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In the ever-evolving landscape of computational grid systems, the meticulous selection of resources tailored to specific tasks is a formidable challenge. This paper introduces an efficient load distribution strategy known as Load Distribution Based Resource Allocation (LDRA), one of the foremost goals is to allocate resources to gain enhanced resource utilization and also try to achieve least possible execution time to fulfil the need for grid systems. A comprehensive performance evaluation unfolds to elevate grid efficiency, pitting LDRA against existing heuristics using the ETC Simulation Benchmark. The study expands further on the real-world dataset from the Gaia Cluster Configurations (https://hpc.uni.lu/systems/gaia/) to verify its significance in the real environment. The LDRA algorithm emerges with superior performance when compared to state-of-the-art such as Max–Min, Opportunistic Load Balancing, AlgHybrid_LB, and Resource Aware Load Balancing for resource utilization, makespan, flowtime, and energy efficiency in the majority of the cases in experimental evaluation. In some cases, the experimental results show that LDRA's usage of the grid resources is remarkable, reaching over 99% in four cases and approaching 98% in two cases of the ETC simulation benchmark. These accomplishments are further mirrored in the evaluation against real datasets, where LDRA's performance among peers is nothing short of exemplary in the cases under study. [ABSTRACT FROM AUTHOR]

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. [ABSTRACT FROM AUTHOR]

Load balancing is a very important and complex problem in computational grids. In load balancing, jobs should be effectively distributed among resources in order to minimize the average completion time and maximize the utilization of all resources even those with low reliabilities and capacities. However, using the less reliable and slow resources implies worse completion time, whereas always selecting the powerful and reliable resources undermines the utilization of other resources. So, it is essential to develop an efficient load balancing method which makes a good tradeoff between these criteria in a way that satisfies the quality of service of jobs and fairly distributes jobs between resources based on their reliabilities and capacities. This paper proposes an efficient multicriteria load balancing method using technique for order preference by similarity to ideal solution which treats load balancing as a multi criteria decision making problem. Also, an effective weighting mechanism is proposed, which adaptively adjusts the weights of the considered criteria according to the system's current state and jobs' characteristics. This mechanism can make an efficient tradeoff between the considered criteria and accurately reflect the importance of each one. By simulation, the proposed method was evaluated and compared with other approaches from the literature. In the range of examined parameters' values, the simulation results show that proposed method minimizes the average completion time by 8.7–15.7%, increases the throughput ratio up to 15.8–19.4%, and maximizes the load balancing level by 7.68–20.1%. [ABSTRACT FROM AUTHOR]

Algorithms of the optimal arrangement of heat sources with volumetric heat release within regions of a complex geometric shape are considered. The distribution found has the minimum total power and provides the temperature in the given temperature corridor. Finite-dimensional approximations of the original problem are constructed in the form of a linear programming problem. A method is given for constructing a finite-difference scheme for solving the heat equation, as well as a brief description of the developed software modules for constructing grids and solving equations. Several computer experiments have been carried out using the developed programs. [ABSTRACT FROM AUTHOR]

Computational grid (CG) environment has a group of autonomous dissimilar distributed computing systems to provide service to user tasks. To attain the auspicious usefulness of CG resources, basically best scheduling algorithms are important. The CG service users are very cautious in time needed for task completion. To meet multiple requirements of the user, the job allocation problem in CG is designed as a multi-objective problem. Evolutionary algorithms have an efficient meta-heuristic technique for optimization problem solving. This work introduces a MapReduce model for non-dominated sorting genetic algorithm (NSGA-II) for independent job scheduling in a CG. This research work attempts to find the optimal schedules by considering makespan and flowtime minimization. A fuzzy membership function is used to analyze the efficiency of the schedule. Set of benchmark instances is used to test the algorithm. Experimental results show that MapReduce model-based NSGA-II generated finer solutions in less time than the MapReduce model-based weighted sum multi-objective genetic algorithm (WMOGA) which is also implemented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents the calculation results by a reactor model, numerical simulation in ANSYS Fluent CFD software, and the results of studying the flow structure and the burn-up fuel in the reverse-flow combustion chamber. These results are intended to create a methodology for further refinement of a combustion process in a combustion chamber. [ABSTRACT FROM AUTHOR]

