From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

• DOI: 10.1016/j.future.2013.12.005
• Publication Date: 2013-12-18T20:00:48Z
• Authors: Javier Conejero; Blanca Caminero; Carmen Carrión; Luis Tomás
• Keywords: Rescheduling; Datavetenskap (datalogi); Computer Sciences; Service level agreement; Reservation; Scheduling in advance; 0202 electrical engineering, electronic engineering, information engineering; QoS; 02 engineering and technology; Grid

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources). The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities. This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources' usage.