With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to ed...With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.展开更多
This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic dir...This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution of a workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear programming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.展开更多
基金Supported by the National Natural Science Foundation of China(62102292)the Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology) of China(HBIRL202103,HBIRL202204)+1 种基金Science Foundation Research Project of Wuhan Institute of Technology of China(K202035)Graduate Innovative Fund of Wuhan Institute of Technology of China(CX2021265)。
文摘With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.
基金Project partially supported by the Polish National Science Center(No.DEC-2012/07/B/ST6/01516)
文摘This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution of a workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear programming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.
