Grid Resource Allocation Research Articles

Pricing in combinatorial double auction-based grid allocation model

This article proposes a novel grid resource allocation model, in which the users and the grid service providers participate in the combinatorial double auction for the resource allocation. To obtain the detailed resource allocation status and the price information, a novel pricing algorithm is designed for the allocation model. Simulation results demonstrate that the proposed algorithm completes the resource allocation and pricing efficiently, and exhibits incentive compatible characteristic. Moreover, users with the higher average price and providers with the lower average price get compensation during the pricing process.

GridVideo: A Practical Example of Nonscientific Application on the Grid

Starting from 1990s and until now, Grid computing has been mainly used in scientific laboratories. Only in the last few years, it is evolving into a business-innovating technology that is driving commercial adoption. In this paper, we describe GridVideo, a Grid-based multimedia application for the distributed tailoring and streaming of media files. The objective is to show, starting from a real experience, how Grid technologies can be used for the development of nonscientific applications. Relevant performance aspects are analyzed, regarding both user-oriented (in terms of responsiveness) and provider-oriented (in terms of system efficiency) requirements. Different multimedia data dissemination strategies have been analyzed and an innovative technique, based on the Fibonacci series, is proposed. To respond to the stringent quality-of-service (QoS) requirements, typical of soft real-time applications, a reservation-based architecture is presented. Such architecture is able to manage the Grid resource allocation, thus enabling the provisioning of advanced services with different QoS levels. Technical and practical problems encountered during the development are discussed, and a thorough performance evaluation of the developed prototype is presented.

Grid Resource Allocation by Means of Option Contracts

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In Grid environments, where virtual organization resources are allocated to users using mechanisms analogue to market economies, strong price fluctuations can have an impact on the nontrivial quality-of-service expected by end users. In this paper, we investigate the effects of the use of option contracts on the quality of service offered by a broker-based Grid resource allocation model. Option contracts offer users the possibility to buy or sell Grid resources in the future for a strike price specified in a contract. By buying, borrowing and selling option contracts using a hedge strategy users can benefit from expected price changes. In this paper, we consider three hedge strategies: the butterfly spread which profits from small changes, the straddle which benefits from large price changes, and the call strategy which benefits from soaring prices. Using our model based on an abstract Grid architecture, we find that the use of hedge strategies augment the ratio of successfully finished jobs to failed jobs. We show that the degree of successfulness from hedge strategies changes when the number of contributed resources changes. By means of a model, we also show that the effects of the butterfly spread is mainly explained by the amount of contributed resources. The dynamics of the two other hedge strategies are best explained by observing the price behavior. We also find that by using hedge strategies the users can increase the probability that a job will finish before the deadline. We conclude that hedging using options is a promising approach to improve resource allocation in environments where resources are allocated by using a commodity market mechanism. </para>

Applying semantics to grid middleware

AbstractScheduling parallel and distributed applications efficiently onto grid environments is a difficult task and a great variety of scheduling heuristics has been developed aiming to address this issue. A successful grid resource allocation depends, among other things, on the quality of the available information about software artifacts and grid resources. In this article, we propose a semantic approach to integrate selection of equivalent resources and selection of equivalent software artifacts to improve the scheduling of resources suitable for a given set of application execution requirements. We also describe a prototype implementation of our approach based on the Integrade grid middleware and experimental results that illustrate its benefits. Copyright © 2009 John Wiley &amp; Sons, Ltd.

An evolutionary game algorithm for grid resource allocation under bounded rationality

AbstractResource allocation cannot reach equilibrium in one‐off game in grid environment because of the bounded rationality of the users. To address this issue, an evolutionary game algorithm for grid resource allocation is proposed in this paper. The evolutionary game theory is introduced to study the selection process of user strategy from the dynamic viewpoint. Firstly, the problem of multiple users competing for a common resource is formulated as a symmetric game. Secondly, replicated dynamic mechanism is used to produce the evolutionary stable point that leads to a satisfied allocation scenario. Finally, the relationships among the evolutionary stable point, valuation functions, and convergence time are discussed in detail. The results of the experiments show that the proposed evolutionary game algorithm is convergent and generates better utility results compared with the classical game algorithm. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Effective Reformulation for Resource Allocation in Computational Grid

