Grid Computing Environments Research Articles

Energy-Aware Workflow Scheduling in Grid Under QoS Constraints

In this study, we have considered the problem of scheduling precedence-constraint parallel applications (workflows) in heterogeneous grid-computing environment. Recently many heuristics have been devoted to grid scheduling typically restricted to optimizing the execution time (makespan) only without paying much concentration on energy consumption. Reducing energy consumption can bring various advantages like reducing operating costs, environmental perspective and increase in system reliability. This paper aims to develop energy-aware task scheduling algorithm in grid based on the dynamic voltage and frequency scaling (DVFS) technique. The user negotiates with the service provider on their quality of service (QoS) requirements along with green computing specifications to reach the service level agreement. With the use of DVFS, the algorithm minimizes the energy consumption of task execution while satisfying the QoS constraints (deadline). The proposed static scheduling algorithm works in three phases: deadline distribution, tasks ordering and then assigning the best services to tasks along with selecting the appropriate voltage levels while meeting its sub-deadline. The simulation results using randomly generated task graphs and task graphs corresponding to real-world problems exhibit that the proposed algorithm achieves energy efficiency and reduces energy consumption up to 68 % with the increase in 30 % of the execution time.

Comparative Analysis of Scheduling Algorithms in Computational Grid Environment

This paper tracks the development of Grid Computing since its inception in the late 1990s to its dominance in today’s world of Distributed Computing and Information Technology. It focuses on the recent developments that spurred our interest to take up this field of research with emphasis on the algorithms we are researching for job scheduling and load balancing in the Grid Environment. The entire structure of the Grid Environment is dynamic and hybrid by nature, changing with the availability and the capability of resources or hosts that perform user tasks and the Quality of Service requirements of the tasks themselves. This makes the problem of developing an optimal task-toresource schedule that ensures proper load balancing and also produces the minimum overall makespan (time to complete scheduled tasks) an NP-Hard Problem. Our attempt in this research is not to find the optimal solution for this problem, but to analyze and test various algorithms that produce acceptable performance in the commonly occurring practical scenarios. The algorithms considered for research and analysis include the classical First Come First Served Algorithm that schedules jobs to the best possible resources on arrival basis, Adaptive Workload Balancing Algorithm that essentially considers the job pool and the resource pool at a given point of time to give the optimal load-balanced schedule, and a brief insight into Genetic Algorithms that apply heuristics to perform optimization for some job criteria. Lastly, we include two algorithms, Fastest Processor to Largest Task First and Nearest Deadline First Served, which we have indigenously developed and are currently testing for performance.

A Survey on Scheduling Heuristics in Grid Computing Environment

Job scheduling is one of the thrust research area in the discipline of Grid computing. Scheduling in the Grid environment is not only complicated but also known to be NP-Complete problem and that is all due to its unique characteristics. Thus, there are limited opportunities to find an optimal solution. In recent past, many eminent researchers reported a variety of Scheduling Heuristics that can have a substantial impact on the performance of the Grid systems. Unfortunately, it gives rise to difficulty in evaluating and keeping track of those solutions. Therefore, the motivation of this comprehensive study is to present firstly, an in-depth review of the topic under discussion mostly in the perspective of Grid Scheduling environment, and secondly, a proposal for a new state-of-the-art classification of the existing Scheduling Heuristics. All these Heuristics in each category have been further studied based on significant parameters frequently used in Scheduling Heuristics. The final part of this study includes a fair assessment of those mostly used dominating parameters. This report deals with the key concepts behind existing Scheduling Heuristics including Objectives, Types of Job Scheduling, Functionality of Grid, Nature of Grid, and the importance of the proposed classification.

