30,222 research outputs found
Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
Grid computing is a distributed system with heterogeneous infrastructures. Resource
management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration
mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms
will enhance ACS in terms of exploration mechanism and solution refinement by
implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing
environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment,
performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan
A Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment
High-performance scheduling is critical to the achievement of application performance on the computational grid. New scheduling algorithms are in demand for addressing new concerns arising in the grid environment. One of the main phases of scheduling on a grid is related to the load balancing problem therefore having a high-performance method to deal with the load balancing problem is essential to obtain a satisfactory high-performance scheduling. This paper presents SAGE, a new high-performance method to cover the dynamic load balancing problem by means of a simulated annealing algorithm. Even though this problem has been addressed with several different approaches only one of these methods is related with simulated annealing algorithm. Preliminary results show that SAGE not only makes it possible to find a good solution to the problem (effectiveness) but also in a reasonable amount of time (efficiency)
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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