Paper Info
Title
Cost-Efficient and Resilient Job Life-Cycle Management on Hybrid Clouds
Abstract
Cloud infrastructure offers democratized access to on-demand computing resources for scaling applications beyond captive local servers. While on-demand, fixed-price Virtual Machines (VMs) are popular, the availability of cheaper, but less reliable, spot VMs from cloud providers presents an opportunity to reduce the cost of hosting cloud applications. Our work addresses the issue of effective and economic use of hybrid cloud resources for planning job executions with deadline constraints. We propose strategies to manage a job's life-cycle on spot and on on-demand VMs to minimize the total dollar cost while assuring completion. With the foundation of stochastic optimization, our reusable table-based algorithm (RTBA) decides when to instantiate VMs, at what bid prices, when to use local machines, and when to checkpoint and migrate the job between these resources, with the goal of completing the job on time and with the minimum cost. In addition, three simpler heuristics are proposed as comparison. Our evaluation using historical spot prices for the Amazon EC2 market shows that RTBA on an average reduces the cost by 72%, compared to running on only on-demand VMs. It is also robust to fluctuations in spot prices. The heuristic, H3, often approaches RTBA in performance and may prove adequate for ad hoc jobs due to its simplicity.
Year
DOI
Venue
2014
10.1109/IPDPS.2014.43
IPDPS
Keywords
Field
DocType
software management,captive local servers,cost-efficient job life-cycle management,hybrid cloud resources,rtba,checkpointing,reusable table-based algorithm,total dollar cost minimization,amazon ec2 market,virtual machines,cloud infrastructure,resilient job life-cycle management,cloud providers,on-demand fixed-price virtual machines,stochastic optimization,historical spot prices,resource management,job execution planning,hybrid clouds,heuristics,reliability,checkpoint,cloud computing,cloud computing, job scheduling, reliability, resource management, hybrid clouds, spot markets,on-demand computing resources,local machines,spot vms,spot markets,stochastic programming,job scheduling,cost reduction,mathematical model,stochastic processes,pricing,servers
Resource management,Virtual machine,Spot contract,Computer science,Server,Parallel computing,Heuristics,Job scheduler,Cloud computing,Cost efficiency,Distributed computing
Conference
ISSN
Citations 
PageRank 
1530-2075
10
0.49
References 
Authors
16
2
Name
Order
Citations
PageRank
Hsuan-Yi Chu1100.83
Yogesh Simmhan2513.05