Towards Efficient Resource Allocation for Heterogeneous Workloads in IaaS Clouds - 2018


Infrastructure-as-a-service (IaaS) cloud technology has attracted much attention from users who have demands on massive 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 an exceedingly physical machine (PM). However, most user jobs demand completely different amounts for different resources. Maybe,high-performance-computing jobs need more CPU cores whereas massive information processing applications need additional memory. The existing homogeneous resource allocation mechanisms cause resource starvation where dominant resources are starved whereas non-dominant resources are wasted. To overcome this issue, we propose a heterogeneous resource allocation approach, known as skewness-avoidance multi-resource allocation (SAMR), to allocate resource in line with diversified necessities on different sorts of resources. Our solution includes a VM allocation algorithm to confirm heterogeneous workloads are allotted appropriately to avoid skewed resource utilization in PMs, and a model-based approach to estimate the suitable range of active PMs to operate SAMR. We show relatively low complexity for our model-primarily based approach for practical operation and correct estimation. In depth simulation results show the effectiveness of SAMR and the performance benefits over its counterparts.

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