Grid Computing has emerged as an important new field focusing on resource sharing. One of the most challenging issues in Grid Computing is efficient scheduling of tasks. In this paper, we propose a Load balancing algorithm for fair scheduling, and we compare it to other scheduling schemes such as the Earliest Deadline First, Simple Fair Task order, Adjusted Fair Task Order and Max Min Fair Sche...

Cloud computing is a new business model based on Internet and aims to provide information service for users. Resource scheduling is one of the key technologies of Cloud Computing. Analyzing the representative achievement of cloud computing resources scheduling, and then according to the problem that there are large gap between most algorithm task design and actual service needs, we present a ta...

job shop scheduling problem has significant importance in many researchingfields such as production management and programming and also combinedoptimizing. job shop scheduling problem includes two sub-problems: machineassignment and sequence operation performing. in this paper combination ofparticle swarm optimization algorithm (pso) and gravitational search algorithm(gsa) have been presented f...

Multiprocessor task scheduling is an important and computationally difficult problem. This paper proposes a comparison study of genetic algorithm and list scheduling algorithm. Both algorithms are naturally parallelizable but have heavy data dependencies. Based on experimental results, this paper presents a detailed analysis of the scalability, advantages and disadvantages of each algorithm. Mu...

Focusing on the problem that users would remove or delete the task at any time in cloud computing environment, a new scheduling algorithm for adapting to dynamic changes of user task is proposed. At first, the algorithm uses directed acyclic graph (DAG) to describe the association relation of cloud computing tasks. When a task needs to be removed, the algorithm utilizes a cost function to deter...

In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving...

In this paper, we propose a new solution for dynamic task scheduling in distributed environment. We argue that a function is existed in the items: execution time, the size of data and the algorithm, therefore we can deduce the execution time of a data mining task from the corresponding the size of data and algorithm. We adopt the sampling method for process the tasks scheduling in distributed d...

This paper reports on a novel decentralised technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a stochastic game formulation of these problems in which tasks have varying hard deadlines and processing requirements. We then introduce a new technique for approximating this game using a series of static potential games, before detailing a decentralised...

Distributed real-time applications require flexible and dynamic scheduling mechanisms to provide timeliness guarantees to application objects. In this paper we present a new scheduling algorithm that exploits the task laxities and the object importances to make effective scheduling decisions. The algorithm uses current timing and resource measurements to determine the feasibility of the tasks a...