Large electromagnetic simulation by hybrid approach on large‐scale parallel computing systems

SummaryThis paper deals with the parallel computing and computation time prediction applied to computational electromagnetics based on rigorous methods for large and complex structures, such as tunnels and airplane cabins. The main objective of this work is represented by the electromagnetic computation of an airplane cabin in order to characterize the propagation channel for a given frequency inside the structure. A well‐known three‐dimensional numerical method, transmission‐line matrix, is adopted for this work. Transmission‐line matrix modelling method is applied to volumes, using the symmetrical condensed node. The planar structures are modelled with the mode matching approach. In order to analyse the electromagnetic field at high frequencies inside large structures as an airplane cabin, the transmission‐line matrix mesh step is very small compared with the size of the physical structure. Thus, the total amount of data that need to be processed exceeds the RAM memory presented on a traditional computing machine. The implementation of the parallel transmission‐line matrix algorithm is a viable solution to this problem taking full advantage of the parallel and distributed architectures, as clusters, grids and supercomputers. The results prove the benefits of the grid computing and supercomputer environments to solve electrically large structures. The parallel hybrid approach, based on Message Passing Interface, is designed for Single Programme Multiple Data programming model. The study highlights the role of parallelization scheme, grid versus supercomputer, with respect to the size of the problem and its repartition. In order to access the computing resources available on different parallel computing platforms, users need to respect a charter containing time and resource number restrictions when doing reservations. So, a time prediction model based on the profile of the transmission‐line matrix algorithm has been developed to allow safe reservations of the computing nodes on grid environment. To have a good agreement between the measured and the predicted values for the computation time, the problem of the cache misses appearing during the simulation has been considered in the profile‐based prediction model of the transmission‐line matrix/modal hybrid application, according to the processor cache memory size. The maximum error for the estimated values of the computation times is about 6.09%. The relevance of this work is based on the fact that is given a complete tool to deal with the large electromagnetic structures: a parallel transmission‐line matrix implementation to take the advantage of the distributed architectures and a time prediction model in order to optimize the resource reservation and the simulation execution on the computing nodes. Copyright © 2014 John Wiley & Sons, Ltd.

A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to LoadBalancing In Grid Computing Environments

Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities.This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources’ usage.

Resource Scheduling Based on Ant Colony Optimization Algorithm in Grid Computing Environments

Resource Scheduling Based on Ant Colony Optimization Algorithm in Grid Computing Environments

Dynamic Fault Tolerance in Desktop Grids Based On Reliability

Fault tolerance is an important issue to guarantee reliable execution of tasks in computational desktop grid environment where execution failures are frequently expected, requires the availability of efficient fault tolerant strategies able to effectively deal with resource failures and/or unplanned periods of unavailability. In this paper we present a Dynamic Fault Tolerant strategy that, rather than just tolerating faults as done by traditional fault-tolerant schedulers, exploit the information concerning size of task, resource speed and resource reliability by maintaining resource history to improve application performance. The performance of this strategy has been compared via simulation with those attained by traditional fault-tolerant strategy. Our results, obtained by considering a set of realistic scenarios modeled after real Desktop Grids, show that our approach results in better application performance and resource utilization.

Host Load Prediction in Computational Grid Environment

When sudden load arises in a grid then load should be transferred to some idle node hence due to load sharing, server down condition not occur hence we predict the load on node and shared it to idle node this is termed as load forecasting.In this paper, we did simulation of grid CPU load in distributed manner which provide the monitoring on each host in network and load of grid is predicted using bpn (back propagation neural network) algorithm. Which provides the effective results in prediction, in addition of that a new predictive algorithm is proposed implemented and compared to the bpn algorithm. In proposed algorithm we calculate the accuracy and compared with bpn algorithm accuracy, after implementation of both methods and simulation of Host load we find the proposed method is much effective then the previously proposed method of BPN algorithm.

Using the Efficient Global Optimization Algorithm to Assist Nuclear Criticality Safety Assessment

Nuclear criticality safety assessment often requires groupwise Monte Carlo simulations of k-effective in order to check subcriticality of the system of interest. A typical task to be performed by safety assessors is hence to find the worst combination of input parameters of the criticality Monte Carlo code (i.e., leading to maximum reactivity) over the whole operating range. Then, checking subcriticality can be done by solving a maximization problem where the input-output map defined by the Monte Carlo code expectation (or an upper quantile) stands for the objective function or “parametric” model. This straightforward view of criticality parametric calculations complies with recent works in Design of Computer Experiments, an active research field in applied statistics. This framework provides a robust support to enhance and consolidate good practices in criticality safety assessment. Indeed, supplementing the standard “expert-driven” assessment by a suitable optimization algorithm may be helpful to increase the reliability of the whole process and the robustness of its conclusions. Such a new safety practice is intended to rely on both well-suited mathematical tools (compliant optimization algorithms) and computing infrastructure (a flexible grid-computing environment).

