Grid Resource Allocation Research Articles

High Performance Hypercube for Better Resource Discovery and Allocation in Simulated Grid based Environment

High Performance Hypercube for Better Resource Discovery and Allocation in Simulated Grid based Environment

Resource Allocation Mechanism with New Models for Grid Environment

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

A new fuzzy negotiation protocol for grid resource allocation

In real-life trading, relaxing decisions in the face of trading pressure is common. Similarly, in market-based grid resource allocation problem designing negotiator agents with the flexibility to relax their decision to (quickly) complete a deal in the face of intense Grid Market Pressure (GMP) is essential. To make this idea possible, we design Enhanced Market- and Behavior-driven Negotiation Agents (EMBDNAs) that adopt new fuzzy negotiation protocol. The protocol focuses on both (1) enhancing Rubinstein's sequential alternating offer protocol to handle multiple trading opportunities and market competition and (2) designing two new Fuzzy Grid Market Pressure Determination Systems (FGMPDSs) for both grid resource consumers and grid resource owners to guide negotiator agents in relaxing their bargaining terms under intense GMP to enhance their chance of successfully acquiring/leasing out resources. Implementing the idea in an agent-based testbed, an experiment for evaluating and comparing EMBDNA against EMDA (Enhanced Market-Driven Agent) and our previous work in name MBDNA (Market- and Behavior-driven Negotiation Agent) were carried out through stochastic simulations. While EMDA relaxes its bargaining term in the face of intense GMP by considering just two relaxation factors the MBDNA uses the same negotiation strategy as EMBDNA but does not relax its bargaining term in the face of intense GMP. The results show that adopting the new fuzzy negotiation protocol, EMBDNAs outperform MBDNAs and EMDAs.

An Interactive Approach for Multi-Attribute Resource Allocation in Grids

Auction-based models and protocols introduce money and pricing as the technique for coordination between users and producers of resources in grids. In this paper, we propose a multi-attribute reverse auction protocol to allocate grid resource. In order to enhance the information efficiency we develop a novel interactive approach supporting both user agents and producer agents. In this approach, an iterative algorithm used to estimate the resource user’s preference utility function can facilitate producer agents to update their bids with incomplete information. Numerical simulating experiments show that our approach can satisfy the resource user’s quality demand on multiple attributes, and achieve high efficiency in user utility. The results also provide an idea to analyze the two-side private information problem in gird resource allocation.

Reward-based allocation of cluster and grid resources for imprecise computation model-based applications

Utility-based resource management is becoming an emerging issue as the utilisation of cluster resources in Grid computing is growing rapidly. In this paper, we provide a new Imprecise Computation IC application model for flexible reward-based Grid resource management. An application in the proposed model consists of multiple independent jobs, in which each job has two parts: mandatory part for the minimum quality and optional part for additional computations. This application model can be applied to QoS-related Grid applications and used in adaptive resource management. We also provide scheduling algorithms for resource allocation of the IC applications based on reward. The profitable optional execution time is analysed for both space-shared and time-shared scheduling policies. Simulation results show that the proposed schemes are beneficial to both users and resource providers in terms of application acceptance rate and total reward.

Agent based sensors resource allocation in sensor grid

Sensor enabled grid may combine real time data about physical environment with vast computational resources derived from the grid architecture. One of the major challenges of designing a sensor enabled grid is how to efficiently schedule sensor resource to user jobs across the collection of sensor resources. The paper presents an agent based scheme for assigning sensor resources to appropriate sensor grid users on the basis of negotiation results among agents. The proposed model consists of two types of agents: the sensor resource agents that represent the economic interests of the underlying sensor resource providers of the sensor grid and the sensor user agents that represent the interests of grid user application using the grid to achieve goals. Interactions between the two agent types are mediated by means of market mechanisms. We model sensor allocation problems by introducing the sensor utility function. The goal is to find a sensor resource allocation that maximizes the total profit. This paper proposes a distributed optimal sensor resource allocation algorithm. The performance evaluation of proposed algorithm is evaluated and compared with other resource allocation algorithms for sensor grid. The paper also gives the application example of proposed approach.

