Abstract
To address the issue of cloud mixed workloads scheduling which might lead to system load imbalance and efficiency degradation in cloud computing, a novel cloud task staggering peak scheduling policy based on the task types and the resource load status is proposed. First, based on different task characteristics, the task sequences submitted by the user are divided into queues of different types by the fuzzy clustering algorithm. Second, the Performance Counters (PMC) mechanism is introduced to dynamically monitor the load status of resource nodes and respectively sort the resources by the metrics of Central Processing Unit (CPU), memory, and input/output (I/O) load size, so as to reduce the candidate resources. Finally, the task sequences of specific type are scheduled for the corresponding light loaded resources, and the resources usage peak is staggered to achieve load balancing. The experimental results show that the proposed policy can balance loads and improve the system efficiency effectively and reduce the resource usage cost when the system is in the presence of mixed workloads.
Highlights
Cloud computing [1,2,3] is an emerging business application providing on-demand access, ubiquitous access characteristics, and elastically scalable information technology (IT) resource usage [4].A variety of applications [5,6] have been deployed in cloud computing platforms, some application tasks have higher Central Processing Unit (CPU) requirements, and some require higher storage, while others require frequentI/O access or larger bandwidth resources
Based on the Performance Counters (PMC) mechanism, this paper monitors the number of resources activity events such as CPU (PCPU ), Mem P Mem, Disk ( P Disk ), and Net ( P Net ) triggered by the execution of Virtual machines (VMs), and respectively records the PMC event set associated with the specific resource device
This paper proposes the Staggering Peak Scheduling by Load-aware Model (SPSLAM) algorithm based on the analyzed task sequences classification and resources load sorting to make full use of these idle resources in cloud computing
Summary
Cloud computing [1,2,3] is an emerging business application providing on-demand access, ubiquitous access characteristics, and elastically scalable information technology (IT) resource usage [4]. The relevant research results of task scheduling have showed that: (1) Different types of tasks (compute-intensive, memory-intensive and data-intensive) have significant differences in resource requirements [10,11]. 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]. We propose a Staggering Peak Scheduling by Load-aware Model (SPSLAM) when researching the task scheduling problem of cloud mixed workloads. We develop a policy SPSLAM from the perspective of task types and resource load for mixed workloads and by doing so, the system can realize load balancing and improve efficiency.
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