Performance evaluation of Static Level based Batch Scheduling Strategy (SLBBS) for computational grid

An integrated security-aware job scheduling strategy for large-scale computational grids

Job scheduling is an important issue in computational grid. In computational grid, computing resources are connected through networks and may locate at different network or regional areas. So, the computational capabilities or network status of computing resources may be different. If we want to take advantage of computational grid, an efficient scheduling algorithm is necessary to assign jobs to the appropriate computing nodes. In this paper, we propose a genetic algorithm based evolutional algorithm (G-EA) to solve the scheduling problems in computational grid. The proposed algorithm uses the optimal searching technique of genetic algorithm and takes different computing capabilities of computing nodes and dynamic network status into consideration. In order to verify the performance of G-EA, a simulation was performed, and it was then compared with four other scheduling algorithms. The results show that the proposed G-EA outperformed all other schedulers across a range of scenarios.

Job Scheduling on Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques addressed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies. In this paper we focus on Struggle GAs and their tuning for the scheduling of independent jobs in computational grids. Our results showed that a careful hash implementation for computing the similarity of solutions was able to alleviate the computational burden of Struggle GA and perform better than standard similarity measures.

With the emerging paradigm of grid computing and the development of grid infrastructures, grid-based applications are becoming a common approach for solving many complex, large-scale problems in science and engineering. In order to benefit from the large computing power of grid systems, efficient allocation of jobs to resources is necessary. In this work, we consider the allocation problem in immediate mode, in which jobs are allocated as soon as they arrive in the system. We implemented several methods and measured four parameters of the system: makespan, flow- time, resource utilization and matching proximity. The immediate methods are especially interesting when good quality allocations are necessary in very short time. The considered methods have been tested using the most difficult benchmark in the literature for the problem. The computational results allowed us to identify which of considered methods perform better for makespan, flowtime, resource utilization and matching proximity. Also, we were able to evaluate the usefulness of such methods if we knew in advance certain grid characteristics such as degree of consistency of computing, heterogeneity of jobs and resources.

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.

Decentralized distributed systems, such as grids, clouds or networks of sensors, have been widely investigated recently. An important nature of such systems is the heterogeneity of their resources; in order to archive the availability, scalability and flexibility. As a consequence, managing the systems to meet requirements is obviously a nontrivial work. The issue is even more challenging in term of job scheduling when the task dependency within each job exists. In this paper, we address such problem of job scheduling, so called workflow-based job scheduling, in the decentralized distributed systems with heterogeneous resources. As such problem is proven to be an NP-complete problem, an efficient heuristic algorithm to address this problem is proposed. The algorithm is based on an observation that the heterogeneity of the resources can affect the execution time of the scheduling. We compare the effectiveness and efficiency of the proposed algorithm with a baseline algorithm. The result shows that our algorithm is highly effective and efficient for the scheduling problem in the decentralized distributed system with heterogeneous resources environment both in terms of the solution quality and the execution time respectively.KeywordsDecentralized algorithmHeterogeneous resourcesWorkflow-based job scheduling

In present era, technical problems are complex enough to be resolved by a single computer. Grid Computing is a promising field that deals with exploiting the idle CPU cycles of resources connected in a network. Job scheduling is a most demanding subject in computing grids. An effective scheduling strategy is required to utilize the resources in grid and to balance the overall load on them. This paper presents an enhanced job scheduling algorithm with budget constraint which extends existing algorithm to include economy factor into account. The main contribution of our work is dual: Firstly it tries to balance load of overall grid system and secondly it assigns tasks to resources with best computing capability which lies within the budget specified by each user. The performance of proposed algorithm is assessed by conducting simulations using GridSim. Observations prove that this algorithm achieves reduced average cost of execution for jobs to great extent so that it lies in the user's stated budget.

