Utilization Of Grid Resources Research Articles

Interaction of Energy-Grid-Load Based on Demand Side Response of Full-cost Electricity Price

In the research of intelligent grids, people usually pay attention to the interaction between power source and load to absorb more new energy. In contrast, the utilization of power source and load on grid resources is less considered, and energy-grid-load is not fully explored. The profound benefits and value of the three interactions. To this end, the paper proposes the interactive mode and method of power supply, power grid, and load based on full-cost electricity price. The power source side needs to consider the impact of transmission costs when optimizing economic dispatch; the load side needs to calculate the full-cost electricity price based on the power flow tracking method, clarify the load’s use of power and grid resources, and reflect the “who benefits, who bears the cost” Principles and introduce demand-side response to reflect the meaning and value of full-cost electricity prices. The research found that the calculation example analysis based on the IEEE30-node system verifies that the method in this paper can reasonably allocate the cost of the power grid according to the user’s utilization of power grid resources, thereby improving the efficiency, fairness, orderliness and safety of power grid investment and operation.

Optimal Energy Management of a Microgrid Incorporating a Novel Efficient Demand Response and Battery Storage System

In this paper, optimal economic management of a grid-connected microgrid (MG) with distributed energy resource (DER) and its interaction with incentive-based demand response programs (DRPs) is studied. The use of DR makes energy management system (EMS) of the MG an efficient tool in balancing the demand and supply, and therefore ensuring the network reliability. In this work, the cost function of customers is developed in the incentive-based DRP with the aim of receiving a more realistic incentive and then it is combined with EMS. Accordingly, the consumers offer hourly power reduction bids based on which they are sorted and then incentive-based payment model is implemented. At times, due to full utilization of grid and MG resources, the supply–demand balance cannot be maintained by respecting the consumer offers. Specific energy policies and contracts are required in this case for mandatory power curtailment in exchange for higher incentive payments by MG operator (MGO). The objective function attempts to minimize operation costs of the MG units such as Diesel Generator fuels costs, cost of power exchange with the main grid, battery energy storage system (BESS) costs and in the mean time, maximize MGO DR benefit. On the other hand, simultaneous EMS and DR management leads to a complex non-linear problem, which can be solved using whale optimization algorithm (WOA) in MATLAB software. To assess the performance of the proposed new approach, a grid-connected MG with DERs and reducible power of consumers is studied within a 24-h time cycle. Also, to verify the scalability of the implemented system, an MG with aggregators and a large scale battery is considered. Simulation results show that incorporating a developed DR into EMS is an efficient way in optimal performance of both demand and supply sides in conjunction with the goals of economic operation of MGs.

A Dynamic Load Balancing Algorithm for Computational Grid using Ant Colony Optimization

Objective: To design and implement an algorithm for load balancing with convenient utilization of heterogeneous grid resources. Methods: In this paper, we introduce Ant based Dynamic Load Balancing Algorithm (ADLBA), a decentralized dynamic load balancing algorithm using Ant Colony Optimization (ACO), which selects the best resources to be allocated to the tasks considering economic cost, resources' capacity, and local load. Results: We used the Gridsim toolkit to evaluate the efficiency of ADLBA against the Randomized Algorithm (RA) with various number of tasks and resource allocation polices. Our study results show that ADLBA outperforms RA in terms of execution cost and total application execution time (makespan), and they also show that using time-shared allocation policy in the resources leads to better results in both algorithms. Conclusion: We found that ADLBA is suitable for grid users which aim to execute their applications quickly with lower cost.

Intelligent self‐adaptive resources selection for grid applications

SummaryGrid computing is considered a promising trend, which enables the sharing of a wide variety of computational and storage resources geographically distributed. Despite the advantages of such paradigm, several problems have emerged during the last decade; most of them caused by an inefficient utilization of grid resources. The present contribution proposes an approach to improve the grid resources selection process. An optimization model for choosing grid resources in an intelligent way has been designed. A mathematical formulation to monitor the resources efficiency has also been established. Furthermore, the model provides a self‐adaptive capability to grid applications, enhancing them for dealing with the changing environmental conditions. The model applies an artificial intelligence algorithm for ensuring an efficient selection. In particular, three different versions have been implemented. Each of them uses a different algorithm. Finally, during the evaluation phase of the model, the experimental tests were performed in a real grid infrastructure. The results show that the model improves the infrastructure throughput, by increasing the finished tasks rate and by reducing the applications execution time. Copyright © 2014 John Wiley & Sons, Ltd.

