Grid Resource Management System Research Articles

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform. The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer. It also derives an objective function with candidate solution (schedule) as input and the outcome is the makespan value denoting the quality of the candidate solution. The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

A hybrid heuristic of Variable Neighbourhood Descent and Great Deluge algorithm for efficient task scheduling in Grid computing

To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. Scheduling tasks to resources in Grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND.

IRMM: An Integrated Resource Management Model for Grid Computing

Grid computing is a collection of heterogeneous systems or heterogeneous objects that are geographically distributed over a network. Resource management is a process in which various activities like allocation of resources and scheduling are performed for handling issues like load balancing, reliability, scalability, maximum, throughput, minimum expectation time and security. There are several factors that make resource management difficult as different system may have different requirements, properties, conditions and different access and cost models. Resource management in Grid is the method of identifying requirements, finding corresponding resources to the applications, allocating those matching resources, scheduling and monitoring. In Grid resource management resource broker plays the very important role. Users communicate with a resource broker to access the grid information. Resource broker discover the resource that are available and negotiates with their owners or their agents to get the reservation of resources. Number of approaches exists through which one can develop grid resource management systems. In this paper a new architectural model has been implemented for grid resource management which is based on the characteristics of both the Economical model and Hierarchical model.

Multi-users Coordinated Sharing Grid Resource Management System

The fundamental task of grid resource management system is to supply on-demand resources service for various users and applications by aggregate heterogeneous, dynamic and increase resource. Most current grid resource management system is exclusive which brings two sides of deficiency distinctively. We bring forward and realize a multi-user coordinated sharing resource management system, which is a utility oriented resource management system to support resources sharing by multi users or multi workload schedulers, thus extendable resource serving capability can be improved. When the system is used in an “intelligent building” and “intelligent community”, the IT cost of the community enterprises can be reduced significantly.

ENHANCED MIN-MIN TASK SCHEDULING ALGORITHM BASED ON LOAD BALANCING IN GRID COMPUTING

: Grid task scheduling is one of the most important parts in Grid resource management system. In this paper, a new task scheduling algorithm is proposed and implemented based on Min-Min algorithm with taking into consideration load balancing. This proposed algorithm works by executing the small tasks by slower resources while executing the relatively large tasks by faster resources under the decided makespan. Makespan and CPU utilization are the two metrics used to evaluate the performance of the proposed algorithm rather than improvement ratio and usages rate of resource. The proposed algorithm consumes the same running time of original Min-Min algorithm even it uses a new derived Expected Sum Completed Time (ESCT) metric rather than using the standard Expected Completion Time (ECT) and Expected Execution Time(EET). Experimentations results show that the proposed algorithm produces relevant equivalent heavy resources as the guaranteed load balance schedule and makespan is reduced.

Advance reservation, co-allocation and pricing of network and computational resources in grids

Through the introduction of economic principles, a Resource Management System (RMS) can incorporate user valuations and externalities such as the usage cost of resources, into its scheduling logic. Consequently, an economic RMS can extract more value from the available infrastructure compared to traditional RMSs. In order to use the available infrastructure efficiently, a grid RMS must take an application’s data requirements into account. Although RMSs exist that support the co-allocation and advance reservation of both network and computational resources, they do not incorporate economic principles. In this paper we present ENARA, an economic RMS with advance reservation and co-allocation support for both network and computational resources. The RMS both allocates and prices resources in line with the demand and supply conditions in the network. Through the use of a novel algorithm that determines the limiting links within a network, the RMS is able to estimate prices of individual network links at the start of its planning phase. This resolves an interdependency issue between resource pricing and allocation. We demonstrate that ENARA is able to align resource allocations with user valuations, significantly increasing the user value generated compared to an online approach, while attaining high utilization levels of the infrastructure.

Job Scheduling Algorithms on Grid Computing: State-of- the Art

Scheduling jobs on computational grids is identified as NP-complete problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. This paper conducted an extensive and wide literature review to study the state of the art of grid scheduling algorithms. This review starts with an overview of the grid technologies and a description of the grid resource management systems. The evolution of the grid scheduling mechanisms is illustrated in this paper started from basic scheduling mechanisms such as Min-Min and Max-Min approaches ending with the swarm intelligence optimization methods. The swarm intelligence and evolutionary mechanisms are also presented and critically analyzed.

