Grid Resource Allocation Research Articles

A Sequential Game-Based Resource Allocation Strategy in Grid Environment

In grid environment, resource load prediction is one of the most important problems in resource allocation optimization. But load status is difficult to estimate accurately due to the dynamic nature and heterogeneity of grid resource. In response to this issue, a resource allocation strategy that uses sequential game method to predict resource load for time optimization in a proportional resource sharing environment is proposed. The problem of multiple users bidding to compete for a common computational resource is formulated as a multi-player dynamic game. Through finding the Nash equilibrium solution of the multi-player dynamic game, resource load is predicted. Using this load information, a set of user optimal bids is produced to partition resource capacity according to proportional sharing mechanism. The experiments are performed based on the GridSim toolkits and the results show that the proposed strategy could generate reasonable user bids, reduce resource processing time, hence overcome the deficiency of Bredin’s strategy, which is not concerned with resource load variation. The conclusion indicates that employing sequential game method for load prediction is feasible in grid resource allocation and adapts better to the dynamic nature of heterogeneous resource in grid environment.

Dynamic resource allocation for joint grid user and provider optimisation in computational grid

This paper is to solve optimal resource allocation in computational grid to optimise resource allocation for grid users and grid providers subject to various constraints. The grid user wants to get optimal benefits, at the same time, the grid provider adjusts the unit price in order to maximise revenue, which is measured as the sum of individual payments. The paper describes how the agents can be assigned proper utility functions to make a natural trade-off between money and resource. The paper proposes a grid resource pricing algorithm for allocating resources to grid users while maximising the revenue of grid providers.

A survey of bargaining models for grid resource allocation

Whereas it is noted that various types of auctions, commodity market models, and contract-net (tendering) model are more widely used for managing Grid resources, this paper focuses on discussing bargaining (negotiation) models for Grid resource management. To this end, this survey supplements and complements existing surveys by reviewing, comparing, and highlighting the very few extant research initiatives on applying bargaining as a mechanism for managing Grid resources. The contributions of this paper are (i) discussing the motivations for considering bargaining models for Grid resource management, (ii) discussing the issues in building bargaining mechanisms for Grid resource management, (iii) comparing the strategies and protocols of state-of-the-art bargaining models for Grid resources, and (iv) discussing possible new directions.

Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE)--a scalable computational system for the high-throughput analysis of genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of genome analysis including acquisition of data from multiple genomic databases; data analysis by a diverse set of bioinformatics tools; and storage of results and annotations. High-throughput computations in GNARE are performed using distributed heterogeneous Grid computing resources such as Grid2003, TeraGrid, and the DOE Science Grid. Multi-step genome analysis workflows involving massive data processing, the use of application-specific tools and algorithms and updating of an integrated database to provide interactive web access to results are all expressed and controlled by a "virtual data" model which transparently maps computational workflows to distributed Grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.

A distributed utility-based two level market solution for optimal resource scheduling in computational grid

This paper investigates the interactions between agents representing users, services and resources to solve resource scheduling optimization in computational grid. In order to reduce the computational complexity, we further decompose the grid resource allocation optimization into subproblems: grid user agent-grid service agent in service market and grid service agent-grid resource agent in resource market. Two-level market converges to its optimal points; a globally optimal point is achieved. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the service market level optimization and resource market level optimization. It demonstrates a practical approach to market responsive resource pricing that can benefit grid providers and users alike. The paper presents two-level market grid resource pricing that is an iterative algorithm used to perform optimal resource allocation. The experiment shows that two-level market based resource pricing scheme outperforms one level market scheme in terms of task completion time and resource allocation efficiency.

Utility driven dynamic resource allocation using competitive markets in computational grid

This paper presents a utility-based market mechanism for dynamic resource allocation in computational grid. The proposed competitive market economy is described. It consists of three different types of agents: grid request agent, grid resource agents, grid task agents; and a grid market that regulates the prices based on the observed aggregate excess demand. In order to solve the problem of heterogeneous demand in the grid, grid users' preferences are summarized by means of their utility functions. We consider budget and complete times as constraints of optimization problem. The objective of the optimization problem is to determine the amount of resources under different constraints to maximize the users' utilities. This paper proves the existence of a solution of the optimization problem, it also specify an iterative algorithm that is used by the grid market to update the prices, and it leads to an allocation that is close to a solution of the optimization problem in a finite number of iterations. The experiments are made to study characteristics of the utility directed allocation algorithm under budget constraint and under completed time constraint in terms of completion time and resource allocation efficiency.

A distributed decomposition policy for computational grid resource allocation optimization based on utility functions

This paper presents a market-oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This allows the integration of different optimization objectives into allocation process. This paper is targeted to solve the above issues by using utility-based optimization scheme. We decompose the optimization problem into two levels of sub problems so that the computational complexity is reduced. Two market levels converge to its optimal points; a globally optimal point is achieved. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the service market level optimization and resource market level optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation. The experiments show that scheduling based on pricing directed resource allocation involves less overhead and leads to more efficient resource allocation than conventional Round-Robin scheduling.

Competitive proportional resource allocation policy for computational grid

This paper presents a competitive proportional resource allocation in computational grid. A system model is described that allows agents representing various grid resources, which owned by different real world enterprises, to coordinate their resource allocation decisions without assuming a priori cooperation. The grid task agents buy resources to complete tasks. Grid resource agents charge the task agents for the amount of resource capacity allocated. Two types of optimization problems related to grid task agent are proposed. Given grid resource agent’s pricing policy, the task agent optimization problem is to complete its job as quickly as possible when spending the least possible amount of money. Given specified amount of time to complete jobs, the task agent optimization problem is to minimize the cost accrued. This paper provides a price-directed proportional resource allocation algorithm for solving the grid task agent resource allocation problem. Experiments are made to compare the performance of the price-directed resource allocation with conventional Round-Robin allocation. The results of experiment show the price-directed allocation has better performance than usual Round-Robin allocation.