Amazon S3 for science grids: a viable solution?
DADC '08 Proceedings of the 2008 international workshop on Data-aware distributed computing
Empirical evaluation of latency-sensitive application performance in the cloud
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
The impact of virtualization on network performance of amazon EC2 data center
INFOCOM'10 Proceedings of the 29th conference on Information communications
CloudCmp: comparing public cloud providers
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Heterogeneity-aware resource allocation and scheduling in the cloud
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
Accelerating the cloud with heterogeneous computing
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
More for your money: exploiting performance heterogeneity in public clouds
Proceedings of the Third ACM Symposium on Cloud Computing
Bobtail: avoiding long tails in the cloud
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Communications of the ACM
Queue - Distributed Computing
Ginseng: market-driven memory allocation
Proceedings of the 10th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
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Cloud computing providers might start with near-homogeneous hardware environment. Over time, the homogeneous environment will most likely evolve into heterogeneous one because of possible upgrades and replacement of outdated hardware. In turn, the hardware heterogeneity will result into performance variation. In this paper, we look into the hardware heterogeneity and the corresponding performance variation within the same instance type of Amazon Elastic Compute Cloud (Amazon EC2). Standard large instance is selected as the example. We find out that there exist three different subtypes of hardware configuration in the standard large instance. Through a set of detailed micro-benchmark and application-level benchmark measurements, we observe that the performance variation within the same subtype of instance is relatively small, whilst the variation between different sub-types can be up to 60%. By selecting better-performing instances to complete the same task, end-users of Amazon EC2 platform can achieve up to 30% cost saving.