DESIGN AND IMPLEMENTATION OF THE MOBILE GRID RESOURCE MANAGEMENT SYSTEM

We present a design of a Mobile Grid Resource Management System aimed at integrating mobile resources to an existing Grid infrastructure, while creating a platform for interoperability with telecommunications providers. In order to reduce the load on an existing infrastructure, we separate responsibilities and run additional features on mobile resources. Finally we present the Ikaros-EG implementation, IkarosM android application and the mobile-Grid concept.The system used for our implementation was the NCSR “Demokritos” ZEUS computer Cluster, where the Ikaros-EG framework consisting of a meta-data collector and the Ikaros-EG plug-in has been installed. The ZEUS cluster has local and Grid access to data and meta-data from numerous scientific experiments. Our objective was to incorporate the mobile resources using the NCSR Demokritos campus wireless infrastructure in order to run applications concerning accounting, statistics, or further data/meta-data formatting.

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.

Economic agent-based resource management in ad hoc grid environment

The ad hoc grid is a spontaneous organisation 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 modelled 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.

Grid Resource Negotiation: Survey and New Directions

Since Grid computing systems involve large-scale resource sharing, resource management is central to their operations. Whereas there are more Grid resource management systems adopting auction, commodity market, and contract-net (tendering) models, this survey supplements and complements existing surveys by reviewing, comparing, and highlighting existing research initiatives on applying bargaining (negotiation) as a mechanism to Grid resource management. The contributions of this paper are: 1) discussing the motivations for considering bargaining models for Grid resource allocation; 2) discussing essential design considerations such as modeling devaluation of Grid resources, considering market dynamics, relaxing bargaining terms, and co-allocation of resources when building Grid negotiation mechanisms; 3) reviewing the strategies and protocols of state-of-the-art Grid negotiation mechanisms; 4) providing detailed comparisons and analyses on how state-of-the-art Grid negotiation mechanisms address the design considerations mentioned in 3); and 5) suggesting possible new directions.

AVE–CPFR WORKING CHAINS ON THE BASIS OF SELECTION MODEL OF COLLABORATIVE CREDIT-GRANTING GUARANTEE APPROACHES

This paper presents AVE and CPFR concepts and their characteristics, establishes and analyzes the AVE-based CPFR working flow, and illustrates the content of the grid resource management and the mission in relation to the corresponding grid resource management system. It focuses on the working flow of the AVE-based CPFR. On this basis, it proposes the AVE-related CPFR mechanism grounded on grid, and further analyzes its working principles, grid methods matching the AVE-related CPFR working flow, the strengths of this mechanism as well as the n-tier prediction working flow. In addition, it constructs the selection model of credit-granting guarantee approaches and provides evidence for it. Under this mechanism, credit risk in the AVE enterprises can be optimized, and the AVE chain matches the working mechanism of CPFR in their capacities of real-time resource sharing, n-tier resource allocation, mission assignment, control, and supervision. It is hoped that the distance management and risk blockage could be achieved in supply chains within AVE enterprises by establishing a strong self-organized and self-control working chain.

Design of Grid Resource Management System Based on Information Service

The author presents an improved “divided” algorithm based on the traditional, classical Min-Min scheduling algorithm, and then simulates the algorithm by GridSim Grid simulator. Comparing with the traditional Min-Min algorithm, this improved one overcomes the disadvantage of load balancing preferably. A kind of fault tolerance scheduling tactics with real-time characteristic was introduced and implemented in this paper.

Special Issue on Grid Resource Management

The nine papers in this special issue focus on Grid resource management. The issue aims to present the latest research approaches and results in designing, analyzing, and developing Grid resource management systems using economic models, agent technologies, as well as an array of approaches (e.g., adaptive workload balancing and meta-brokering) that enhance and augment current Grid scheduling systems.

Policy Adjuster-driven Grid Workflow Management for Collaborative Heart Disease Identification System

