Abstract
The word grid computing is originated from a new computing infrastructure for scientific research in various areas. Cloud is the commercial version of grid. There are various challenges in Grid computing which are applicable in cloud also. The main challenges are resource management, job scheduling, security problems, fault tolerance, virtualisation etc. In the above current research challenges, job scheduling is the fundamental issue in achieving high performance in grid and cloud computing systems. However, it is a big challenge for efficient scheduling algorithm design and implementation. In scheduling algorithms, some are cost effective and some others are performance based. CPU intensive problems are very much increased and the research on effective scheduling is going deeply and widely. In this scenario, the scheduling problem is very interesting and this is the perfect time to develop a new scheduling strategy. The computing power in the grid is scattered around the globe. Any resource owner can put his own cost and other several policies for renting his resource. In such an environment the scheduling will be difficult and price variant. So developing an optimum scheduling algorithm with optimised cost and reduced waiting time is always better than an optimum duration or time based approach. Utilising the resources effectively in a minimum amount of cost and waiting time of jobs are always a better approach. The profile based selection is another approach in grid. In this paper we discusses two algorithms developed by us. We also portraits the possibilities of the same in cloud environment.
Highlights
In scheduling algorithms, some are cost-effective and some others are performance based
A more advanced grid technology may consist of complex job scheduling algorithms which are capable of automatically performing these scheduling tasks on behalf of the user[4]
It is better to execute such jobs locally[7] because there is no advantage in migrating such jobs when we consider cost and network delay. Another objective is to develop a jobprofile based selection of scheduling algorithms that consist of TimeLine strategy too
Summary
When we consider the actual grid environment, the resource demand and the availability jointly determine the cost of computing power. Bimal V O. et al, International Journal of Advanced Research in Computer Science, 9 (1), Jan-Feb 2018,456-460 and computational requirement but the objective of timeline algorithm is to reduce the waiting time of jobs by migrating jobs to other countries. The simple migration techniques may not be an efficient strategy because many counties suffering from resource pool saturation. It is better to execute such jobs locally[7] because there is no advantage in migrating such jobs when we consider cost and network delay. As discussed above, another objective is to develop a jobprofile based selection of scheduling algorithms that consist of TimeLine strategy too. The average processing time required is calculated from the total resource capacity of particular grid[9]
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