Stochastic Workflow Scheduling with QoS Guarantees in Grid Computing Environments

Grid computing comprises of distributed computer systems which are geographically dispersed to share the combination of resources in a heterogeneous environment. Grid computing is one of the latest technologies to solve very complex problems in scientific applications, using the resource sharing concept in the dynamic virtual organizations. The main problem occurs in most of the organization is that the resources are become idle and underutilized. In some organizations, even the server machines can often be relatively idle. Grid computing provides an approach for exploiting these underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. Effective and Efficient Task scheduling is an important aspect of Grid computing. The objective of this paper is that a grid scheduler first allocates the important(high priority) jobs to the resources and then it allocates the low prioritize job so as to achieve the maximum resource utilization rate, minimize the makespan and avoid the load balancing level problem. In this paper, we propose a new framework and QoS(Quality of Service) Priority Based Scheduling Algorithm for effective task scheduling to the resources in the grid environment. The algorithm is simulated using Java. The results show that our proposed QoS priority based scheduling algorithm gives better results in makespan and resource utilization rate than other algorithms such as QoS guided weighted mean time min (QWMTM), Min-Min and Max-Min heuristic algorithms.

Many applications involve the concepts of scheduling, such as communications, packet routing, production planning [Zhai et al., 2006], classroom arrangement [Mathaisel & Comm, 1991], aircrew scheduling [Chang, 2002], nurse scheduling [Ohki et al., 2006], food industrial [Simeonov & Simeonovova, 2002], control system [Fleming & Fonseca, 1993], resource-constrained scheduling problem [Chen, 2007] and grid computing. There are many different types of scheduling problems such as real-time, job-shop, permutation flow-shop, project scheduling and other scheduling problems have been studied intensively. However, in this work, the studied grid task scheduling problem is much more complex than above stated classic task scheduling problems. Restated, a grid application is regarded as a task scheduling problem involving tasks with inter-communication and distributed homogeneous or heterogeneous resources, and can be represented by a task interaction graph (TIG). Grid is a service for sharing computing power and data storage capacity over the Internet. The grid systems outperform simple communication between computers and aims ultimately to turn the global network of computers into one vast computational resource. Grid computing can be adopted in many applications, such as high-performance applications, large-output applications, data-intensive applications and community-centric applications. These applications major concern to efficiently schedule tasks over the available multi-processor environment provided by the grid. A grid is a collaborative environment in which one or more tasks can be submitted without knowing where the resources are or even who owns the resources [Foster et al., 2001]. The efficiency and effectiveness of grid resource management greatly depend on the scheduling algorithm [Lee et al., 2007]. Generally, in the grid environment, these resources are different over time, and such changes will affect the performance of the tasks running on the grid. In grid computing, tasks are assigned among grid system [Salman, 2002]. The purpose of task scheduling in grid is to find optimal task-processor assignment and hence minimize application completion time (total cost). Most scheduling problems in these applications are categorized into the class of NP-complete problems. This implies that it would take amount of computation time to obtain an optimal solution, especially for a large-scale scheduling problem. A variety of approaches have been applied to solve scheduling problems, such as

From Rio to Iragua--sustainability versus efficiency and equity for preventive health interventions.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

Resource use efficiency (RUE) is an ecological concept that measures the proportion of supplied resources, which is converted into new biomass, i.e. it relates realized to potential productivity. It is also commonly perceived as one of the main mechanisms linking biodiversity to ecosystem functioning based on the assumption that higher species numbers lead to more complementary and consequently more efficient use of the available resources. While there exists a large body of literature lending theoretical and experimental support to this hypothesis, there are a number of inconsistencies regarding its application: First, empirical tests use highly divergent approaches to calculate RUE. Second, the quantification of RUE is commonly based on measures of standing stock instead of productivity rates and total pools of nutrients instead of their bioavailable fractions, which both vary across systems and therefore can introduce considerable bias. Third, conceptual studies suggest that the relationship between biodiversity, productivity and RUE involves many more mechanisms than complementary resource use, resulting in variable magnitude and direction of biodiversity effects on productivity. Moreover, RUE has mainly been applied to single elements, ignoring stoichiometric or metabolic constraints that lead to co-limitation by multiple resources. In this review we illustrate and discuss the use of RUE within and across systems and highlight how the various drivers of RUE affect the diversity-productivity relationship with increasing temporal and spatial scales as well as under anthropogenic global change. We illustrate how resource supply, resource uptake and RUE interactively determine ecosystem productivity. In addition, we illustrate how in the context of biodiversity and ecosystem functioning, the addition of a species will only result in more efficient resource use and consequently higher community productivity if the species’ traits related to resource uptake and RUE are positively correlated.

\n\t\t\t\t\tThis paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.\n\t\t\t\t

The two major problems in grid computing applications are, resource management and job scheduling. These problems do occur due to distributed and heterogeneous nature of the resources. This paper introduces a model in job scheduling in grid computing environments. A dynamic scheduling algorithm is proposed to maximize the resource utilization and minimize processing time of the jobs. The proposed algorithm is based on job grouping. The results show that the proposed scheduling algorithm efficiently reduces the processing time of jobs.

The Grid Computing has emerged as a thorny platform to tackle numerous large-scale issues, particularly in science and engineering domains. One of the primary issues related to the economical and effective utilization of heterogeneous resources in a Grid scheduling. It is mainly due to the dynamic nature of grid. Grid scheduling could be subtle higher cognitive process that operates at totally different levels of grids. Grid Schedulers is employed to map user’s job to resources in keeping with their necessities. There are handful programming mechanism for grid environment the realistically wear down this dynamic nature in literature. In this study, Sensible Centrality Scheduling is used to deal with the programming computationally intensive Horde of Jobs (HOJ) applications. Their common and first aim is that they create planning choices while not totally correct performance prediction information. Another purpose to notice is that this Sensible algorithm adopts redite (needless replication) jobs. Our analysis study employs variety of experiments with numerous simulation settings. The results show the efficiency and aggressiveness of our algorithms in comparison to existing ways and we proved that is sensible centrality algorithm is the best algorithm.

