Enabling Adaptive Grid Scheduling and Resource Management

Wider adoption of the Grid concept has led to an increasing amount of federated computational, storage and visualisation resources being available to scientists and researchers. Distributed and heterogeneous nature of these resources renders most of the legacy cluster monitoring and management approaches inappropriate, and poses new challenges in workflow scheduling on such systems. Effective resource utilisation monitoring and highly granular yet adaptive measurements are prerequisites for a more efficient Grid scheduler. We present a suite of measurement applications able to monitor per-process resource utilisation, and a customisable tool for emulating observed utilisation models. We also outline our future work on a predictive and probabilistic Grid scheduler. The research is undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising Grid Resource Management) in cooperation with BT

Resource and Application Models for Advanced Grid Schedulers

As Grid computing is becoming an inevitable future, managing, scheduling and monitoring dynamic, heterogeneous resources will present new challenges. Solutions will have to be agile and adaptive, support self-organization and autonomous management, while maintaining optimal resource utilisation. Presented in this paper are basic principles and architectural concepts for efficient resource allocation in heterogeneous Grid environment

Designing and simulating smart grids

Growing energy demands and the increased use of renewal energies have changed the landscape of power networks leading to new challenges. Smart Grids have emerged to cope with these challenges by facilitating the integration of traditional and renewable energy resources in distributed, open, and self-managed ways. Innovative models are needed to design energy infrastructures that can enable self-management of the power grid. Software architectures smoothly integrate the software that provides self-management to Smart Grids and their hardware infrastructures. We present a framework to design the software architectures of autonomous Smart Grids in an intuitive domain-oriented way and to simulate their execution by automatically generating the code from the designed autonomous smart grid architectures

Towards Grid Monitoring and deployment in Jade, using ProActive

This document describes our current effort to gridify Jade, a java-based environment for the autonomic management of clustered J2EE application servers, developed in the INRIA SARDES research team. Towards this objective, we use the java ProActive grid technology. We first present some of the challenges to turn such an autonomic management system initially dedicated to distributed applications running on clusters of machines, into one that can provide self-management capabilities to large-scale systems, i.e. deployed on grid infrastructures. This leads us to a brief state of the art on grid monitoring systems. Then, we recall the architecture of Jade, and consequently propose to reorganize it in a potentially more scalable way. Practical experiments pertain to the use of the grid deployment feature offered by ProActive to easily conduct the deployment of the Jade system or its revised version on any sort of grid

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-to-peer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures.Comment: 8 pages, 9 figures. Presented at the 2nd IEEE Int Conference on eScience & Grid Computing. Amsterdam Netherlands, December 200

USING SMART GRID TECHNOLOGY IN ENERGY DISTRIBUTION SYSTEMS

Using smart grid technology in today energy distribution systems will reduce cost, reach manageability, provide safety of energy supply chain to end customer and provide new innovative energy service delivery. Term “smart grid” can be explained with following words – intelligent, self-sustained, with management based on IP (Internet Protocol) telecommunication network for transportation of critical data in real-time from customer site (smart meters, smart homes, smart buildings) and distributed power plants to central management station (energy service provider operations). Main function of the central management station is to acquire and evaluate stored data in real time and based on this stored and evaluated data, in case of emergency, power outage on some subsystem or increased need for power on specific location, to apply necessary steps in real-time. Therefore data conformity and security in smart grid technology is an important function concept to implement. Nevertheless primary goal of smart grid technology is to improve the efficiency, reliability and safety of power delivery by modernizing both the transmission and the distribution grids. This article has a goal to provide a high-end top-level view of a modern telecommunication infrastructure needed to implement a smart grid technology into an energy transmission and distribution grid