Randomized routing of virtual machines in IaaS data centers.

Hadi Khani,Hamed Khanmirza

doi:10.7717/peerj-cs.211

Hadi Khani, Hamed Khanmirza

Open Access

https://doi.org/10.7717/peerj-cs.211

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Abstract

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.

Highlights

Infrastructure-as-a-Service (IaaS) cloud providers (CPs), such as Amazon, Google and Microsoft, have huge data centers to provide on demand virtual machines (VMs) to their customers
The values chosen may be quite applicable to small- to large-sized CPs data centers that try to keep the utilization of their servers as high as possible while guarantee a minimum QoS for the users
Effective resource management is a major challenge for the leading CPs (e.g., Google, Microsoft, Amazon)

Summary

Introduction

Infrastructure-as-a-Service (IaaS) cloud providers (CPs), such as Amazon, Google and Microsoft, have huge data centers to provide on demand virtual machines (VMs) to their customers. The CP has a variety of challenges, such as higher resource utilization, less cooling expenses and lower operation expenses. All of these efficiency metrics are positively correlated. Less power consumption means less operational expense, less cooling bills and higher utilization in the data center. This lets us choose the power consumption as the key metric representing others. The resource management of CP has the chance to revise the initial placement of VMs onto PMs by live migrating techniques or dynamic consolidation.

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