Grid enables resource sharing and dynamic allocation of computational resources. It is a great challenge to make numerous resources available on-demand to guarantee the Quality-of-Service for jobs. This paper presents a two-stage optimization model for resource allocation in grid. Job constraints are classified into mandatory constraints and negotiated constraints. In the first stage, a preprocessing procedure such as resource discovery deals with the mandatory constraints. This has been fulfilled as our previous work. In the second stage, negotiated constraints are treated as Knapsack Problem-based optimization problem. Centralized scheduling and decentralized scheduling have been considered in this paper. This work is fulfilled as part of the Constellation Model for grid resource management. We formulate centralized scheduling as Multi-Constraint Multiple Knapsack Problem (MCMKP). In centralized scheduling, jobs are submitted to a global job queue. A global scheduler assigns each job to a proper grid site according to the scheduling strategy. The scheduling is done periodically (e.g. daily or weekly). We formulate decentralized scheduling as Multi-Dimensional Knapsack Problem (MDKP). In decentralized scheduling, jobs are submitted to local job queues. Allocation decisions are made by local schedulers individually. Jobs that can not be executed immediately are sent to a global waiting queue. When local scheduling is initialized, a local scheduler can select jobs from both the local job queue and the global waiting queue. Objectives of both centralized scheduling and decentralized scheduling are to optimize the utility defined by a grid economy approach. The defined utility makes the trade-offs between user-concerned metrics and system-concerned metrics. Heuristic algorithms such as Very Large-Scale Neighborhood Search proposed by R. K. Ahuja and C. B. Cunha [2005] can be used to solve the combinatorial optimization problem. We implemented a prototype of the Constellation Model. Genetic algorithms for constrained optimization which is proposed by S. Venkatraman and G. G. Yen [2005] have been implemented to solve the above optimization problems. Experimental results show that, performance metrics such as gained utility, response rate, and resource utilization are improved and resources are allocated in an optimal way.

A new optimal approach for multiple optimisation objectives grid resource allocation and scheduling

The article considers the resource allocation and scheduling problem in a grid computing environment. The article proposes system optimisation scheduling (SOS) that provides a potential solution of joint optimisation of objectives for both the resource and application layer, which combine both application-oriented and resource-oriented scheduling benefits. Grid systems will strive to find an optimal relation between user satisfaction and resource utilisation. Utility functions are used to express grid user's Quality of Service requirement, resource provider's benefit function and system's objectives. In order to verify the efficiency of the proposed scheduling algorithm, we compare the performance of application optimisation scheduling, resource optimisation scheduling, SOS with a traditional Round-Robin algorithm. The simulations study the effect of the request rate and task-to-resource ratio on the different scheduling algorithm.

Utility-driven solution for optimal resource allocation in computational grid

Optimal resource allocation is a complex undertaking due to large-scale heterogeneity present in computational grid. Traditionally, the decision based on certain cost functions has been used in allocating grid resource as a standard method that does not take resource access cost into consideration. In this paper, the utility function is presented as a promising method for grid resource allocation. To tackle the issue of heterogeneous demand, the user's preference is represented by utility function, which is driven by a user-centric scheme rather than system-centric parameters adopted by cost functions. The goal of each grid user is to maximize its own utility under different constraints. In order to allocate a common resource to multiple bidding users, the optimal solution is achieved by searching the equilibrium point of resource price such that the total demand for a resource exactly equals the total amount available to generate a set of optimal user bids. The experiments run on a Java-based discrete-event grid simulation toolkit called GridSim are made to study characteristics of the utility-driven resource allocation strategy under different constraints. Results show that utility optimization under budget constraint outperforms deadline constraint in terms of time spent, whereas deadline constraint outperforms budget constraint in terms of cost spent. The conclusion indicates that the utility-driven method is a very potential candidate for the optimal resource allocation in computational grid.

Approach to allocate computational grid resources based on online reverse auction technology

Using economic model to research grid resource management is a new hot topic in the field of grid computing.Based on current grid resources management methods,an online reverse auction technology-based approach to allocating computational grid resources was proposed concerning oversupply environment.The corresponding QoS function is defined to analyze the application range of this approach as well as its advantages.Finally,the results of simulation experiments indicate that this method is effective for computational grid resource allocation.

