%0 Journal Article
%T Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid
%A Piyush Chauhan
%A Nitin
%J Journal of Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/202843
%X Complex problems consisting of interdependent subtasks are represented by a direct acyclic graph (DAG). Subtasks of this DAG are scheduled by the scheduler on various grid resources. Scheduling algorithms for grid strive to optimize the schedule. Nowadays a lot of grid resources are attached by P2P approach. Grid systems and P2P model both are newfangled distributed computing approaches. Combining P2P model and grid systems we get P2P grid systems. P2P grid systems require fully decentralized scheduling algorithm, which can schedule interreliant subtasks among nonuniform computational resources. Absence of central scheduler caused the need for decentralized scheduling algorithm. In this paper we have proposed scheduling algorithm which not only is fruitful in optimizing schedule but also does so in fully decentralized fashion. Hence, this unconventional approach suits well for P2P grid systems. Moreover, this algorithm takes accurate scheduling decisions depending on both computation cost and communication cost associated with DAGĄ¯s subtasks. 1. Introduction Splitting a huge job into subtasks yields interdependent subtasks. Once predecessor subtasks return results only then will the execution of successor subtask take place. To characterize a set of subtasks and their dependency on each other we can use directed acyclic graph (DAG). Nodes represent subtasks and dependencies are denoted by arc joining the two nodes. Most of the DAG tasks are highly computation and communication intensive. Intertask dependencies lead to a very complex scenario to find a solution in an efficient manner. Moreover, because of financial constraints most of the organizations do not own high-end computational resources like cluster of supercomputers. The grid provides a solution to get out of this situation. We can access computational resources available on the grid and schedule our DAG based task upon them. Scheduling is the method to shortlist nodes from the available computational resources and then assign tasks upon them in an efficient manner. A lot of scheduling algorithms [1] are in place to schedule tasks upon grid [2, 3]. However, they use either single server as central scheduler or metascheduler approach. Due to political causes, depending upon central scheduler in a grid computing environment is not viable. Problem with metascheduler takes place when no single cluster has adequate computational resources to execute the bulky job. Moreover, scalability and bottleneck problems are present in both meta- and central-scheduler approach. These shortcomings directed the
%U http://www.hindawi.com/journals/je/2014/202843/