Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP-complete). Most existing task-scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth-aware algorithm for divisible task scheduling in cloudcomputing environments. A nonlinear programming model for the divisible task-scheduling problem under the bounded multi-port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth-aware task-scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair-based task-scheduling algorithm, the bandwidthonly task-scheduling algorithm, and the computation-only task-scheduling algorithm, the proposed algorithm (BATS) has better performance.
Combining the resources of the data center including CPU and memory, the work in REF proposes a task scheduling algorithm with bandwidth perception with a consideration of task scheduling of cloud computing.
Bandwidth-aware divisible task scheduling for cloud computing
Softw. Pract. Exp.
Softw. Pract. Exp.
Computer Science