Modeling and performance evaluation of QoS-aware job scheduling of computational grids

To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated QoS-aware job dispatching policy is proposed, which correlates priorities of incoming jobs used for job selecting at the local scheduler of the grid node with the job dispatching policies at the global scheduler for computational grids. The stochastic high-level Petri net （SHLPN） model of a two-level hierarchy computational grid architecture is presented, and a model refinement is made to reduce the complexity of the model solution. A performance analysis technique based on the SHLPN is proposed to investigate the QoS-aware job scheduling policy. Numerical results show that the QoS-aware job dispatching policy outperforms the QoS-unaware job dispatching policy in balancing the high-priority jobs, and thus enables priority-based QoS.

A novel resource co-allocation model with constraints to budget and deadline in computational grid

To address the issue of resource co-allocation with constraints to budget and deadline in grid environments, a novel co-allocation model based on virtual resource agent was proposed. The model optimized resources deployment and price scheme through a three-side co-allocation mechanism, and applied queuing system to model the work of grid resources for providing quantitative deadline guarantees for grid applications. The validity and solutions of the model were presented theoretically. Extensive simulations were conducted to examine the effectiveness and the performance of the model by comparing with other co-allocation policies in terms of deadline violation rate, resource benefit and resource utilization. Experimental results show that compared with the three typical co-allocation policies, the proposed model can reduce the deadline violation rate to about 3.5% for the grid applications with constraints to budget and deadline. Also, the system benefits can be increased by about 30% compared with the those widely-used co-allocation policies.

Multiple QoS modeling and algorithm in computational grid

Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.

Improved algorithmic mechanism based on game theory in computational grids

Computational grids （CGs） aim to offer pervasive access to a diverse collection of geographically distributed resources owned by different serf-interested agents or organizations. These agents may manipulate the resource allocation algorithm in their own benefit, and their selfish behavior may lead to severe performance degradation and poor efficiency. In this paper, game theory is introduced to solve the problem of barging for resource collection in heterogeneous distributed systems. By using the Cournot model that is an important model in static and complete information games, the algorithm is optimized in order to maximize the benefit. It can be seen that the approach is more suitable to the real situation and has practical use. Validity of the solutions is shown.

Task-and-role-based access-control model for computational grid

Access control in a grid environment is a challenging issue because the heterogeneous nature and independent administration of geographically dispersed resources in grid require access control to use fine-grained policies. We established a task-and-role-based access-control model for computational grid (CG-TRBAC model), integrating the concepts of role-based access control (RBAC) and task-based access control (TBAC). In this model, condition restrictions are defined and concepts specifically tailored to Workflow Management System are simplified or omitted so that role assignment and security administration fit computational grid better than traditional models; permissions are mutable with the task status and system variables, and can be dynamically controlled. The CG-TRBAC model is proved flexible and extendible. It can implement different control policies. It embodies the security principle of least privilege and executes active dynamic authorization. A task attribute can be extended to satisfy different requirements in a real grid system.

A scheduling strategy for finite element analysis on the computational grid

The computational grid provides a promising platform for the deployment of various high-performance computing applications. A grid system consists of heterogeneous resource domains, while the computational tasks of finite element analysis may differ in demand of computing power. The cost-effective utilization of resources in the grid can be obtained through scheduling tasks to optimal resource domains. Firstly, a cost-effective scheduling strategy is presented for finite element applications. Secondly, aiming at the conjugate gradient solver stemming from finite element analysis, a performance evaluation formula is presented for determining optimal resouree domains, which is derived from phase parallel model and takes the heterogeneous characteristic of resource domains into account. Finally, experimental results show that the presented formula delivers a good estimation of the actual execution time, and indicate that the presented formula can be used to determine optimal resource domains in the grid environment.

A New Method for Out-of-core Applications on Computational Grids

More and more out of core problems that involve solving large amounts of data are researched by scientists. The computational grid provides a wide and scalable environment for those large scale computations. A new method supporting out of core computations on grids is presented in this paper. The framework and the data storage strategy are described, based on which an easy and efficient out of core programming interface is provided for the programmers.

A novel deadline and budget constrained scheduling heuristics for computational grids

The conventional deadline and budget constrained （DBC） scheduling heuristics for economic-based computational grids does not take the inconsistency of grid heterogeneity into account, which can lead to decline of application completion ratios. Motivated by this fact, a novel DI3C scheduling heuristics was proposed to deal with sequential workflow applications. In order to valuate the inconsistency, the relative cost （RC） metric was introduced, which was used to indicate the task-starving degree for resources. The new algorithm assigns tasks to resources, considering completion time, budget and RC together. The GridSim toolkit and the benchmark suites of the standard performance evaluation corporation （SPEC） were used to simulate the heterogeneous grid environment and applications. The experimental results show that the task and workflow completion ratios of the new heuristics are higher than those of the conventional heuristics.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Construction of overlay network based on P2P technology in Grid environment

Based on the advantages of both Grid and peer-to-peer （P2P） networks, an overlay network in the Grid environment is constructed by P2P technologies by a modified version of the Chord protocol. In this mechanism, different nodes＇ accesses to different resources are determined by their contribution. Therefore, the heterogeneous resources of virtual organizations in large-scale Grid can be effectively integrated, and the key node failure as well as system bottleneck in the traditional Grid environment is eliminated. The experimental results indicate that this management mechanism can achieve better average performance in the Grid environment and maintain the P2P characteristics as well.

Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

An Improved Grid Security Infrastructure by Trusted Computing

Current delegation mechanism of grid security infrastructure （GSI） can＇t satisfy the requirement of dynamic, distributed and practical security in grid virtual organization. To improve this situation, a TC-enabled GSI is discussed in this paper. With TC-enabled GSI, a practical delegation solution is proposed in this paper through enforcing fine granularity policy over distributed platforms with the emerging trusted computing technologies. Here trusted platform module is treated as a tamper-resistance module to improve grid security infrastructure. With the implement of Project Daonity, it is demonstrated that the solution could gain dynamic and distributed security in grid environment.

Remote Sensing Image Deblurring Based on Grid Computation

In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.

“Digital Region” in the Context of the “Grid Computing”

This paper is to construct a “digital local, regional, region” information framework based on the technology of “SIG” and its significance and application to the regional sustainable development evaluation system. First, the concept of the “grid computing” and “SIG” is interpreted and discussed, then the relationship between the “grid computing” and “digital region” is analyzed, and the framework of the “digital region” is put forward. Finally, the significance and application of “grid computing” to the “region sustainable development evaluation system” are discussed.

Performance Predictable ServiceBSP Model for Grid Computing

This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP （integer program） approach, the Service Selection Agent selects the computation services with global optimized QoS （quality of service） consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.

Resource management and job scheduling of China earthquake grid experiment system: Construction of resource management and job dynamic scheduling model ProRMJS

Grid technique is taken as the third generation internet technology and resource management is the core of it. Aiming at the problems of resource management of CEDAGrid （China Earthquake Disaster Alleviation and Simulation Grid） in its preliminary construction, this paper presents a resource management and job scheduling model： ProRMJS to solve these problems. For platform supposed agreeably each computing node can provide computation service, ProRMJS uses ＂computation pool＂ to support scheduler, and then the scheduler allocates jobs dynamically according to computing capability and status of each node to ensure the stability of the platform. At the same time, ProRMJS monitors the status of job on each node and sets a time threshold to manage the job scheduling. By estimating the computing capability of each node, ProRMJS allocates jobs on demand to solve the problem of supposing each node can finish the job acquiescently. When calculating the computing capability of each node, ProRMJS allows for the various factors that affect the computing capability and then the efficiency of the platform is improved. Finally the validity of the model is verified by an example.

Using Hash Tree for Delegation Revocation in Grids

Grid security infrastructure （GSI） provides the security in grids by using proxy certificates to delegate the work of authentication. At present, revocation proxy certificate has two kinds of methods, one is using certificate revocation list （CRL） and the other is giving the certificate a short period of validity. However, when a lot of certifications are revoked, CRL will be the burden in the system. If the certificate has a short period of validity, entities should be often updating the certificate. In this paper, we propose a scheme for proxy certificate revocation using hash tree. Our scheme only needs hash value comparisons to achieve the purpose of certificate revocation. Previous two methods have to wait the expiration of the certificate. Therefore, our scheme is more flexible than previous methods.

Adaptive Trust Management Framework in P2P Grid Computing

In P2P Grid computing systems, the authorization decision is often tackled by two different trust management methods： policy-based approach, where authorization are built on logical rules and verifiable properties encoded in signed credentials, and reputation-based approach, based on collecting, aggregating and disseminating reputation among the peers. However, the overhead caused by proof of compliance on authorization and the absence of certifying authorities may negate the strong and objective security advantages of policy-based approach, whilst vagueness, complexity and inaccurate characterization caused by reputation evolution may eliminate the quantitative and flexible advantages of reputation-based approach. We propose an adaptive trust management framework, which combines the merit of policy proof and reputation evolution such that authorization is aware of not only the strong and objective security traits, but also the calculability and the availability security traits. Finally, the framework of system is proposed.

Grid Service Framework: Supporting Multi-Models Parallel Grid Programming

Web service is a grid computing technology that promises greater ease-of-use and interoperability than previous distributed computing technologies. This paper proposed Group Service Framework, a grid computing platform based on Microsoft. NET that use web service to: (1) locate and harness volunteer computing resources for different applications, and (2) support multi-models such as Master/Slave, Divide and Conquer, Phase Parallel and so forth parallel programming paradigms in Grid environment, (3) allocate data and balance load dynamically and transparently for grid computing application. The Grid Service Framework based on Microsoft. NET was used to implement several simple parallel computing applications. The results show that the proposed Group Service Framework is suitable for generic parallel numerical computing.

THE STRATEGY OF RESOURCE MANAGEMENT BASED ON GRID COMPUTING

This paper analyzes the defaults of traditional method according to the resource management method of grid computing based on virtual organization. It supports the concept to ameliorate the resource management with mobile agent and gives the ameliorated resource management model. Also pointed out is the methodology of ameliorating resource management and the way to realize in reality.