pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems
Middleware '08 Proceedings of the ACM/IFIP/USENIX 9th International Middleware Conference
The cost of a cloud: research problems in data center networks
ACM SIGCOMM Computer Communication Review
True value: assessing and optimizing the cost of computing at the data center level
Proceedings of the 6th ACM conference on Computing frontiers
The Method and Tool of Cost Analysis for Cloud Computing
CLOUD '09 Proceedings of the 2009 IEEE International Conference on Cloud Computing
Characterizing cloud computing hardware reliability
Proceedings of the 1st ACM symposium on Cloud computing
On energy management, load balancing and replication
ACM SIGMOD Record
A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers
IEEE Transactions on Services Computing
Towards more effective utilization of computer systems
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Reducing electricity cost through virtual machine placement in high performance computing clouds
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing
Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations
Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture
Concurrency and Computation: Practice & Experience
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
| Hi-index | 0.00 |
Cloud providers and organizations with a large IT infrastructure manage evolving sets of hardware resources that are subject to continual change. As existing computing assets age, newer, more capable and more efficient ones are generally acquired. Significant variability of hardware components leads to inefficient use of computing assets within the organization. We claim that only a detailed understanding of the whole infrastructure will lead to significant optimizations and savings. In this paper we report results on a dataset of 1,171 assets from two different data centers, on which we present a thorough analysis of how the costs of running a computing asset are related to its resource capacity (i.e., CPU and RAM). This analysis is formalized in a cost model that could be used by organizations to make an optimal decision with regards to which computing assets should migrate their workload (i.e. should be disconnected or discarded) and which ones should receive such workload.