Using the Efficient Global Optimization Algorithm to Assist Nuclear Criticality Safety Assessment

Nuclear criticality safety assessment often requires groupwise Monte Carlo simulations of k-effective in order to check subcriticality of the system of interest. A typical task to be performed by safety assessors is hence to find the worst combination of input parameters of the criticality Monte Carlo code (i.e., leading to maximum reactivity) over the whole operating range. Then, checking subcriticality can be done by solving a maximization problem where the input-output map defined by the Monte Carlo code expectation (or an upper quantile) stands for the objective function or “parametric” model. This straightforward view of criticality parametric calculations complies with recent works in Design of Computer Experiments, an active research field in applied statistics. This framework provides a robust support to enhance and consolidate good practices in criticality safety assessment. Indeed, supplementing the standard “expert-driven” assessment by a suitable optimization algorithm may be helpful to increase the reliability of the whole process and the robustness of its conclusions. Such a new safety practice is intended to rely on both well-suited mathematical tools (compliant optimization algorithms) and computing infrastructure (a flexible grid-computing environment).

Service Oriented Load Balancing Framework in Computational Grid Environment

Grid computing or computational grid has become a vast research field in academics. It is a promising platform that provides resource sharing through multi-institutional virtual organizations for dynamic problem solving. Such platforms are much more cost-effective than traditional high performance computing systems. Due to the provision of scalability of resources, these days grid computing has become popular in industry as well. However, computational grid has different constraints and requirements to those of traditional high performance computing systems. In order to fully exploit such grid systems, resource management and scheduling are key challenges, where issues of task allocation and load balancing represent a common problem for most grid systems as because the load scenarios of individual grid resources are dynamic in nature. The objective of this paper is to review different existing load balancing algorithms or techniques applicable in grid computing and propose a layered service oriented framework for computational grid to solve the prevailing problem of dynamic load balancing.

Integration of a computational grid and virtual geographic environment to facilitate air pollution simulation

Air pollution, which is a global environmental problem, has been the hot research area among the scientists in the geoscience community. Air pollution simulation is of low-efficiency caused by the computation-intensive models, such as MM5 or WRF, and the complicated and unfriendly user interface. These issues are addressed in this paper by integrating computational grid and virtual geographic environment (VGE). The computational grid is employed to improve the computation efficiency of air pollution models. The VGE is used as a straightforward and easy to use interface to navigate the air pollution modeling workflow and improve the operational efficiencies of the models with respect to initiation, computation, and output visualization. On the aspect of technique implementation, this paper designs a framework and addresses the methodologies of the integration of computational grid and VGE. The prototype system, which integrates the computation grid of the Chinese University of Hong Kong (CUGrid) and a VGE to facilitate air pollution simulation based on the Mesoscale Model Version 5 (MM5), was developed. Based on the prototype system, a case was tested and the results indicate that the efficiencies of air pollution simulation on the model computation and workflow operation based on MM5 are increased significantly. This success also proves the reasonability of our general contribution of integrating computational grid and VGE to facilitate air pollution simulation.

An Efficient Algorithm for Resource Allocation in Parallel and Distributed Computing Systems

Resource allocation in heterogeneous parallel and distributed computing systems is the process of allocating user tasks to processing elements for execution such that some performance objective is optimized. In this paper, a new resource allocation algorithm for the computing grid environment is proposed. It takes into account the heterogeneity of the computational resources. It resolves the single point of failure problem which many of the current algorithms suffer from. In this algorithm, any site manager receives two kinds of tasks namely, remote tasks arriving from its associated local grid manager, and local tasks submitted directly to the site manager by local users in its domain. It allocates the grid workload based on the resources occupation ratio and the communication cost. The grid overall mean task response time is considered as the main performance metric that need to be minimized. The simulation results show that the proposed resource allocation algorithm improves the grid overall mean task response time. (Abstract)

