Task graph Scheduling is a multi-objective optimization and NP-hard problem. In this paper a new algorithm on homogeneous multiprocessors systems is proposed. Basically, Scheduling algorithms are targeted to balance the two parameters of time and energy consumption. These two parameters are up to a certain limit in contrast with each other and improvement of one causes reduction in the other one. The problem is to achieve the trade-off between these two parameters. Pre-Scheduling algorithms are mainly aimed at modifying the structure of Task graph to gain optimal Scheduling.In the proposed algorithm the suitable number of processors for Scheduling the Task graph is computed. The idea of Nash equilibrium is mainly applied to compute the appropriate number of processors in such a way that the idle time of the processors is reduced while their processing power is increased. Also, considering the communication costs and interdependencies, the Tasks are merged as their earliest start time is reduced. In this way, the length of the critical path is reduced while the degree of parallelism is increased and ultimately the completion time is reduced. Our experimental result on a number of known benchmark graphs demonstrates the effect of our proposed algorithm.

Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper Scheduling method can lead to efficiency of resources by decreasing response time and costs.This research studies the existing approaches of Task Scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages.Afterwards, a compound algorithm is presented in order to allocate Tasks to resources properly and decrease runtime. In this paper we proposed a new method for Task Scheduling by learning automata (LA). This method where has named RAOLA is trained by historical information of Task execution on the cloud, then divide Task to many classes and evaluate them. Next, manage virtual machine for capture physical resources at any period based on rate of Task classes, such that improve efficiency of cloud network.

Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with Task tim has direct influence on cloud networks’ performance and efficiency. Presenting a proper Scheduling method can lead to efficiency of resources by decreasing response time and costs. This paper studies the existing approaches of Task Scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages. Afterwards, a compound algorithm is presented in order to allocate Tasks to resources properly and decrease runtime. The proposed algorithm is built according to conditions of compounding Minmin and Sufferage algorithms. In the proposed algorithm, Task allocation between machines takes place alternatively and with continuous change of Scheduling algorithms. The main idea of the proposed algorithm is to concentrate on the number of Tasks instead of the existing resources. The simulation results reveal that the proposed algorithm can achieve higher performance in decreasing response time.