Earticle

A Computational grid is highly useful computing infrastructure that enables effective access of resources to perform high performance computing wherein job management, efficient resource utilization are key grid service issues. To make grid environment greatly useful, an optimized scheduling system is essential as computational grid is often heterogeneous with complex environment. This complexity raises difficulties in gathering the load information of computational resources as well as coping up with user needs specifically when users dealt with jobs. Other existing fact of grid computing is that it is best known for handling batch job rather than interactive job submission. Interactive Job submission is useful in situations when a job requires human intervention to control the job result. This research paper tries to satisfy the requirements of the user by developing user driven scheduler. We have developed a scheduler here after referred as NGSched compatible with Globus and hence provides user on-demand and interactive access to grid resources. It adopts novel approach for scheduling multiple jobs by giving privileges of resource selection to the user. The proposed and implemented algorithm also satisfies the deadlines given by the user for job execution on the selected resources and balances the load on grid nodes. The proposed heuristic also calculates the share of each resource according to capability to handle load. Since this scheduler is user centric, it facilitates the user to monitor the job, cancel the job, schedule on selected resources, suspend and resume the job. Results obtained proved that our innovative algorithm is better and efficient.

High throughput computing (HTC) is of great importance in grid computing environments. HTC is aimed at minimizing the total makespan of all of the tasks submitted to the grid environment in long execution of the system. To achieve HTC in grids, suitable task scheduling algorithms should be applied to dispatch the submitted tasks to the computational resources appropriately. In this paper, a new task scheduling algorithm is proposed to assign the tasks to the grid resources with goal of minimizing the total makespan of the environment. The proposed algorithm uses genetic approach to find the most suitable match between the tasks and grid resources. The simulation results obtained from applying the proposed algorithm to schedule independent and sequential tasks to the grid resources demonstrate the applicability of the algorithm in grid environments.

Dynamic adaptation has become an important issue when designing and developing distributed applications in order to manage their Quality of Service (QoS). This is especially challenging when distributed applications run in environments in which resources vary unpredictably over time. To deal with fluctuations in resource availability and inherent heterogeneity of distributed environments requires dynamic adaptation of each application. This trend motivates the design of resource-aware applications ensuring a given performance level by adapting their behavior to changing contexts. To tune the use of resources, adaptive systems include the necessary mechanisms to modify applications' behavior. This paper presents a general distributed middleware for enabling behavior adaptation of distributed applications. The middleware combines application designer specification of alternative execution behaviors with information about the execution environment context for deciding when and how to adapt itself according to the available resources. A description language specifies applications' behavior and the description of their related resource use. The QoS management requires a common execution model for all applications. Simulations of the QoS management of heterogeneous applications illustrate our proposal and show the benefits. In addition, we discuss lessons learned from our experience.

Cloud Computing is a new and vast area that is impacting new business model and is heavily using the network as a platform. As the number of cloud services grow , it will be difficult to find the appropriate Cloud service suitable for our needs. We combine the service selection algorithm and ranking mechanism along with particle swarm optimization algorithm to find the appropriate service matching our needs. We are using Quality of Service as our main criteria. We consider travelling as our subject and take availability, reliability and price as our measure of Quality of Service . We use multi-objective optimization strategies to find the best Cloud service.

The optimization of scheduling problems is based on different criteria to optimize. One of the most important criteria is the minimization of completion time of the last task on the last machine called makespan. In this paper, we present a parallel algorithm for solving the permutation flow shop problem. Our algorithm is a basic parallel distributed algorithm deployed in a grid of computer (Grid’5000). The objective of this work is minimizing the total makespan of the tasks. Our algorithm uses the exact Branch and Bound method to find optimal solutions of the problem through the distribution of the tasks among the available processors. Computational results of our parallel algorithm using well known Taillard’s benchmarks, showed encouraging results. In particular, we succeeded to solve two new instances to optimality which had never been resolved before neither in sequential nor in parallel [29].