Task scheduling is essential to enhance the performance of large scale collaborative and distributed e-business and e-science applications. A typical such application comprises multiple communicating tasks to be executed on virtualized resources also called as virtual machines (VMs). However, scheduling multiple tasks to VMs is non-trivial and is proven to be NP-Complete. The primary agenda of any scheduling technique is to reduce the makespan, which reflects the completion time of the exit task. Focusing on makespan reduction for scheduling multiple tasks across heterogeneous VMs, in this paper, we propose a model called H3CSA based on a meta-heuristic crow search algorithm (CSA). In order to assess the performance of H3CSA, its performance is compared with baselines such as HEFT-B, HEFT-T, HEFT-L, a genetic algorithm based technique (N-GA) and particle swarm optimization based technique (PPSO). Simulation results show improved performance of H3CSA with respect to N-GA and PPSO for smaller applications. However, considering sizeable applications H3CSA shows comparable performance with respect to N-GA and improved results for PPSO.

中文翻译：

---

H3CSA：一种面向云环境的完工时间感知任务调度技术

任务调度对于提高大规模协作和分布式电子商务和电子科学应用程序的性能至关重要。典型的此类应用程序包括要在也称为虚拟机 (VM) 的虚拟化资源上执行的多个通信任务。但是，将多个任务调度到 VM 并非易事，并且已被证明是 NP-Complete 的。任何调度技术的主要议程都是减少makespan，它反映了退出任务的完成时间。在本文中，我们着眼于减少跨异构 VM 调度多个任务的完工时间，提出了一种基于元启发式乌鸦搜索算法 (CSA) 的称为H3CSA的模型。为了评估H3CSA的性能，将其性能与 HEFT-B、HEFT-T、HEFT-L、基于遗传算法的技术 (N-GA) 和基于粒子群优化的技术 (PPSO) 等基线进行比较。仿真结果表明H3CSA相对于 N-GA 和 PPSO 的较小应用的性能有所提高。然而，考虑到相当大的应用，H3CSA显示出与 N-GA 相当的性能和 PPSO 的改进结果。