Computational grids are established with the intention of providing shared access to hardware and software based resources with special reference to increased computational capabilities. Fault tolerance is one of the most important issues faced by the computational grids. The main contribution of this survey is the creation of an extended classification of problems that incur in the computational grid environments. The proposed classification will help researchers, developers, and maintainers of grids to understand the types of issues to be anticipated. Moreover, different types of problems, such as omission, interaction, and timing related have been identified that need to be handled on various layers of the computational grid. In this survey, an analysis and examination is also performed pertaining to the fault tolerance and fault detection mechanisms. Our conclusion is that a dependable and reliable grid can only be established when more emphasis is on fault identification. Moreover, our survey reveals that adaptive and intelligent fault identification, and tolerance techniques can improve the dependability of grid working environments. [ABSTRACT FROM AUTHOR]

Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Non-dominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multi-objective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives. [ABSTRACT FROM AUTHOR]

Scheduling performance in computational grid can potentially benefit a lot from accurate execution time estimation for parallel jobs. Most existing approaches for the parallel job execution time estimation, however, require ample past job traces and the explicit correlations between the job execution time and the outer layout parameters such as the consumed processor numbers, the user-estimated execution time and the job ID, which are hard to obtain or reveal. This paper presents and evaluates a novel execution time estimation approach for parallel jobs, the user-behavior clustering for execution time estimation, which can give more accurate execution time estimation for parallel jobs through exploring the job similarity and revealing the user submission patterns. Experiment results show that compared to the state-of-art algorithms, our approach can improve the accuracy of the job execution time estimation up to 5.6 %, meanwhile the time that our approach spends on calculation can be reduced up to 3.8 %. [ABSTRACT FROM AUTHOR]

A numerical investigation of flows in a cyclone chamber has been carried out at the fraction of bottom blast φ =0-0.5, at the values of the dimensionless pinch diameter d/D =0.7 with different locations and configurations of nozzles for air intake. In the simulation of swirling flows, the well-known k-ε and k-ω turbulence models, as well as the laminar flow model, were used. A satisfactory agreement between the results of numerical simulation and experimental data at d/D =0.5-0.7 is obtained. For a chamber with a relative pinch diameter d/D =0.3 the calculated flow parameters differ substantially from experimental values. [ABSTRACT FROM AUTHOR]

Performance is a main issue in parallel application development. Dynamic tuning is a technique that changes certain applications' parameters on-line to improve their performance adapting the execution to actual conditions. To perform that, it is necessary to collect measurements, analyze application behavior and carry out tuning actions during the application execution. Computational Grids present proclivity for dynamic changes in the environment during the application execution. Therefore, dynamic tuning tools are necessary to reach the expected performance indexes of applications on those environments. This paper addresses the dynamic tuning of parallel/distributed applications on Computational Grids. We analyze Grid environments to determine their characteristics and we present the development of dynamic tuning tool GMATE enabled for such environments. The performance analysis is based on performance models that indicate how to improve the application execution. A particular problem which provokes performance bottlenecks is the load imbalance in Master/Worker applications. A heuristic to dynamically tune granularity of work and number of workers is proposed. Finally, we describe the experimental validation of the performance model and its applicability on a set of real parallel applications. [ABSTRACT FROM AUTHOR]

Reaction and diffusion processes are used to model chemical and biological processes over a wide range of spatial and temporal scales. Several routes to the diffusion process at various levels of description in time and space are discussed and the master equation for spatially discretized systems involving reaction and diffusion is developed. We discuss an estimator for the appropriate compartment size for simulating reaction-diffusion systems and introduce a measure of fluctuations in a discretized system. We then describe a new computational algorithm for implementing a modified Gillespie method for compartmental systems in which reactions are aggregated into equivalence classes and computational cells are searched via an optimized tree structure. Finally, we discuss several examples that illustrate the issues that have to be addressed in general systems. [ABSTRACT FROM AUTHOR]

