User Centric Job Management In Grid Using Multiple Agents An Unified Approach

It has always been accepted that a community of HPC users requires interactive, on demand access to HPC resources. Grid middleware technologies have been aimed at the execution of the sequential batch jobs which is a dominating paradigm, while lacking support for interactive applications. The reason is that grid middleware was developed for compute intensive jobs, which may run for a long time before a result becomes available, which may sometimes leads to unattended execution of jobs. To extend this application domain by providing interactivity support to the users, Agent based technology is used. The research work which is explored and partially implemented considers various aspects of interactive job management in the grid enviroment.This paper explores the specific nature of scheduling the grid based interactive jobs which is closely analyzed and reveals the complex approach adopted by intelligent and multiagent technology.

Analyzing the Performance of Centralized Clustering Techniques for Realistic Wireless Sensor Network Topologies

AbstractClustering techniques in wireless sensor networks enables energy efficient coordination among the densely deployed nodes for data delivery till the base station. Many clustering protocols have been suggested in the recent past. The topology of the nodes, mostly seen in the literature, is of random type. This paper analyzes the performance aspects of various centralized clustering techniques for wireless sensor networks. LEACH-Centralized, KMeans-CP, FCM-CP and HSA-CP protocols have been compared with respect to clustering and data delivery process for various realistic topologies. The simulations were performed for these protocols and performance of the protocols has been critically analyzed. HSA-CP clustering method performs better compared to other techniques for almost each topology examined in the paper

Prediction Based Efficient Resource Provisioning and Its Impact on QoS Parameters in the Cloud Environment

The purpose of this paper is to provision the on demand resources to the end users as per their need using prediction method in cloud computing environment. The provisioning of virtualized resources to cloud consumers according to their need is a crucial step in the deployment of applications on the cloud. However, the dynamical management of resources for variable workloads remains a challenging problem for cloud providers. This problem can be solved by using a prediction based adaptive resource provisioning mechanism, which can estimate the upcoming resource demands of applications. The present research introduces a prediction based resource provisioning model for the allocation of resources in advance. The proposed approach facilitates the release of unused resources in the pool with quality of service (QoS), which is defined based on prediction model to perform the allocation of resources in advance. In this work, the model is used to determine the future workload prediction for user requests on web servers, and its impact toward achieving efficient resource provisioning in terms of resource exploitation and QoS. The main contribution of this paper is to develop the prediction model for efficient and dynamic resource provisioning to meet the requirements of end users

Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images

Oil spills on the ocean surface are a serious threat to the marine ecosystem. Automation of oil spill detection through full/dual polarimetric Synthetic Aperture Radar (SAR) images is considered a good aid for oil spill disaster management. This paper uses the power of log transformation to discern the scattering behavior more effectively from the coherency matrix (T3). The proposed coherency matrix is tested on patches of the clean sea surface and four different classes of oil spills, viz. heavy sedimented oil, thick oil, oil-water emulsion, fresh oil; by analyzing the entropy (H), anisotropy (A), and mean scattering angle alpha (α), following the H/A/α decomposition. Experimental results show that not only does the proposed T3 matrix differentiate between Bragg scattering of the clean sea surface from a random scattering of thick oil spills but is also able to distinguish between different emulsions of oil spills with water and sediments. Moreover, unlike classical T3, the proposed method distinguishes concrete-like structures and heavy sedimented oil even though both exhibit similar scattering behavior. The proposed algorithm is developed and validated on the data acquired by the UAVSAR full polarimetric L band SAR sensor over the Gulf of Mexico (GOM) region during the Deepwater Horizon (DWH) oil spill accident in June 2010

Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images

Oil spills on the ocean surface are a serious threat to the marine ecosystem. Automation of oil spill detection through full/dual polarimetric Synthetic Aperture Radar (SAR) images is considered a good aid for oil spill disaster management. This paper uses the power of log transformation to discern the scattering behavior more effectively from the coherency matrix (T3). The proposed coherency matrix is tested on patches of the clean sea surface and four different classes of oil spills, viz. heavy sedimented oil, thick oil, oil-water emulsion, fresh oil; by analyzing the entropy (H), anisotropy (A), and mean scattering angle alpha (α), following the H/A/α decomposition. Experimental results show that not only does the proposed T3 matrix differentiate between Bragg scattering of the clean sea surface from a random scattering of thick oil spills but is also able to distinguish between different emulsions of oil spills with water and sediments. Moreover, unlike classical T3, the proposed method distinguishes concrete-like structures and heavy sedimented oil even though both exhibit similar scattering behavior. The proposed algorithm is developed and validated on the data acquired by the UAVSAR full polarimetric L band SAR sensor over the Gulf of Mexico (GOM) region during the Deepwater Horizon (DWH) oil spill accident in June 2010