Devi D Chitra, Uthariaraj V Rhymend
Ramanujam Computing Centre, Anna University, Chennai 600 025, India.
ScientificWorldJournal. 2016;2016:3896065. doi: 10.1155/2016/3896065. Epub 2016 Feb 3.
Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.
云计算运用调度和负载均衡的概念，将任务迁移到未充分利用的虚拟机上，以有效共享资源。云计算环境中非抢占式任务的调度是一种不可恢复的限制，因此必须在初始放置时就将其分配到最合适的虚拟机上。实际上，到达的作业由多个相互依赖的任务组成，它们可能在多个虚拟机中或同一虚拟机的多个核心上执行独立任务。此外，作业在服务器运行期间以不同的随机间隔在各种负载条件下到达。通过静态或动态调度将任务分配到适当的资源，对参与的异构资源进行管理，以使云计算更高效，从而提高用户满意度。这项工作的目标是通过考虑每个虚拟机（VM）的能力、每个请求作业的任务长度以及多个任务的相互依赖性，来引入和评估所提出的调度和负载均衡算法。通过与现有方法进行比较，研究了所提算法的性能。