Macroeconomics based Grid resource allocation

Resource allocation is the key technology in Grid computing. While most of the existing literature of economics-based Grid resource allocation relies on the basic microeconomic principle, which concerns the behavior of individual agents in the Grid, this paper provides a novel approach based on macroeconomics, which concerns large aggregate behavior instead of individual actions. First, we propose a macroeconomics-based market framework that is well suited for a service-oriented Grid. Then, some macroeconomics-based resource allocation strategies, which can effectively improve the performance of the whole Grid market, are given. Simulation results prove good performance of the proposed method.

A hybrid petri-net model of grid workflow

In order to effectively control the random tasks submitted and executed in grid workflow, a grid workflow model based on hybrid petri-net is presented. This model is composed of random petri-net, colored petri-net and general petri-net. Therein random petri-net declares the relationship between the number of grid users’ random tasks and the size of service window and computes the server intensity of grid system. Colored petri-net sets different color for places with grid services and provides the valid interfaces for grid resource allocation and task scheduling. The experiment indicated that the model presented in this letter could compute the valve between the number of users’ random tasks and the size of grid service window in grid workflow management system.

Modeling and Supporting Grid Scheduling

Grid resource management systems and schedulers are important components for building Grids. They are responsible for the selection and allocation of Grid resources to current and future applications. Thus, they are important building blocks for making Grids available to user communities. In this paper we briefly analyze the requirements of Grid resource management and provide a classification of schedulers. Then, we define an extensible formal model for Grid scheduling activities, and characterize the general Grid scheduling problem. Finally, we provide a reference architecture for the support of our model and discuss different aspects of architectural implementations.

Joint QoS optimization for layered computational grid

Many existing grid resource allocation and scheduling algorithms mainly focus on isolated layers of the grid architecture. The inflexibility of the strict layering structure results in an inefficient utilization of the grid resources. This paper takes a system view of the computational grid and aims to jointly optimize global QoS by adopting cross-layer design. Cross-layer design is based on information exchange and joint optimization among multiple grid layers. Parameters from different layers are provided to a cross-layer optimizer, which selects the values of the layer specific parameters maximizing joint global QoS. The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer. In simulation part, we compare the performance of the global joint QoS optimization approach with application layer local optimization and resource layer local optimization approach, respectively.

Cross-layer optimization policy for QoS scheduling in computational grid

This paper presents a cross-layer quality of service (QoS) optimization policy for computational grid. Efficient QoS management is critical for computational grid to meet heterogeneity and dynamics of resources and users’ requirements. There are different QoS metrics at different layers of computational grid. To improve perceived QoS by end users over computational grid, QoS supports can be addressed in different layers, including application layer, collective layer, fabric layer and so forth. The paper tackles cross-layer grid QoS optimization as optimization decomposition, each layer corresponds to a decomposed subproblem. The proposed policy produces an optimal set of grid resources, service compositions and user's payments at the fabric layer, collective layer and application layer respectively to maximize global grid QoS. The cross-layer optimization problem decomposes into three subproblems: grid resource allocation problem, service composing and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of grid resources and service demand. In order to coordinate the subproblems, cross-layer QoS feedback mechanism is established to ensure different layer interactions. The simulations are conducted to validate the efficiency of the proposed policy.

Distributed Graphics Pipelines on the Grid

jgViz uses standard grid technologies and Chromium cluster graphics software to schedule the best available distributed graphics pipeline. Visualization of large data sets using computer graphics techniques has become a mainstream requirement for many scientists and engineers. However, achieving interactive performance when rendering a complex visualization often requires high-performance computing facilities. One solution is to exploit real-time graphics accelerators, which are used in the full range of computing devices from games consoles and set-top boxes to supercomputers and advanced training simulators. At the same time, the service infrastructure for distributed networks has produced the third generation of the connected world: the grid. jgViz, our Java-implemented grid visualization system, uses grid functionality to enable transparent access to reliable parallel graphics pipelines. Lightweight and highly portable, jgViz aims to be plug-and-play for the existing grid resource allocation mechanism

Research on a Grid Resource Allocation Algorithm Based on MAS Non-Cooperative Bidding Game

Research on a Grid Resource Allocation Algorithm Based on MAS Non-Cooperative Bidding Game

