Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139875
Title: Towards efficient resource allocation for heterogeneous workloads in IaaS clouds
Authors: Wei, Lei
Foh, Chuan Heng
He, Bingsheng
Cai, Jianfei
Keywords: Engineering::Computer science and engineering
Issue Date: 2015
Source: Wei, L., Foh, C. H., He, B., & Cai, J. (2018). Towards efficient resource allocation for heterogeneous workloads in IaaS clouds. IEEE Transactions on Cloud Computing, 6(1), 264-275. doi:10.1109/TCC.2015.2481400
Journal: IEEE Transactions on Cloud Computing
Abstract: Infrastructure-as-a-service (IaaS) cloud technology has attracted much attention from users who have demands on large amounts of computing resources. Current IaaS clouds provision resources in terms of virtual machines (VMs) with homogeneous resource configurations where different types of resources in VMs have similar share of the capacity in a physical machine (PM). However, most user jobs demand different amounts for different resources. For instance,high-performance-computing jobs require more CPU cores while big data processing applications require more memory. The existing homogeneous resource allocation mechanisms cause resource starvation where dominant resources are starved while non-dominant resources are wasted. To overcome this issue, we propose a heterogeneous resource allocation approach, called skewness-avoidance multi-resource allocation (SAMR), to allocate resource according to diversified requirements on different types of resources. Our solution includes a VM allocation algorithm to ensure heterogeneous workloads are allocated appropriately to avoid skewed resource utilization in PMs, and a model-based approach to estimate the appropriate number of active PMs to operate SAMR. We show relatively low complexity for our model-based approach for practical operation and accurate estimation. Extensive simulation results show the effectiveness of SAMR and the performance advantages over its counterparts.
URI: https://hdl.handle.net/10356/139875
ISSN: 2168-7161
DOI: 10.1109/TCC.2015.2481400
Rights: © 2015 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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