Task Scheduling Heuristic in Grid Computing

Task scheduling is heart of any grid application which guides resource allocation in grid. Heuristic task scheduling strategies have been used for optimal task scheduling. Heuristic techniques have been widely used by the researchers to solve resource allocation problem in grid computing. In this paper, we classify heuristic task scheduling strategies in grid on the basis of their characteristics. We identified different types of heuristics such as population based heuristic, economic heuristic, meta heuristic, simple heuristic, and hybrid heuristic.

A QOS-BASED RESOURCE ECONOMIC SCHEDULING IN MANUFACTURING GRI

Manufacturing grid is now becoming a novel manufacturing pattern. After analyzing the existing grid resource scheduling and allocation approaches and economy principle, a QoS-based resource economic scheduling algorithm is studied. Firstly, QoS-based resource economic scheduling model is proposed. Secondly, with market mechanism introduced into grid resource scheduling research, QoS-based resource economic scheduling algorithm is presented and discussed detailedly including scheduling mathematical model and the optimization process of scheduling algorithm. Finally, by simulation experiments the effectiveness and flexibility of the proposed algorithm are proved compared with classical algorithm. DOI: http://dx.doi.org/10.5755/j01.mech.18.4.2336

Negotiation strategies considering market, time and behavior functions for resource allocation in computational grid

Providing an efficient resource allocation mechanism is a challenge to computational grid due to large-scale resource sharing and the fact that Grid Resource Owners (GROs) and Grid Resource Consumers (GRCs) may have different goals, policies, and preferences. In a real world market, various economic models exist for setting the price of grid resources, based on supply-and-demand and their value to the consumers. In this paper, we discuss the use of multiagent-based negotiation model for interaction between GROs and GRCs. For realizing this approach, we designed the Market- and Behavior-driven Negotiation Agents (MBDNAs). Negotiation strategies that adopt MBDNAs take into account the following factors: Competition, Opportunity, Deadline and Negotiator's Trading Partner's Previous Concession Behavior. In our experiments, we compare MBDNAs with MDAs (Market-Driven Agent), NDF (Negotiation Decision Function) and Kasbah in terms of the following metrics: total tasks complementation and budget spent. The results show that by taking the proposed negotiation model into account, MBDNAs outperform MDAs, NDF and Kasbah.

A flexible layered control policy for resource allocation in a sensor grid

The paper proposes a flexible layered control policy for sensor resource allocation in a sensor grid. In order to allocate sensor resources in the system to maximize the sensor grid utility, different controllers are deployed at three levels: a job-level controller, an application group controller, and a sensor grid system controller. At the lowest levels, job-level controllers perform fast, frequent, local adaptation for optimizing a single sensor grid application at a time, while, at the highest levels, sensor grid system controllers perform less frequent control actions to optimize all applications. Sensor grid system control considers all sensor grid applications in response to large system changes at coarse time granularity. Sensor grid system control exploits the interlayer coupling of the resource layer and the application layer to achieve a system-wide optimization based on the sensor grid users’ preferences. Job-level control adapts a single application to small changes at fine granularity. The layered control system uses a set of utility functions to evaluate the performance of sensor grid applications and groups. The control system chooses control actions that would result in a higher level of utility. In the simulation, a performance evaluation of the algorithm is carried out.

Self-Adaptive Economic-Based Resource Allocation in Ad-Hoc Grids

Resource allocation is the process of discovering and allocating resources to requested tasks in a way that satisfy both user jobs and resource administrators. In ad-hoc Grids, resource allocation is a challenging undertaking as tasks and resources are distributed, heterogeneous in nature, owned by different individuals or organizations and they may arise spontaneously at any time with various requirements and availabilities. In this paper, the authors address an economic-based framework for resource allocation in ad-hoc Grids to deal with the dynamic nature of such networks. Within the economic framework, self-interested nodes in ad-hoc Grids are considered as consumers (buyers) and producers (sellers) of resources. Consumers and producers of resources are autonomous agents that cooperate through a simple, single metric namely the price that summarizes the global state of a network in a number. Adaptation is achieved by individual nodes through adopting a bidding strategy that adjusts the price according to the current state of the network in order to optimize the local utility of the node.