Performance analysis of replication mechanism using mobile agent in computational grid using WADE

Grid computing is a coordinated resource sharing and solving the problems in organisations which are dynamic and virtual in nature. Many grid applications will be running in environments where interaction faults more commonly occur between diverse grid nodes. As resources may also be used outside of organisational boundaries, it becomes iteratively difficult to guarantee that a resource being used is not malicious one. Because of the diverse faults and failure conditions developing, deploying, and executing long running applications over the grid remains a challenge. Hence, fault tolerance is a primary factor for grid computing. The prototype system here is designed using agents to provide service replication, reactivation and avoids the single point of failure in a simulated computational grid environment. The agents and the workflows are provided by a common software platform called WADE. Moreover, workflows in the computational grid can also be generated using WOLF which is embedded in the WADE platform itself.

AN EFFICIENT FAULT TOLERANT SCHEDULING APPROACH FOR COMPUTATIONAL GRID

Grid computing serves as an important technology to facilitate distributed computation computational grids solve large scale scientific problems using heterogeneous geographically distributed resources. Problems like dispatching and scheduling of tasks are considered as major issues in computational grid environment. The Grid Scheduler must select proper resources for executing the tasks with less response time. There are various reasons such as network failure, overloaded resource conditions, or non-availability of required software components for execution failure. Thus, fault-tolerant systems should be able to identify and handle failures and support reliable execution in the presence of failures. Hence the integration of fault tolerance measures and communication time with scheduling gains much importance. In this study, a new fault tolerance based scheduling approach Fault Tolerant Min-Min (FTMM) for scheduling statically available meta tasks is proposed wherein failure rate and the fitness value are calculated. The performance of the fault tolerant scheduling policy is compared with min-min scheduling policy using GridSim and the results shows that the proposed policy performs better with less makespan in the presence of failures. The number of tasks successfully completed is also more when compared to the non-fault tolerant min-min scheduling policy. Thus the proposed FTMM algorithm not only achieves better hit rate but also improved makespan.

Virtualization for Grid Computing Environments to Increase the Computing Power Efficiency and Infrastructure

Virtualization for Grid Computing Environments to Increase the Computing Power Efficiency and Infrastructure

A Flight Simulator that Grouping Aircrafts Simultaneously Take off and Land in Open Grid Computing Environment

The performance of airplane in commercial airline environment is determined by, and therefore an indicator of performance measure of, the thermodynamic properties of airplane. The aim of this study was to establish the use of simulators to determine aircraft accident for a flight of airplanes and evaluate the potential of new airspace structure and airport’s runway. This indicates that there is a possibility of obtaining airplane performance from analysis and verification simulating airplane. As compared with AIRBUS Full Flight Simulator, a multiple aircrafts flight simulator that grouping aircrafts simultaneously take off and land was presented, which is basis on a parallel distributed computing in Open Grid Computing Environment (OGCE), and service oriented architecture (SOA) of software in multiple aircraft simulator, the performance of collaborative flight of multiple aircrafts is evaluated.

Evaluating decision-making performance in a grid-computing environment using DEA

Energy saving involves two direct benefits: sustainability and cost reduction, both of which Information Technologies must be aware. In this context, clusters, grids and data centres represent the hungriest consumers of energy. Energy-saving policies for these infrastructures must be applied in order to maximize their resources. The aim of this paper is to compare how efficient these policies are in each location of a grid infrastructure. By identifying efficient policies in each location and the slack in inputs and outputs of the inefficient locations, Data Envelopment Analysis presents a very useful technique for comparing and improving efficiency level. This work enables managers to uncover any misuse of resources so that corrective action can be taken.

Application of ACO for Resource Discovery in Grid Computing Environment

utilization of resources is the key factor for any environment. The grid environment is also more dynamic, it facilitates the movement of resources in and out of the environment flexibly. Hence maintenance of centralized registries is not sufficient for this demanding environment. Here, we propose a new approach for resource discovery which employs the Ant Colony Optimization (ACO) technique for resource discovery it also includes the routing information and enhances the resource discovery process.