Abstract: This paper proposes a policy adjuster-driven Grid workflow management system for collaborative healthcare platform, which supports collaborative heart disease diagnosis applications. To select policies according to service level agreement of users and dynamic resource status, we devised a policy adjuster to handle workflow management polices and resource management policies using policy decision scheme. We implemented this new architecture with workflow management functions based on policy quorum based resource management system for providing poincare geometry-characterized ECG analysis and virtual heart simulation service. To evaluate our proposed system, we executed a heart disease identification application in our system and compared the performance to that of the general workflow system and PQRM system under different types of SLA. Keywords: Grid Worfklow, Collaborative Healthcare Platform, SLA, Policy Adjuster 1. Introduction There have been many attempts to create healthcare system or platform by combining the existing medical service with IT such as sensor and communication technology. In other words, the general health clinical data and information are exchanged electronically, and the patient, doctor, hospital and laboratories can be connected through communication network to perform medical practices. Such healthcare system is enabled and supported by IT, yet the core technology will be high-speed information exchange technology and high performance computing technology in distributed environment. Grid technology can meet these requirements by bringing heterogeneous resources together and allocating them efficiently to applications. Medical applications are no longer restricted to one medical institute. Some collaborated applications like heart disease simulator, an instance of workflow jobs, require several resources work together. So the healthcare platform requires collaborative job scheduling function based on resource status which is the core part of Grid resource management system [2][3]. However, the complexity of resource management for QoS guarantee in Grid significantly increases as the grid computing grows. One of the promising approaches trying to solve this problem is policy based resource management system [2] that is suitable to the complex management environments. To manage resource in Grid, it is required to have a sort of grid resource broker and scheduler that manage resources and jobs in the virtual Grid environment. Authors in [3] suggested policy-based resource management system (PQRM) architecture by consideration of Grid Service Level Agreement (SLA) and abstract architecture of grid resource management. In medical healthcare environment, beside most of single-program applications, there exist many applications which require co-process of many programs following a strict processing order. Since these applications are executed on Grid, they are called Grid workflows. Current Grid resource management systems do not consider complexity of collaborative medical applications and service level guarantee at workflow level. In this paper, we propose Grid workflow-driven healthcare platform for collaborative applications. In the sense of optimum management, PQRM [5] provides optimum resource quorum for applications, so we develop workflow-Integrated PQRM as one instance for this workflow-driven healthcare platform. PQRM’s available resource quorum is optimum resource set in resource management point of view and our workflow-driven system can provide optimum resource set in workflow management point of view. Two management philosophies require a policy decision scheme to adjust based on user’s SLA. We evaluate the proposed workflow-driven healthcare platform by measuring the completion time of collaborative heart disease simulator under different types of user SLA. DOI : 10.3745/JIPS.2008.4.3.103

Availability Modeling and Cost Optimization for the Grid Resource Management System

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid resource management system (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of resource management (RM) servers and the length limitation of request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis.

VRM: a failure-aware Grid resource management system

For resource management in Grid environments, advance reservations turned out to be very useful and hence are supported by a variety of Grid toolkits. However, failure recovery for such systems has not yet received the attention it deserves. In this paper, we address the problem of remapping reservations to other resources, when the originally selected resource fails. Instead of dealing with jobs already running, which usually means checkpointing and migration, our focus is on jobs that are scheduled on the failed resource for a specific future period of time but not started yet. The most critical factor when solving this problem is the estimation of the downtime. We avoid the drawbacks of under- or over-estimating the downtime by a dynamic load-based approach that is evaluated by extensive simulations in a Grid environment and shows superior performance compared to estimation-based approaches.

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.

Workflow applications in GridLab and PROGRESS projects

AbstractIn this paper we present our motivations and ideas for workflow management based on our experiences gained in two projects: GridLab and PROGRESS. In these projects we have been dealing with real use cases and end users' requirements for workflow management. Therefore, we were able to define and implement functional workflow extensions to the Grid(Lab) Resource Management System (GRMS). GRMS is a resource management system with a workflow engine that executes and manages jobs on remote Grid resources. One can submit to GRMS workflow experiments based on an XML workflow schema, defining flexible mechanisms for dynamic workflow control, including various types of precedence constraints, different locations of the final data products and executables, etc. All of these features allow end users to speed up remote workflow calculations and improve data management mechanisms. Copyright © 2005 John Wiley & Sons, Ltd.

The use of economic agents under price driven mechanism in grid resource management

This paper presents multi-economic agent for grid resource management. A system model is described that allows agents representing various grid resources and grid users to interact without assuming priori cooperation. The system model consists of three layers. The lower layer is the underlying grid resource. The middle layer is the agent-based grid resource management system. It consists of three types of agent and market institution that allocates resources. The grid task agents buy resources to complete tasks. Grid resource agents charge the task agents for the amount of resource capacity allocated. Grid resource agents are registered with a Grid Manager. The third layer is the user layer at which grid request agents provide interfaces to the grid user' request. The three processes involved in grid resource management are given. A price-directed algorithm for solving the grid task agent resource allocation problem is presented. A basic performance evaluation is given. Finally, some conclusions are given.