Title :
Cloud Benchmarking for Performance
Author :
Varghese, Blesson ; Akgun, Ozgur ; Miguel, Ian ; Long Thai ; Barker, Adam
Author_Institution :
Sch. of Comput. Sci., Univ. of St. Andrews, St. Andrews, UK
Abstract :
How can applications be deployed on the cloud to achieve maximum performance? This question has become significant and challenging with the availability of a wide variety of Virtual Machines (VMs) with different performance capabilities in the cloud. The above question is addressed by proposing a six step benchmarking methodology in which a user provides a set of four weights that indicate how important each of the following groups: memory, processor, computation and storage are to the application that needs to be executed on the cloud. The weights along with cloud benchmarking data are used to generate a ranking of VMs that can maximise performance of the application. The rankings are validated through an empirical analysis using two case study applications, the first is a financial risk application and the second is a molecular dynamics simulation, which are both representative of workloads that can benefit from execution on the cloud. Both case studies validate the feasibility of the methodology and highlight that maximum performance can be achieved on the cloud by selecting the top ranked VMs produced by the methodology.
Keywords :
cloud computing; software performance evaluation; virtual machines; cloud benchmarking; financial risk application; molecular dynamics simulation; top ranked VM; virtual machines; Aggregates; Bandwidth; Benchmark testing; Cloud computing; Risk analysis; Standards; Virtual machining; cloud benchmarking; cloud performance; cloud ranking;
Conference_Titel :
Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on
Conference_Location :
Singapore
DOI :
10.1109/CloudCom.2014.28