Efficient Resource Provisioning by Means of Sub-domain Based Ontology and Dynamic Pricing in Grid Computing

Grid computing was introduced with the provision of delivering high performance computing through connecting distributed heterogeneous resources over the Internet. However, due to the varied nature of Grid applications and lack of suitable resource provisioning tools and mechanisms, a major part of Grid resources remains unutilized. Therefore, provisioning mechanisms must understand how the resources are defined for different applications to obtain maximum accessibility and to increase resource utilization. In this paper, we present a sub-domain based ontology resource provisioning mechanism to increase the utilization of Grid resources. The use of a sub-domain based ontology structure enables us to provision resources in terms of computational context. As the Grid targets resources across distributed domains and ownerships, inspiring mechanisms are essential to increase sharing of resources among the providers. We, therefore, extend our model to measure its effectiveness in an economic system's point of view. We evaluate the significance of using dynamic pricing over static pricing to deal with the dynamic nature of the Grid. The results show improved success probability and system's profit compared to that of traditional resource provisioning mechanisms.

A DECENTRALIZED DYNAMIC LOAD BALANCING FOR COMPUTATIONAL GRID ENVIRONMENTS

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The computational grid is a new parallel and distributed computing paradigm that provides resources for large scientific computing applications. The main techniques that are most suitable to cope with the dynamic nature of the grid are the effective utilization of grid resources and the distribution of application load among multiple resources in a grid environment. This paper addresses the problem of scheduling and load balancing in a grid environment. A Decentralized Dynamic load balancing algorithm is proposed which combines the strong points of neighbor based and cluster based load balancing techniques. This algorithm estimates system parameters such as resource processing capacity, load on each resource and transfer delay for scheduling and load balancing. A set of simulation experiments show that the proposed algorithm provides significant performance over existing ones.

Genetic Algorithm based Resource Broker for Computational Grid

In this paper, we present a resource broker architecture for a computational Grid which uses Genetic Algorithm (GA) for brokering. Resource brokering implies selection of appropriate resource providers for jobs submitted to the Grid. Resource brokering is normally done with the objective of optimizing some performance parameter such as minimizing the total cost of running the jobs or maximizing the utilization of Grid resources. It is a challenging task since the search space for the problem consists of all possible allocations of submitted jobs to available resource providers in a Grid and may be very large. GAs are found to be efficient for such optimization problems. Moreover, the configuration and workload of a Grid is dynamic in nature. Our GA based resource broker tries to address these issues so that jobs are scheduled efficiently.

On the Improvement of Grid Resource Utilization: Preventive and Reactive Rescheduling Approaches

One of the key motivations of computational and data Grids is the ability to make coordinated use of heterogeneous computing resources which are geographically dispersed. However, the provision of Quality of Service (QoS) to Grid users is still a challenge that needs the attention of the research community. Reservation of resources in advance has been proposed as a way of providing QoS guarantees but they may not always be possible. For this reason, this work focuses on meta-scheduling of jobs in advance as a way of enhancing the provision of QoS. Thereby, jobs are scheduled some time before they are actually executed, but no resource is physically reserved. One of the drawbacks of this scenario is that fragmentation may appear in resources (free time slots but not large enough to execute a job) which leads to poor resource utilization. For that reason, two techniques have been developed to tackle poor resource utilization, whose main idea consists of rescheduling already scheduled jobs so that a new incoming job can be allocated. These rescheduling techniques have been implemented within a middleware that supports meta-scheduling in advance, which relies on the GridWay meta-scheduler. Finally, these proposals have been tested using a real testbed involving heterogeneous computing resources distributed across different national organizations, with different experiments showing their efficiency.

