvement of autonomy, the increasing complexity of tasks, and the emergence of various types of advanced sensors, higher requirements are imposed on the computing performance of USV clusters, especially for latency sensitive tasks. However, during the execution of marine operations, due to the relative movement of the USV cluster nodes and the network topology of the cluster, the wireless channel states are changing rapidly, and the computing resources of cluster nodes may be available or unavailable at any time. It is difficult to accurately predict in advance. Therefore, we propose an optimized offloading mechanism based on the classic multi-armed bandit (MAB) theory. This mechanism enables USV cluster nodes to dynamically make offloading decisions by learning the potential computing performance of their neighboring team nodes to minimize average computation task offloading delay. It is an optimized algorithm named Adaptive Upper Confidence Boundary (AUCB) algorithm, and corresponding simulations are designed to evaluate the performance. The algorithm enables the USV cluster to effectively adapt to the marine vehicular fog computing networks, balancing the trade-off between exploration and exploitation (EE). The simulation results show that the proposed algorithm can quickly converge to the optimal computation task offloading combination strategy under heavy and light input data loads.

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“…After development completion, the algorithm strategy will no longer change. For example, the problems studied in [5][6][7][8] are static offloading decisions.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

et al. 2019

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“…Some researchers focused on multisite offloading, in which the computation can be offloaded to multiple cloud servers. Goudarzi et al established a weighted model for multisite offloading in MCC in terms of execution time, energy consumption, and weighted cost and then proposed an offloading strategy algorithm based on GA [20]. ey used a reserve population besides the original GA population to diversify chromosomes and modified genetic operators to adapt to the multisite offloading problem.…”

Section: Related Workmentioning

confidence: 99%

“…At present, most studies (e.g., [18][19][20][21][22][23][24][25]) focus on static offloading decision algorithms, which assume that mobile cloud environments do not change. ese algorithms develop offloading strategies through program analysis during the application development phase, and the offloading strategies are fixed after the completion of the application development.…”

Section: Related Workmentioning

confidence: 99%

“…F OML represents the weighted total cost predicted by the online ML-based DMM. F OML − F e ≤ F th means the weighted total cost F OML predicted by the online ML-based DMM is good enough so that the online ML-based DMM is used as the current DMM (Lines [19][20]. Otherwise, the GA-based DMM is used (Lines 21-22).…”

Section: Cooperative Runtime Offloading Decision Algorithmmentioning

confidence: 99%

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Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Jin

Wang

Hua

2019

Mobile Information Systems

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Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.

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“…The power consumption for different models depends on the time and performance. The cloudlets application reduces the magnitude of energy requirement in support of the necessary signal transmission between the communication devices [14]. The communication anchored on the cloudlet schedules relies on the improved energy efficiency to an extent where the progressive estimation of the performance improves with increased innovation in the scheduling techniques in the platform.…”

Section: Experiments and Analysismentioning

confidence: 99%

Optimized Cloudlet Scheduling Based Method for Mobile Phones

Mohammad¹,

Li²

2019

Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturin

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The improvement in communication technology incorporates sophisticated electronic gadgets that rely on wireless technique to support the signal transmission from the emitter to the receiver points. The cloudlet scheduling method relies on the environment and task type to accomplish the scheduling process in an algorithm supported by the visual machine programs. The paper captures the cloudlet network gains attributed to the increased use of cloudlet scheduling technique in tablets and mobile phones operated within the communication and data delivery platform. The system relies on improved storage space and processing speed with which the systems works with reference of minimizing power usage. The platform sustains efficient programming of tasks through the application of meta-heuristic algorithms. The paper focuses on AES algorithm and how it solves a given task at every instance while optimizing system efficiency. The beauty of the algorithm is efficient energy consumption in the data transfer platform that adopts the analysis in response to the task completion based on first-come-first-serve. The concept sustains the sequences of service delivery in the computing networks. The algorithm anchored in the cloudlet platform facilitates the use of the internet to support data transmission between different users and the server that rely on the same computing infrastructure.

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A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Cited by 32 publications

References 26 publications

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Optimized Cloudlet Scheduling Based Method for Mobile Phones

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