A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Abstract

Abstract-In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPUintensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms.

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

“…The simulator and generator were the open source software developed by the Pegasus group at the University of Southern California. The experimental results were compared with fuzzy clustering and two level based task scheduling (FCTLBS) [11], multi-queue interlacing peak scheduling method (MIPSM) [13], and least connection (LC) [18].…”

“…(2) Heterogeneity of node resources and dynamic nature of load. When scheduling tasks of different types, the load size of resources such as CPU, memory, and I/O on the node resources at the current time needs to be considered [12,13]. Unreasonable task scheduling policy might lead to a load imbalance of resources, which not only…”

Mobile payments in global markets using biometrics and cloud

“…The biometric scheme was used to authenticate a user, and identity-based encryption scheme was used to encrypt and decrypt the data. Mechanisms proposed by Zuo et al 27,28 are very useful for scheduling the tasks by balancing the load and optimizing the performance, resource costs. With a large number of users, authentication mechanism using a single server will result in traffic bottlenecks and there will be a delay in scheduling them.…”

“…Using cloud, we can avoid this scenario by efficiently scheduling the jobs on multiple instances of a cloud. In another study, 27 authors proposed different scheduling mechanisms on the basis of task classifications and organizing the resources on the basis of the load. In another study, 28 a multiobjective optimization scheduling by considering both resource costs and performance of execution into account.…”

“…In another study, 28 a multiobjective optimization scheduling by considering both resource costs and performance of execution into account. Mechanisms proposed by Zuo et al 27,28 are very useful for scheduling the tasks by balancing the load and optimizing the performance, resource costs. In another study, 29 authors proposed a self-adaptive threshold-based dynamically weighted load evaluation method (termed SDWM) to evaluate the load state of the cloud resources.…”