Quality of context and grid computing: An user satisfaction and system performance trade off model

The trend of grid computing available on the Internet has generated challenges to the allocation of resources provided by this type of environment. Considering the user's satisfaction, many of these challenges can be solved by the quality of experience paradigm that takes into account several context parameters. Considering the resources utilization, the quality of context paradigm can be used to improve the system performance. Once that a grid platform is a context sensitive system, so its possible to deal jointly with user's satisfaction and system performance. In this sense, the quality of context can be used to process the context informations and provide several management decisions. So, in this paper we propose a model to obtain runtime improvement for individual users, and improve the global system performance using quality of context. Experimental results shows improvements up to 53% in the runtime jobs, and some improvement in the global environment performance when applied our model in a grid scenario.

[1]  Borja Sotomayor,et al.  Combining batch execution and leasing using virtual machines , 2008, HPDC '08.

[2]  P. Sadayappan,et al.  Selective Reservation Strategies for Backfill Job Scheduling , 2002, JSSPP.

[3]  Giuseppe M. L. Sarnè,et al.  Improving Grid Nodes Coalitions by Using Reputation , 2014, IDC.

[4]  Min Chen,et al.  Fairness Resource Allocation in Blind Wireless Multimedia Communications , 2013, IEEE Transactions on Multimedia.

[5]  Fabien Hermenier,et al.  Cluster-wide context switch of virtualized jobs , 2010, HPDC '10.

[6]  Markus Fiedler,et al.  A generic quantitative relationship between quality of experience and quality of service , 2010, IEEE Network.

[7]  Gregory D. Abowd,et al.  Providing architectural support for building context-aware applications , 2000 .

[8]  Carl Eklund,et al.  Quality of service support in IEEE 802.16 networks , 2006, IEEE Network.

[9]  Giuseppe M. L. Sarnè,et al.  An Agent Based Negotiation Protocol for Cloud Service Level Agreements , 2014, 2014 IEEE 23rd International WETICE Conference.

[10]  Michael Krause,et al.  Challenges in Modelling and Using Quality of Context (QoC) , 2005, MATA.

[11]  Alexander L. Stolyar,et al.  Maximizing Queueing Network Utility Subject to Stability: Greedy Primal-Dual Algorithm , 2005, Queueing Syst. Theory Appl..

[12]  Matthias Baumgarten,et al.  Measuring the Probability of Correctness of Contextual Information in Context Aware Systems , 2009, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.

[13]  Terje Aven,et al.  A conceptual framework for linking risk and the elements of the data-information-knowledge-wisdom (DIKW) hierarchy , 2013, Reliab. Eng. Syst. Saf..

[14]  Mario A. R. Dantas,et al.  Toward assessing Quality of Context parameters in a ubiquitous assisted environment , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[15]  John A. Schormans,et al.  Evaluating QoE in Cognitive Radio Networks for Improved Network and User Performance , 2013, IEEE Communications Letters.