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’sTrading 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. [ABSTRACT FROM AUTHOR]

An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler ( Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios. [ABSTRACT FROM AUTHOR]

The Computational Grid (CG) provides a wide distributed platform for high end computing intensive applications. Scheduling on Computational grid is known to be NP-Hard problem and requires an efficient solution. Recently, quantum inspired computing has been introduced in the literature to solve such a complex combinatorial optimization problem efficiently. Combination of Genetic Algorithm (GA) and quantum concept evolves a new meta-heuristic technique known as Quantum Genetic Algorithms (QGA). QGA is a search procedure based on evolutionary computation and Quantum Computing (QC). This paper proposes a novel technique of scheduling in computational grid using QGA. The work simulates the model to study its performance. It also makes a comparative study with a GA-based scheduling model. Simulation results reveal the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Many traditional parallel matrix computing algorithms are performed on regular resource topologies, such as mesh. However, the grid resource topology is often irregular in practice. In this paper, we present a transformation algorithm of grid resource topology for achieving virtual meshes. And on the virtual mesh, these traditional parallel algorithms can be performed in a modern computational grid environment. The basic idea of our topology transformation is to align the basic blocks of grid computational resources through permutations in a virtual mesh. Designing a cost function of heuristic search scheme for the transformation, we use it to fully exploit the computational and communicational abilities of grid resources. The experiment results show that our aligning block permutation can significantly reduce the time complexity of search tree. They also show that the heuristic search scheme can effectively find the block permutation that makes better use of computational and communicational abilities of grid resources. [ABSTRACT FROM AUTHOR]

The results of analyzing the reliability of six turbulence models are presented as applied to calculations of flows in the asymmetric plane diffuser. The influence of grid parameters upon the calculation results is studied and the accuracy of flow separation and reattachment points prediction is determined. Also carried out is the comparison of processor time spent for calculations with the aid of each model. [ABSTRACT FROM AUTHOR]

Under modern conditions, and in perspective, an approach with use of rectangular grids in the form of GIS elevation rasters offers a number of ponderable advantages. [ABSTRACT FROM AUTHOR]

Due to recent advancements in mobile computing and communication technologies, mobile ad hoc computational Grids are emerging as a new computing paradigm, enabling innovative applications through sharing of computing resources among mobile devices without any pre-existing network infrastructure. Energy-efficient resource allocation is one of the key issues in mobile ad hoc computational Grids due to limited battery life of mobile nodes. To reduce energy consumption, we propose a hybrid power-based resource allocation scheme for allocation of interdependent tasks to nodes within mobile ad hoc computational Grid. The basic idea is to exploit dependencies and task type, and allocate interdependent tasks to nodes accessible at minimum transmission power. We also propose a power-based algorithm to search a group of closest nodes to allocate a set of interdependent tasks. Compared to traditional algorithms, complexity of proposed algorithm depends on number of transmission power levels rather than number of nodes within a Grid. The scheme is validated in a simulation environment using various workloads and parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The grid is a promising infrastructure that can allow scientists and engineers to access resources among geographically distributed environments. Grid computing is a new technology which focuses on aggregating resources (e.g., processor cycles, disk storage, and contents) from a large-scale computing platform. Making grid computing a reality requires a resource broker to manage and monitor available resources. This paper presents a workflow-based resource broker whose main functions are matching available resources with user requests and considering network information statuses during matchmaking in computational grids. The resource broker provides a graphic user interface for accessing available and the appropriate resources via user credentials. This broker uses the Ganglia and NWS tools to monitor resource status and network-related information, respectively. Then we propose a history-based execution time estimation model to predict the execution time of parallel applications, according to previous execution results. The experimental results show that our model can accurately predict the execution time of embarrassingly parallel applications. We also report on using the Globus Toolkit to construct a grid platform called the TIGER project that integrates resources distributed across five universities in Taichung city, Taiwan, where the resource broker was developed. [ABSTRACT FROM AUTHOR]