Assessing Risk in a computational grid environment is an essential need for a user who runs applications from a remote machine on the grid, where resource sharing is the main concern. As Grid computing is the ultimate solution believed to meet the ever-expanding computational needs of organizations, analysis of the various possible risks to evaluate and develop solutions to resolve these risks is needed. For correctly predicting the risk environment, we made a comparative analysis of various machine learning modeling methods on a dataset of risk factors. First we conducted an online survey with international experts about the various risk factors associated with grid computing. Second we assigned numerical ranges to each risk factor based on a generic grid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. The empirical results illustrate that the proposed framework is able to provide risk assessment with a good accuracy. KeywordsGrid computingRisk assessmentFeature selectionData mining

A parallel computing approach to run fast and full‐wave electromagnetic simulation of complex structures in Grid Computing environment is presented. In this study, we show how Grid Computing improves speed and/or reliability over that provided by a single computer, while typically being much more cost‐effective than single computers of comparable speed or reliability. An efficient monolithic (unique) formulation for the electromagnetic modelling of complex (multi‐scale) structures, i.e. structures that exhibit multiple metallic patterns whose sizes cover a large range of scales, is used here. This approach, named the Scale‐Changing Technique, is based on the cascade of multi‐modal Scale‐Changing Networks, each network modelling the electromagnetic coupling between two successive scale levels. These networks can be first computed separately, in an adaptive use of Grid Computing architecture nature, and then cascaded for the global electromagnetic simulation. Based on this technique, a fast computer algorithm was developed and tested in the Grid‐Computing environment. For illustration purposes, the electromagnetic analysis of multi‐scale structures, applied to phase‐shifter elements and an example of infinite passive reflectarray, was carried out. The obtained results have confirmed the effectiveness of such an approach compared with sequential computing. This approach shows very good computation performance while keeping the same accuracy. Besides, this method is very promising for optimizing circuit with multiple design parameters to handle and for the global electromagnetic simulation of multi‐scale and/or oer‐sized structures. Copyright © 2010 John Wiley & Sons, Ltd.

The requirement of semantic technology has been augmented day by day in IT related applications due to various features including interoperability. Effective development and organization of semantic knowledge are essential for maximum throughput in any application. We extend and develop two sub-domain computer resource ontologies and utilize them in a resource discovery process of a Grid computing environment to reduce the job rejection rate. Grid computing aggregates distributed computing resources to execute computationally complex jobs. The selection of resources in a Grid system involves finding and locating resources based on users' requirements. Identifying an appropriate resource selection mechanism for Grid jobs is a major concern because overall performance of a Grid depends on it and it also helps to schedule and allocate resources. We compute semantic similarity threshold values and employ both extended ontolgies in a decentralized resource discovery model of Grid Computing. The simulation is carried out using GridSim and PlanetSim to evaluate the effectiveness of a semantic resource discovery model. The results show improved success probability for complex jobs and reduce communication overheads compared to the non semantic resource discovery model.

Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population

The accelerated development in peer‐to‐peer and Grid computing has positioned them as promising next‐generation computing platforms. They enable the creation of virtual enterprises for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. This framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price of services based on supply‐and‐demand and their value to the user. They include commodity market, posted price, tender and auction models. In this paper, we discuss the use of these models for interaction between Grid components to decide resource service value, and the necessary infrastructure to realize each model. In addition to usual services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking and enforcement services. We briefly discuss existing technologies that provide some of these services and show their usage in developing the Nimrod‐G grid resource broker. Furthermore, we demonstrate the effectiveness of some of the economic models in resource trading and scheduling using the Nimrod/G resource broker, with deadline and cost constrained scheduling for two different optimization strategies, on the World‐Wide Grid testbed that has resources distributed across five continents. Copyright © 2002 John Wiley & Sons, Ltd.

High Altitude Platforms (HAPs) is the name of a new technology to provide wireless narrowband and broadband services. A unique property of HAPs is the ability of preparing emergency telecommunication facilities once the catastrophic events occur. In these situations efficient use of limited spectrum resources would be of great importance. This paper presented a fuzzy channel allocation scheme in HAPs for emergency mobile communications. The suggested scheme is based on guard channel policy. It dynamically adjusts optimum number of reserved channels according to important quality of service (QoS) parameters to make an efficient use of resources, while prioritizes on-going calls over new calls. Simulation compares the proposed fuzzy scheme with fixed guard channel scheme and complete sharing scheme. Results show that, the fuzzy scheme is able to achieve a better grade of service (GoS), while can keep handoff failures low as network traffic load changes.

The global economic impact of basketball is measured in tens of billions of dollars and requires the efficient use of resources to maximize success on and off the court. Today, coaches, players, investors, and owners need to take full advantage of modern analytical methods and digital video software capabilities to make the most efficient use of a teams resources. This research is the first in a series that makes full use of modern analytic methods and begins to define new defensive and offensive criteria to supplement the decades old game box score performance information. Data envelopment analysis and statistical methods are used to evaluate two new defensive performance metrics on defensive efficiency. The two new defensive metrics are contested and uncontested passes that are fully defined in the articles appendix. Future research will expand the sample size and allow for more comprehensive models of basketball team defensive efficiency.