Abstract
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.
Highlights
Due to the high-speed development and popularization of the internet, network resource sharing has emerged; cloud computing is a service related to network resources, and in recent years, cloud computing has become well developed
This paper proposes a scheduling algorithm for the cloud computing system based on the driver of dynamic essential path (DDEP)
We propose a scheduling algorithm for the cloud computing system based on the driver of dynamic essential path to solve the problem of the scheduling result affected by the scheduling order change of each task node in the scheduling process
Summary
Due to the high-speed development and popularization of the internet, network resource sharing has emerged; cloud computing is a service related to network resources, and in recent years, cloud computing has become well developed. Most list-scheduling algorithms solve the independent task scheduling problem based on a homogeneous environment, which make the algorithms simple and feasible with low time complexity. Certain researchers have proposed many dependent task scheduling algorithms for heterogeneous systems These algorithms consider both the task node itself and the communication cost between task nodes, such as HEFT&CPOP [19],HEFT-Lookahead [20], CEFT [21], which sort all task nodes prior to the actual scheduling by the task graph itself. The main contributions of this paper are summarized as follows: we consider first the problem of scheduling order for all task nodes affected by the actual computation time (cost) and communication time (cost) of task node in the scheduling processing.
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