The rapid advances of network technologies shed light on many aspects of the practicability of large scale ubiquitous computing. Grid technology has been recognized as an efficient solution to coordinate large-scale shared resources and execute complex applications in heterogeneous network environments. The problem of resource management and task allocation has always been one of the main challenges. In this paper, we present an efficient task allocation strategy for distributing tasks onto computing nodes in the underlying heterogeneous networks. The contribution of the proposed technique is to minimize average turnaround time by dispatching tasks to processors with smallest communication ratio. System throughput could be also enhanced by dispersing processor idle time. The proposed technique can be applied to heterogeneous cluster systems as well as computational grid environments, in which the communication costs vary in different clusters. Experimental results show that the proposed scheme outperforms other previous algorithms in terms of throughput and turnaround time. [ABSTRACT FROM AUTHOR]

Computational Grids (CGs) are large scale dynamical networks of geographically distributed peer resource clusters. These clusters are independent but cooperating computing systems bound by a management framework for the provision of computing services, called Grid Services. In its basic form, the Grid scheduling problem consists in finding at least one cluster that has the capacity to handle, within the constraints of a specified quality of service, a user service request submitted to the CG. Since CGs span distinct management domains, the scheduling process has to be decentralized. Furthermore, it has to account for the ubiquitous uncertainty on the state of the CG. In this paper, we propose a scalable distributed Entropy-based scheduling approach that utilizes a Markov chain model to capture the dynamics of the service capacity state. An entropy-based quantification of the uncertainty on the service capacity information is developed and explicitly integrated within the proposed Grid scheduling approach. The performance of the proposed scheduling strategy is validated, through simulation, against a random delegation scheme and a load balancing-based scheduling strategy with respect to throughput, exploitation and convergence speed, respectively. [ABSTRACT FROM AUTHOR]

Cooperation of multi-domain massively parallel processor systems in computing grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, network adaptability and scalability raise the challenge for the international design of multisite job scheduling models and algorithms. It presents multisite job scheduling schema through the introduction of multisite job scheduling model and the performance model under the grid environment. It introduces two job multisite and cooperative scheduling models and algorithms with the core of the optimal and greedy-heuristic resource selection strategies. Meanwhile, compared with single and multisite cooperative scheduling models and algorithms introduced by Sabin, Yahyapour and other persons, the validity and advance of the scheduling model and the performance model herein are proved. [ABSTRACT FROM AUTHOR]

We present the latest instantiation of GridSAT [1], a distributed and complete satisfiability solver that is explicitly designed to aggregate Grid resources for application performance. GridSAT was previously shown to outperform the state-of-the-art sequential solvers. In this work, we explore the unprecedented solving power GridSAT enables through algorithmic and implementation innovations. We describe the implementation techniques that allow GridSAT to leverage a variety of high-end batch-scheduled resources, clusters, interactive workstations, and personal computing resources through autonomous scheduling, checkpoint scheduling, and work migration. These innovations have allowed GridSAT to solve a set of ‘hard’ and previously unsolved industrial and community satisfiability problems. In addition to this new solution power, GridSAT also outperforms the otherwise highest performance general solvers on the annual SAT competition [21] performance benchmarks. [ABSTRACT FROM AUTHOR]

We investigate the quality of solutions obtained from sample-average approximations to two-stage stochastic linear programs with recourse. We use a recently developed software tool executing on a computational grid to solve many large instances of these problems, allowing us to obtain high-quality solutions and to verify optimality and near-optimality of the computed solutions in various ways. [ABSTRACT FROM AUTHOR]

A general scheduling framework (GSF) for independent tasks in computational Grid is proposed in this paper, which modeled by Petri net and located on the layer of Grid scheduler. Furthermore, a new mapping algorithm aimed at time and cost is designed on the basis of this framework. The algorithm uses weighted average fuzzy applicability to express the matching degree between available machines and independent tasks. Some existent heuristic algorithms are tested in GSF, and the results of simulation and comparison not only show good flexibility and adaptability of GSF, but also prove that, given a certain aim, the new algorithm can consider the factors of time and cost as a whole and its performance is higher than those mentioned algorithms. [ABSTRACT FROM AUTHOR]