A probabilistic and adaptive scheduling algorithm using system-generated predictions for inter-grid resource sharing

Abstract

Rapid advancement and more readily availability of Grid technologies have encouraged many businesses and researchers to establish Virtual Organizations (VO) and make use of their available desktop resources to solve computing intensive problems. These VOs, however, work as disjointed and independent communities with no resource sharing between them. We, in previous work, have proposed a fully decentralized and reconfigurable Inter-Grid framework for resource sharing among such distributed and autonomous Grid systems (Rao et al. in ICCSA, 2006). The specific problem that underlies in such a collaborating Grids system is scheduling of resources as there is very little knowledge about availability of the resources due to the distributed and autonomous nature of the underlying Grid entities. In this paper, we propose a probabilistic and adaptive scheduling algorithm using systemgenerated predictions for Inter-Grid resource sharing keeping collaborating Grid systems autonomous and independent. We first use system-generated job runtime estimates without actually submitting jobs to the target Grid system. Then this job execution estimate is used to predict the job scheduling feasibility on the target system. Furthermore, our proposed algorithm adapted itself to the actual resource behavior and performance. Simulation results are presented to discuss the correctness and accuracy of our proposed algorithm.

Estimation of legacy application upgrade time using evolutionary approach

“…Different approaches such as [4], [5], [6] or [7] are thinking about declared problem mainly from the hardware perspective (working with wide range of metrics for particular hardware). We abstract the hardware configuration (it is basically required to upgrade all databases under same conditions) and look at the problem from different perspective.…”

Power-Aware Linear Programming based Scheduling for heterogeneous computer clusters

“…While determining the exact task execution time on a target machine remains a challenge, there exist several techniques that can be used to estimate an expected value for the task execution time (see Rao and Huh [27] and Seneviratne and Levy [29], for example). The policies considered in this paper exploit estimates on mean task execution times rather than exact execution times.…”

A funding and governing model for achieving sustainable growth of computing e-infrastructures

“…The work by Rao and Huh introduces a probabilistic and adaptive scheduling algorithm for intergrid resource sharing, using job estimates to predict the job scheduling feasibility on the target system [1]. A comparison between different grid resource provisioning mechanisms was presented by Assunção and Buyya [28].…”

“…The foundation for this has been set in the past few years. During that time, computing einfrastructures endured core advancements (e.g., grid computing technology, service-oriented computing [1][2][3][4]) and achieved a wide acceptance in an effort to build the global computing e-infrastructure. Many computing einfrastructures were initiated, and countless innovative hardware, concepts, middleware, and applications were introduced.…”