Market_based grid resource allocation using new negotiation model

This paper presents a new negotiation model for designing Market- and Behavior-driven Negotiation Agents (MBDNAs) that address computational grid resource allocation problem. To determine the amount of concession for each trading cycle, the MBDNAs are guided by six factors: (1) number of negotiator's trading partners, (2) number of negotiator's competitors, (3) negotiator's time preference, (4) flexibility in negotiator's trading partner's proposal, (5) negotiator's proposal deviation from the average of its trading partners’ proposals, and (6) previous concession behavior of negotiator's trading partner. In our experiments, we compare grid resource consumer (GRC) of type MBDNAs (respectively grid resource owner (GRO) of type MBDNAs) with MDAs (Market Driven Agents) in terms of the following metrics: total tasks complementation and average utility (respectively resource utilization level and average utility). The results show that by taking the proposed factors into account, MBDNAs of both types make a more efficient concession amount than MDAs and are, therefore, considered an appropriate mechanism for grid resource allocation in different grid workloads and market types.

Grid Resource Allocation Model Based on Incomplete Information Game

针对网格计算环境动态,异构和分布的特性以及网格资源分配中资源利用率低、效益不均等问题,结合微观经济学理论,建立了一种多赢家式的网格资源拍卖模型(muti-winners auction model,简称MWAM).将隐马尔可夫模型应用在网格用户t 时刻出价状态预测方面,并结合分配算法计算出能够获得所需资源的概率;并且在原有资源分配机制的基础上,结合非完全信息纳什均衡理论设计了一种多赢家拍卖算法.从理论上证明了资源分配结束后系统收益最大,且本模型符合微观经济学中的激励相容性与个人理性准则.实验模拟在验证了隐马尔可夫预测的可行性的同时,又与几种具有代表性的算法相比较,从资源利用率、系统总收益等方面突显了本模型的优势.

Optimal Power Allocation Under Communication Network Externalities

Efficient resource allocation is an important problem that aims for a “greener” and more environmentally friendly electric power grid. The smart behavior of the newly emerged grid, combined with two-way communication between users and the operator allows for actions like measurement, monitoring, prediction, and control signaling so as to maximize social welfare. We introduce a framework for optimal resource allocation in smart grids that also considers the uncertainty in message signaling. This introduces communication network externalities, added on top of the existing transmission network ones. The task at hand resembles the so called local public goods problem in mathematical economics terminology, a problem impractical to solve using centralized mechanisms. We propose an iterative, decentralized algorithm for its solution. The algorithm is scalable for deployment in large networks since it requires only messages per network user per iteration, where is the number of users. Moreover, it is guaranteed to converge, does not require revelation of private information from each user and all algorithm actions can be realized by programmable smart devices of the grid.

Multi-layer optimization in service-oriented sensor grid

Sensor devices such as video cameras, infrared sensors and microphones are being widely exploited in grid application. The paper deals with multi-layer optimization in service oriented sensor grid to optimize utility function of sensor grid, subject to resource constraints at resource layer, service composition constraints at service layer and user preferences constraints at application layer respectively. The multi-layer optimization problem can be decomposed into three subproblems: sensor grid resource allocation problem, service composing problem, and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of sensor grid resources and service demand. The proposed algorithm decomposes global sensor grid optimization problem into a sequence of three sub-problems at three layers via an iterative algorithm. The simulations are conducted to validate the efficiency of the multi-layer optimization algorithm. The experiments compare the performance of the multi-layer global optimization approach with application layer local optimization and resource layer local optimization approach respectively.

