An Analysis of Provisioning and Allocation Policies for Infrastructure-as-a-Service Clouds
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Improving scheduling performance using a q-learning-based leasing policy for clouds
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
QoS-Aware Revenue-Cost Optimization for Latency-Sensitive Services in IaaS Clouds
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
Modeling and performance analysis of large scale IaaS Clouds
Future Generation Computer Systems
Two levels autonomic resource management in virtualized IaaS
Future Generation Computer Systems
Cost-effective cloud HPC resource provisioning by building semi-elastic virtual clusters
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Scheduling jobs in the cloud using on-demand and reserved instances
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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Recent Infrastructure-as-a-Service offers, such as Amazon's EC2 cloud, provide virtualized on-demand computing resources on a pay-per-use model. From the user point of view, the cloud provides an inexhaustible supply of resources, which can be dynamically claimed and released. This drastically changes the problem of resource provisioning and job scheduling. This article presents how billing models can be exploited by provisioning strategies to find a trade-off between fast/expensive computations and slow/cheap ones for indepedent sequential jobs. We study a dozen strategies based on classic heuristics for online scheduling and bin-packing problems, with the double objective of minimizing the wait time (and hence the completion time) of jobs and the monetary cost of the rented resources. We simulate these strategies on real grid workloads in two cases. First, we use the workloads as a whole, which is representative of a large community of users sharing some common resources. Second, we use the workloads extracted for each individual user. These lighter workloads correspond to users submitting work independently from others and paying for their own resources. Our findings show that on large workloads, a little budget increase allows to achieve optimal wait time, while trade-off heuristics may be largely beneficial for individual users with lighter workloads.