Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
GRENCHMARK: A Framework for Analyzing, Testing, and Comparing Grids
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
Queue - Virtualization
Amazon S3 for science grids: a viable solution?
DADC '08 Proceedings of the 2008 international workshop on Data-aware distributed computing
How a consumer can measure elasticity for cloud platforms
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
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)
On a Catalogue of Metrics for Evaluating Commercial Cloud Services
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Hardware-in-the-loop simulation for automated benchmarking of cloud infrastructures
Proceedings of the Winter Simulation Conference
Configurable performance analysis and evaluation framework for cloud systems
International Journal of Information and Communication Technology
Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach
Journal of Grid Computing
Towards software performance engineering for multicore and manycore systems
ACM SIGMETRICS Performance Evaluation Review
Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing
Journal of Intelligent Manufacturing
The analysis of service provider-user coordination for resource allocation in cloud computing
Information-Knowledge-Systems Management
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Cloud computing has emerged as a new technology that provides large amounts of computing and data storage capacity to its users with a promise of increased scalability, high availability, and reduced administration and maintenance costs. As the use of cloud computing environments increases, it becomes crucial to understand the performance of these environments. So, it is of great importance to assess the performance of computing clouds in terms of various metrics, such as the overhead of acquiring and releasing the virtual computing resources, and other virtualization and network communications overheads. To address these issues, we have designed and implemented C-Meter, which is a portable, extensible, and easy-to-use framework for generating and submitting test workloads to computing clouds. In this paper, first we state the requirements for frameworks to assess the performance of computing clouds. Then, we present the architecture of the C-Meter framework and discuss several cloud resource management alternatives. Finally, we present ourearly experiences with C-Meter in Amazon EC2. We show how C-Meter can be used for assessing the overhead of acquiring and releasing the virtual computing resources, for comparing different configurations, and for evaluating different scheduling algorithms.