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Sökning: WFRF:(Abdi Somayeh)

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1.
  • Abdi, Somayeh, et al. (författare)
  • Cognitive and Time Predictable Task Scheduling in Edge-cloud Federation
  • 2022
  • Ingår i: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665499965
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we present a hierarchical model for time predictable task scheduling in edge-cloud computing architecture for industrial cyber-physical systems. Regarding the scheduling problem, we also investigate the common problem-solving approaches and discuss our preliminary plan to realize the proposed architecture. Furthermore, an Integer linear programming (ILP) model is proposed for task scheduling problem in the cloud layer. The model considers timing and security requirements of applications and the objective is to minimize the financial cost of their execution.
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2.
  • Abdi, Somayeh, et al. (författare)
  • Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm
  • 2024
  • Ingår i: Journal of Supercomputing. - : Springer. - 0920-8542 .- 1573-0484.
  • Tidskriftsartikel (refereegranskat)abstract
    • The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.
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3.
  • Abdi, Somayeh, et al. (författare)
  • Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm
  • 2024
  • Ingår i: International Conference on Cloud Computing and Services Science, CLOSER - Proceedings. - : Science and Technology Publications, Lda. - 9789897587016 ; , s. 159-166
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.
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4.
  • Abbafati, Cristiana, et al. (författare)
  • 2020
  • Tidskriftsartikel (refereegranskat)
  •  
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  • Resultat 1-4 av 4
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