In recent years, cloud computing has emerged as a cost-effective way to deliver on-demand and metered computing resources. In a cloud, elasticity of resource usage is typically realized through the “on-demand” provision principle supported by the “pay-as-you-go” business model. However, little has been investigated into elasticity of algorithm for cloud computing. This paper introduces a novel elastic algorithm (EA) in which the computation itself is organized in a “pay-as-you-go” fashion. In contrast to conventional algorithms, where computation is a deterministic process that only produces an “all-or-nothing” result, an EA generates a set of approximate results corresponding to its resource consumption. As more resources are consumed, better results can be derived. In this sense, quality of the algorithm is elastic to its resource consumption. The desirable properties for EA are formalized, and ambitious agenda for future research is provided in this area and propose several challenges.
GUO Yi-ke, HAN Rui
. Elastic Algorithm in Cloud Computing: Overview and Prospect[J]. Journal of Shanghai University, 2013
, 19(1)
: 1
-4
.
DOI: 10.3969/j.issn.1007-2861.2013.01.001
[1] Han R, Guo L, Guo Y, et al. A deployment platform for dynamically scaling applications in the cloud [C]// The 3rd IEEE International Conference on Cloud Computing
Technology and Science. 2011: 506-510.
[2] Han R, Ghanem M M, Guo L, et al. Enabling costaware and adaptive elasticity of multi-tier cloud applications [J]. Future Generation Computer Systems, 2012, DOI: 10.1016/j.future.2012.05.018.
[3] Han R, Guo L, Ghanem M M, et al. Lightweight resource scaling for cloud applications [C]// The 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2012: 644-651.
[4] Poladian V, Sousa J P, Garlan D, et al. Dynamic configuration of resource-aware services [C]// The 26th International Conference on Software Engineering. 2004: 604-613.
[5] Gaber M M, Yu P S. A framework for resourceaware knowledge discovery in data streams: a holistic approach with its application to clustering [C]// The 2006 ACM Symposium on Applied Computing. 2006: 649-656.
[6] Dean T L. Intractability and time-dependent planning [C]// The 7th National Conference on Artificial Intelligence. 1989: 245-266.
[7] Dean T, Boddy M. An analysis of time-dependent planning [C]// The 7th National Conference on Artificial Intelligence. 1988: 49-54.
[8] Zilberstein S. Using anytime algorithms in intelligent systems [J]. AI Magazine, 1996, 17: 73-83.
[9] Guo Y, Ghanem M, Han R. Does the cloud need new algorithms: an introduction to elastic algorithms [C]// The 4th International Conference on Cloud Computing. 2012: 66-73.
[10] Pharr M, Humphreys G. Physically based rendering: from theory to implementation [M]. San Fransisco: Morgan Kaufmann, 2004: 1-43.
