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Author Varman, Peter ♦ Gulati, Ajay ♦ Shanmuganathan, Ganesha
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Cloud computing ♦ Demand-based allocation ♦ Distributed algorithm ♦ Resource management
Abstract Current public cloud offerings sell capacity in the form of pre-defined virtual machine (VM) configurations to their tenants. Typically this means that tenants must purchase individual VM configurations based on the peak demands of the applications, or be restricted to only scale-out applications that can share a pool of VMs. This diminishes the value proposition of moving to a public cloud as compared to server consolidation in a private virtualized datacenter, where one gets the benefits of statistical multiplexing between VMs belonging to the same or different applications. Ideally one would like to enable a cloud tenant to buy capacity in bulk and benefit from statistical multiplexing among its workloads. This requires the purchased capacity to be dynamically and transparently allocated among the tenant's VMs that may be running on different servers, even across datacenters. In this paper, we propose two novel algorithms called BPX and DBS that are able to provide the cloud customer with the abstraction of buying bulk capacity. These algorithms dynamically allocate the bulk capacity purchased by a customer between its VMs based on their individual demands and user-set importance. Our algorithms are highly scalable and are designed to work in a large-scale distributed environment. We implemented a prototype of BPX as part of VMware's management software and showed that BPX is able to closely mimic the behavior of a centralized allocator in a distributed manner.
Description Affiliation: VMware Inc, Palo Alto, CA, USA (Shanmuganathan, Ganesha; Gulati, Ajay) || Rice University, Houston, TX, USA (Varman, Peter)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-10
Publisher Place New York
Journal ACM SIGMETRICS Performance Evaluation Review (PERV)
Volume Number 41
Issue Number 1
Page Count 14
Starting Page 67
Ending Page 80


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Source: ACM Digital Library