Energy Conservation In Computational Grids

With the increasing development of high-speed wide-area networks and powerful though low cost computational resources, grid computing has emerged as an attractive computing paradigm.With recent advances in computing, the scope of grid computing has been expanded to include mobile and pervasive devices. Grids have become one of the highest consumers of energy. High performance computing is achieved at the cost of energy consumption. Increasing the performance of computer systems requires increasing number of resources which leads to higher power consumption and it creates negative impact on environment. Idle sites in grid are direct sources of energy waste. Saving energy causes two direct advantages: financial and environmental cost reduction and sustainability. With the Grid becoming a viable high-performance alternative to the traditional supercomputing environment, various aspects of Grid resource utilization which can help in energy conservation are gaining significance. In this paper we explore the possibility of reduction in energy consumption of a grid of heterogeneous computers by making use of different power states of cpu.

A Hybrid Model for Load Aware Trust Management in Grid

Problem statement: Trust management in a distributed dynamic environment like grid has been very vital since the allocation of appropriate resources to meet user request influences the success of the system. While considering a set of resources which are loaded invariably, balancing the load also contributes for the efficient resource utilization. Approach: This study proposes a trust calculation model, which considers weighted parameters like direct trust, reputation trust, load average information and network efficiency. Network efficiency also varies with the granularity of jobs, hence the experiment were conducted with an inclusion of this criteria. Results and Conclusion: The proposed Hybrid Model for Load aware Trust management system (HMLT) allocates the job based on the trust values. The resources with higher trust value get more jobs. As a result the performance of the proposed system found to be faster and has a better utilization of grid resources when studied in a grid environment.

Security Aware Parallel and Independent Job Scheduling in Grid Computing Environments Based on Adaptive Job Replication

In grid environment, jobs may be scheduled to multiple machines across different administrative domains. However, grid security is a main hurdle to make the job scheduling decision secure, reliable and fault tolerant. A s ecurity- aware parallel and independent job s cheduling algorithm in grid computing environment based on adaptive job replications was proposed. In risky and failure-prone grids, it is robust due to its adaptive job replications and rescheduling mechanism. Simulation results generated based on the SimGrid package show that a higher scheduling success of 97% rate and higher grid resource utilization of 74% can be achieved. The performance of our algorithm is better than non-security-aware scheduling algorithms based on fixed-number job replications and it is fault tolerant and scalable.

A fuzzy modeling based dynamic resource allocation strategy in service grid

One of the major challenges in service grid is to guarantee the promised quality of service (QoS) for all the admitted users or applications, while maximizing the resource utilization through dynamic resource sharing. An efficient resource allocation method should ensure the service QoS and balance the load among service grid nodes which are often highly dynamic, heterogeneous and linked by wide-area network. In this paper, a new dynamic resource allocation method is presented and analyzed based on fuzzy modeling to solve the adaptation between heterogeneous applications with multiple QoS requirements and grid resource. Simulations in service grid with heterogeneous QoS requirements reveal that the proposed dynamic resource allocation method can distribute most suitable resource among the different service quickly and sensitively as the service QoS demand varies under the constraint of achieving maximum utilization of grid resources.

Optimization With High-Cost Objective Function Evaluations in a Computing Grid and an Application To Simulation-Based Design

The emerging grid computing technologies are aimed at the creation of virtual supercomputers to conduct huge scale scientific computations by harvesting computing resources on the Internet. This paper introduces a grid-enabled implementation of an optimization program for large-scale optimization problems requiring high-cost, black-box objective function evaluations. Adopting grid computing can be particularly beneficial for building surrogates such as response surfaces and carrying out large-scale optimizations using stochastic optimization algorithms. However, several problems have to be resolved for effective utilization of grid resources because of heterogeneity in computer capacity among grid resources and limited network conditions inherent in grid systems. This paper identifies some of the problems and introduces algorithms to effectively carry out large-scale optimizations on a grid. Specifically, asynchronous genetic and particle swarm optimization algorithms are developed for grid computing environments. The performance and characteristics of the grid-enabled implementations are assessed via extensive numerical tests. Finally, structural design based on high-fidelity simulations is carried out using the proposed algorithm in a computing grid system.

