Nithiapidary Muthuvelu

An Adaptive And Parameterized Job Grouping Algorithm For Scheduling Grid Jobs

An unorganized deployment of grid applications with a large amount of fine-grain jobs would let the communication overhead dominate the overall processing time, resulting in a low computation-communication ratio. Grids dynamic nature complicates the planning of the job scheduling activity for minimizing the application processing time. This paper presents a grid job scheduling algorithm, based on a parameterized job grouping strategy, which is adaptive to the runtime grid environment. Jobs are grouped based on the job processing requirements, resource policies, network conditions and users QoS requirements. Simulations using the GridSim toolkit reveal that the algorithm reduces the overall application processing time significantly.

Task granularity policies for deploying bag-of-task applications on global grids

Deploying lightweight tasks individually on grid resources would lead to a situation where communication overhead dominates the overall application processing time. The communication overhead can be reduced if we group the lightweight tasks at the meta-scheduler before the deployment. However, there is a necessity to limit the number of tasks in a group in order to utilise the resources and the interconnecting network in an optimal manner. In this paper, we propose policies and approaches to decide the granularity of a task group that obeys the task processing requirements and resource-network utilisation constraints while satisfying the users QoS requirements. Experiments on bag-of-task applications reveal that the proposed policies and approaches lead towards an economical and efficient way of grid utilisation. Highlights? Grouping the fine-grain grid tasks highly reduces the application processing time. ? QoS and the resource-network utilisation constrains affect the size of a task group. ? We present batch resizing policies and techniques to create the task groups. ? Our strategies support both parametric and non-parametric sweep applications. ? Our task group deployment increases the resource utilisation.

On-Line task granularity adaptation for dynamic grid applications

Deploying lightweight tasks on grid resources would let the communication overhead dominate the overall application processing time Our aim is to increase the resulting computation-communication ratio by adjusting the task granularity at the grid scheduler We propose an on-line scheduling algorithm which performs task grouping to support an unlimited number of user tasks, arriving at the scheduler at runtime The algorithm decides the task granularity based on the dynamic nature of a grid environment: task processing requirements; resource-network utilisation constraints; and users QoS requirements Simulation results reveal that our algorithm reduces the overall application processing time and communication overhead significantly while satisfying the runtime constraints set by the users and the resources.

Batch Resizing Policies and Techniques for Fine-Grain Grid Tasks: The Nuts and Bolts

The overhead of processing fine-grain tasks on a grid induces the need for batch processing or task group deployment in order to minimise overall application turnaround time. When deciding the granularity of a batch, the processing requirements of each task should be considered as well as the utilisation constraints of the interconnecting network and the designated resources. However, the dynamic nature of a grid requires the batch size to be adaptable to the latest grid status. In this paper, we describe the policies and the specific techniques involved in the batch resizing process. We explain the nuts and bolts of these techniques in order to maximise the resulting benefits of batch processing. We conduct experiments to determine the nature of the policies and techniques in response to a real grid environment. The techniques are further investigated to highlight the important parameters for obtaining the appropriate task granularity for a grid resource.

QoS-based Task Group Deployment on Grid by Learning the Performance Data

Overhead of executing fine-grain tasks on computational grids led to task group or batch deployment in which a batch is resized according to the characteristics of the tasks, designated resource, and the interconnecting network. An economic grid demands an application to be processed within the given budget and deadline, referred to as the quality of service (QoS) requirements. In this paper, we increase the task success rate in an economic grid by optimally mapping the tasks to the resources prior to the batch deployment. The task-resource mapping (Advance QoS Planning) is decided based on QoS requirement and by mining the historical performance data of the application tasks using a genetic algorithm. The mapping is then used to assist in creating the task groups. Practical experiments are conducted to validate the proposed method and suggestions are given to implement our method in a cloud environment as well as to process real-time tasks.

An empirical study on secure communication for grid information service

Grid Information Service (GIS) is an entity that gathers the latest information about the grid resources. These resources information are then retrieved by the grid users or brokers prior to planning task deployment in a grid environment. Hence, the GIS frequently communicate with the resources for their latest status, and hardware and software specifications. This creates the need for timely and secure communication mechanisms between GIS and the resources. In this paper, an empirical study is conducted to evaluate the performance of Identity-based authenticated Key Exchange (IDKEX) and Secure Socket Layer (SSL) protocols for secure communications between GIS and grid resources. From the observation, ID-KEX protocol shows better performance in terms of time and space consumptions which appear to be more efficient in the presence of large number of resources.

A Comparison Study on Key Exchange-Authentication protocol

A key exchange protocol enables two parties to share a common key for encrypting a large amount of data. Authentication is an essential requirement prior to the key exchange process in order to prevent man-in-the-middle attack. It is important to understand the capabilities and performance of the existing key exchange protocols before employing the protocols in our applications. In this paper, we compare Secure Socket Layer, Secure Shell, and Identity-based key exchange protocols by quantifying the performance, complexity, and level of security of each protocol. Detailed experiments and observations are conducted to examine the protocols in terms of disk usage, computation time, and data transmission time. The analysis shows that the identity-based key exchange maintains similar security level as the other protocols, while conveying better performance. General Terms Security, protocol, cryptography.