Agent Based Priority Heuristic for Job Scheduling on Computational Grids

A grid is an infrastructure for resource sharing. At present, many scientific applications require high computing power in processing, which can only be achieved by using the computational grid. For the selection and allocation of grid resources to current and future applications, grid job scheduling is playing a very vital role for computational grids. They constitute the building blocks for making grids available to the society. The efficient and effective scheduling policies, when assigning different jobs to specific resources, are very important for a grid to process high computing intensive applications. This paper presents an agent based job scheduling algorithm for efficient and effective execution of user jobs. This paper also includes the comparative performance analysis of our proposed job scheduling algorithm along with other well known job scheduling algorithms considering the quality of service parameters like waiting time, turnaround time, response time, total completion time, bounded slowdown time and stretch time. We also conducted the QoS based evaluation of the scheduling algorithms on an experimental computational grid using real workload traces. Experimental evaluation confirmed that the proposed grid scheduling algorithms posses a high degree of optimality in performance, efficiency and scalability. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs.

Learning Automata Based Method for Grid Computing Resource Valuation with Resource Suitability Criteria

In economic Grid environments, the producers (resource owners) and consumers (resource users) have different goals, objectives, strategies, and supply-and-demand patterns. Mechanism based on economic models is an effective approach to solve the problem of grid resources management. Grid resource valuation and allocation is one of the fundamental problems in grid resource management. The essence of this problem is how to allocate and valuation resources for achieving the goal of a highly efficient utilization of resources in response to current resource valuations. Pricing policies are based on the demand from the users and the supply of resources is the main driver in the competitive, economic market model. In this paper, we present a new method of resource allocation and valuation based on the learning automata algorithms in order to maximize the benefit for both grid providers and grid users. We formulate the problem as an environment that learning automata's allocate best resource based on its complete time for proffered application. After allocate of resource, valuation of it based on its complete time is done. With this method the valuation of resource is based on their suitability for jobs execution. Using computer simulations, it is shown that the proposed methodology have higher performance in comparing with existing methods.

Policy-Based Scheduling and Resource Allocation for Multimedia Communication on Grid Computing Environment

This paper discusses policy-based scheduling techniques on heterogeneous resource for multimedia services to deal with the demands of next-generation multimedia applications on grid computing environment. The proposed scheduling algorithm has the following features, which can be utilized on grid computing environment. First, the algorithm supports the resource usage constrained scheduling. A grid consists of the resources that are owned by decentralized institutions. Second, the algorithm performs the optimization-based scheduling. It provides an optimal solution to the grid resource allocation problem. Third, the algorithm assumes that a set of resources is distributed geographically and heterogeneous in nature. Fourth, the scheduling method dynamically adjusts to the grid status. It tracks the current workload of the resources. The performance of the proposed algorithm is evaluated with a set of predefined metrics. In addition to show the simulation results for the out-performance of the policy-based scheduling a set of experiment is performed on open science grid.

Design and implementation of economics-based resource management system in ad hoc grid

The ad hoc grid is a spontaneous organization of cooperating heterogeneous nodes into a logical community without a fixed infrastructure and with only minimal administrative requirements. Resource management for ad hoc grids is challenging due to the participation of heterogeneous, dynamic, autonomous and ephemeral grid nodes. The paper proposes an ad hoc grid resource management system, the producers and consumers of ad hoc grid resource are modeled as the self-interested decision-makers described in microeconomic theory. All market participants in the ad hoc grid environment including grid resources and services can be represented as agents. We apply economic agents to build ad hoc grid resource management, where ad hoc grid resource consumers and providers can buy and sell ad hoc grid resource based on an underlying economic architecture. The main processes involved in ad hoc grid resource management are resource registration, discovery, and resource allocation. The experiments are conducted to compare ad hoc grid resource allocation algorithm with other ad hoc grid resource allocation algorithm. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme.

An Economic Model for Resource Allocation in Grid Computing

Allocating resources in grid computing requires local and external schedulers to communicate in order to achieve an efficient management of the resources themselves. To this end, some economic/market-based models have been introduced in the literature, where users, external schedulers, and local schedulers negotiate to optimize their objectives. In this paper, we propose a tender/contract-net model for the grid resource allocation problem, showing the interactions among the involved actors. The performance of the proposed market-based approach is experimentally compared with a round-robin allocation protocol, a system-centric least-cost allocation approach, and also a market-based approach available from the literature.