Relaxed-criteria G-negotiation for Grid resource co-allocation

Supporting resource co-allocation is essential for realizing the Grid vision because computational intensive applications may require more resources than a single computing machine can provide in one administrative domain. Given that the various stakeholders often have their own requirements and supply-and-demand patterns, successfully obtaining commitments through concurrent negotiations with multiple resource owners to simultaneously access several resources is a very challenging task. This position paper (i) suggests that a relaxed-criteria G-negotiation mechanism may be used to enhance the success rates of negotiation agents in Grid resource co-allocation, (ii) proposes a testbed for realizing the relaxed-criteria G-negotiation mechanism, (iii) discusses experimental designs for evaluating the proposed mechanism, and (iv) discusses previous results obtained from using a relaxed-criteria G-negotiation protocol for Grid resource allocation.

The catallaxy approach for decentralized economic-based allocation in Grid resource and service markets

Efficient resource allocation in dynamic large-scale environments is one of the challenges of Grids. In centralized economic-based allocation approaches, the user requests can be matched to the fastest, cheapest or most available resource. This approach, however, shows limitations in scalability and in dynamic environments. In this paper, we explore a decentralized economic approach for resource allocation in Grid markets based on the Catallaxy paradigm. Catallactic agents discover selling nodes in the resource and service Grid markets, and negotiate with each other maximizing their utility by following a strategy. By means of simulations, we evaluate the behavior of the approach, its resource allocation efficiency and its performance with different demand loads in a number of Grid density and dynamic environments. Our results indicate that while the decentralized economic approach based on Catallaxy applied to Grid markets shows similar efficiency to a centralized system, its decentralized operation provides greater advantages: scalability to demand and offer, and robustness in dynamic environments.

Resource discovery and allocation in wireless grids by using a multiagent system

This paper presents an Agent-based Discovery and Allocation of Resources (ADAR) model for wireless grids of virtual organizations. ADAR allocates cost effective resources for the jobs in order to maximize the resource utilization. It employs resource discovery mechanism in a hierarchical fashion: firstly within a local cluster, secondly within a virtual organization if a resource is not available in the local cluster, and finally among the virtual organizations if a resource is not available within the local virtual organization. ADAR uses five types of agents namely Job Processing Agents (JPAs), Job Mobile Agents (JMAs), Resource Monitoring Agents (RMAs), Actual Organization Resource Broker Agents (AORBAs), and Virtual Organization Resource Manager Agents (VORMAs). JPA is static and executes a job within the same machine if the resources are available; otherwise it creates a JMA that carries the job requirements with it and communicates with its associated AORBA to discover the required resources. The AORBA is a static agent and discovers the optimum cost resources within its cluster. AORBA interacts with the VORMAs in case the resources are not available within a cluster. VORMA is a static agent and can interact with other VORMAs. The RMA is a static agent and monitors the resource status of a machine. The discovery results are informed to JMA, which makes the source device to migrate its job to the discovered resource. ADAR is evaluated in a simulated environment by using different wireless grid scenarios for virtual organizations. The performance parameters evaluated are: job cost function, resource utilization, bandwidth utilization, agent overheads, and resource discovery time against different mobility factors and varying system loads.

A SERVICE-ORIENTED APPROACH TO ENFORCE GRID RESOURCE ALLOCATIONS

We present the SweGrid Accounting System (SGAS) — a decentralized and standards-based system for Grid resource allocation enforcement that has been developed with an emphasis on a uniform data model and easy integration into existing scheduling and workload management software.The system has been tested at the six high-performance computing centers comprising the SweGrid computational resource, and addresses the need for soft, real-time quota enforcement across the SweGrid clusters.The SGAS framework is based on state-of-the-art Web and Grid services technologies. The openness and ubiquity of Web services combined with the fine-grained resource control and cross-organizational security models of Grid services proved to be a perfect match for the SweGrid needs. Extensibility and customizability of policy implementations for the three different parties that the system serves (the user, the resource manager, and the allocation authority) are key design goals. Another goal is end-to-end security and single sign-on, to allow resources to reserve allocations and charge for resource usage on behalf of the user.We conclude this paper by illustrating the policy customization capabilities of SGAS in a simulated setting, where job streams are shaped using different modes of allocation policy enforcement. Finally, we discuss some of the early experiences from the production system.