Adaptive Scheduling Solution for Grid Meta-Brokering

The nearly optimal, interoperable utilization of various grid resources play an important role in the world of grids. Though well-designed, evaluated and widely used resource brokers have been developed, these existing solutions still cannot cope with the high uncertainty ruling current grid systems. To ease the simultaneous utilization of different middleware systems, researchers need to revise current solutions. In this paper we propose advanced scheduling techniques with a weighted fitness function for an adaptive Meta-Brokering Grid Service, which enables a higher level utilization of the existing grid brokers. We also set up a grid simulation environment to demonstrate the efficiency of the proposed meta-level scheduling solution. The presented evaluation results show that the proposed novel scheduling technique in the meta-brokering context delivers better performance.

Adaptive Processor Allocation for Moldable Jobs in Computational Grid

In a computational grid environment, a common practice is try to allocate an entire parallel job onto a single participating site. Sometimes a parallel job, upon its submission, cannot fit in any single site due to the occupation of some resources by running jobs. How the job scheduler handles such situations is an important issue which has the potential to further improve the utilization of grid resources as well as the performance of parallel jobs. This article develops adaptive processor allocation policies based on the moldable property of parallel jobs to deal with such situations in a heterogeneous computational grid environment. The proposed policies are evaluated through a series of simulations using real workload traces. The results indicate that the proposed adaptive processor allocation policies can further improve the system performance of a heterogeneous computational grid significantly.

XML-Based Data Model and Architecture for a Knowledge-Based Grid-Enabled Problem-Solving Environment for High-Throughput Biological Imaging

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

A Grid-based Virtual Reactor: Parallel performance and adaptive load balancing

We address the problem of porting parallel distributed applications from static homogeneous cluster environments to dynamic heterogeneous Grid resources. We introduce a generic technique for adaptive load balancing of parallel applications on heterogeneous resources and evaluate it using a case study application: a Virtual Reactor for simulation of plasma chemical vapour deposition. This application has a modular architecture with a number of loosely coupled components suitable for distribution over the Grid. It requires large parameter space exploration that allows using Grid resources for high-throughput computing. The Virtual Reactor contains a number of parallel solvers originally designed for homogeneous computer clusters that needed adaptation to the heterogeneity of the Grid. In this paper we study the performance of one of the parallel solvers, apply the technique developed for adaptive load balancing, evaluate the efficiency of this approach and outline an automated procedure for optimal utilization of heterogeneous Grid resources for high-performance parallel computing.

Fuzzy Logic-Based Secure and Fault Tolerant Job Scheduling in Grid

The uncertainties of grid sites security are main hurdle to make the job scheduling secure, reliable and fault-tolerant. Most existing scheduling algorithms use fixed-number job replications to provide fault tolerant ability and high scheduling success rate, which consume excessive resources or can not provide sufficient fault tolerant functions when grid security conditions change. In this paper a fuzzy-logic-based self-adaptive replication scheduling (FSARS) algorithm is proposed to handle the fuzziness or uncertainties of job replication number which is highly related to trust factors behind grid sites and user jobs. Remote sensing-based soil moisture extraction (RSBSME) workload experiments in real grid environment are performed to evaluate the proposed approach and the results show that high scheduling success rate of up to 95% and less grid resource utilization can be achieved through FSARS. Extensive experiments show that FSARS scales well when user jobs and grid sites increase.

Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling

In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions. We report the simulated grid performances of these new grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant min-min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of grid computing services. These risk-resilient job scheduling schemes can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment.

QoS-Based Grid Resource Management

As resource management becomes a hot research in Grid Computing area, current research forces on solving heterogeneity of grid environment, but the research on enhancing the efficiency of resource management on condition of delivering seamless QoS (quality of service) is not very abundant. In addition, current research about Grid QoS forces on importing related fruit on QoS from multimedia network to support Grid QoS. For that, a hierarchical structure of gird QoS is proposed in this paper. QoS parameters are newly classified into five categories and they can be measured at VO (virtual organization) layer. Then by making use of SNAP (service negotiation and acquisition protocol), the analysis on QoS parameter mapping and converting based on the hierarchical structure model is also addressed. At last, the research on Grid QoS is applied to scheduling heuristics to improve on Min-Min algorithm. The result of the simulation shows that QoS-based resource management can effectively improve grid resource utilization and service ask for success rate in dynamic service-oriented grid.