In 1998, Ian Foster and Carl Kesselman, together with thirty distinguished experts in high-performance computing and networking, laid the foundations of a new computing model called GRID. Their vision was introduced in the book, The GRID: Blueprint for a New Computing Infrastructure (see review, SCPE Vol. 3, No. 3 ). The editors wrote: … A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities… allowing new classes of applications to emerge… as yet, we have only a preliminary understanding of what these new applications will look like. Over the next five years, Grid computing became the hottest R&D field. Academia and national labs built experimental infrastructures, new startups were established to develop Grid technologies, and the HPC giant vendors joined the party. Each one pushed in a different direction based on what and how he believed Grid computing should look. It was only a matter of time until people started to ask what the Grid is all about. A survey of ten Grid experts produced ten different answers. The question remains without a clear answer, leaving many people confused. Ian Foster picked up the gloves. In his article, What is the GRID? A three point checklist (GRIDtoday, July 22, 2002: Vol. 1, No. 6), he redefines the Grid model to reflect the trades and changes in the field during the past five years. According to Foster, a Grid is a system that: Coordinates resources that are not subject to centralized control. Uses standard, open, general-purpose protocols and interfaces. Delivers a nontrivial quality of service. But Foster's new definition could not encompass all of the Grid's approaches. Moreover, Foster explained by example what the Grid is not: A cluster management system such as Sun's Sun Grid Engine, Platform's Load Sharing Facility, or Viridian's Portable Batch System can, when installed on a parallel computer or local area network, deliver quality of service guarantees and thus constitutes a powerful Grid resource. However, such a system is not a Grid itself, due to its centralized control of the hosts that it manages Within a week the responses started to come in. The first one was from Wolfgang Gentzsch, Director of Grid computing at Sun Microsystems (GRIDtoday, August 5, 2002: Vol. 1, No. 8). Gentzsch wrote: I very much like Ian's two earlier definitions. Combined, they may provide a very generic Grid definition. The new checklist, however, reduces the potential of a wide and rich variety of Grids, with different architectures, operational models, and applications, and may miss the chance to becoming widely accepted as a standard definition of the Grid. Why do we need a definition for the Grid? Can the Grid be defined? Is a definition is necessary to the Grid's development? The Grid is an open vision and thus cannot be defined. An attempt to define the Grid can only reach the foreseen technology horizon, and this horizon is only one to two years ahead. Grid computing is a paradigm in shift. New architectures and standards are changing the shape and direction of the Grid's development. In the past year one such prominent architecture was Open Grid Services Architecture (OGSA). The OGSA is the first attempt to bridge the gap between two cyberspace worlds: the Internet and the Grid. The Internet and the Grid are two different computing models. There are people who believe that the Grid is the next generation of the Internet and the World Wide Web, saying that the Great Global Grid is still many years away. Others state that the Grid will complement, not replace, the Internet as we know it. In 1998, when the Grid blueprint was published, a new Internet language appeared called eXtensible Markup Language (XML). XML is a development tool for a new Internet computing model known as Web Services. At that time, nobody understood that there is a relationship between Web Services and the Grid. Three years past before Steve Tuecke from Argonne National Laboratory started to write a spec for Grid Services. Later, people from IBM joined the mission and together they created OGSA. The OGSA specification outlines interfaces to grid computing software that comply with Web Services standards. If adopted, Grid services such as job scheduling, authentication, failure-detection, staging of applications and data, and migration of applications and data, will all be accessed through standard Web Services architecture. OGSA is basically where Grid meets Web Services and maybe the first step towards full integration of the Internet and the Grid. The Internet appeared a decade ago. In the beginning, the Net offered primitive applications: a mailing system, ftp services, and a web of home pages. Nobody asked, What is the Internet? The years passed and new emerging Internet technologies were developed. Today we can talk on the phone over the Net, chat with friends, and video conference with colleagues on the other side of the world. New computing and business models were developed: e-commerce, e-learning, e-science, and e-you-name-it. And nobody asks what the Internet is. The Grid is a super-model, a bag of networking models associated with many types of architectures, software packages, applications, and standards. Today, when people talk about the Grid they mention Data Grids, Science Grids, and Campus Grids. And at the same time they mention Farm computing, Peer-to-peer computing, and Utility computing, among others. Ten years from now many of today's technologies will disappear and new ones will be developed. I don't know how Grid computing will look a decade from now, but I know that we will have g-commerce, g-learning, g-science, and g-chat. Nobody will ask what the Grid is. It will be obvious, just as the Internet is today. Ami Marowka The Computer Aided Design Laboratory School of Engineering and Computer Science Hebrew University of Jerusalem

This paper provides a review of heuristics and metaheuristics methods, to solve the job scheduling problem in grid systems under the ETC (Expected Time to Compute) model. The problem is an important issue for efficient resource management in computational grids, which is performed by schedulers of these High Performance Computing systems. We present an overview of methods and a comparison of the results reported in the papers that use ETC model. The best methods are identified according to Braun et al. instances [8], which are ETC model instances most used in literature. This survey can help new researchers to lead them directly at the best scheduling algorithms already available to perform deep future works.

Artificial life techniques for load balancing in computational grids

Job scheduling is a critical mechanism which can affect significantly the performance of complex heterogeneous distributed systems, such as Grids. Since, computational Grids are multi-criteria environments by nature, modern allocation algorithms should concern more than one criterion to produce scheduling solutions. In this paper, we propose a multi-criteria Genetic Algorithm, which tries to eliminate the security risks and power consumption of the system, apart from the completion time of jobs. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, introducing the Accelerated Genetic Algorithm. In addition, Accelerated Genetic Algorithm with Overhead is also presented, which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. We propose the best combination of weights of each criterion of our Genetic Algorithm in order to maximize the performance of the system. Afterwards, Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristics. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

PurposeGrid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the diversity of available resources among different jobs in the grid. However, the major problem is the optimal job scheduling for heterogeneous resources, in which each job needs to be allocated to a proper grid's node with the appropriate resources. An important challenge is to solve optimally the scheduling problem, because the capability and availability of resources vary dynamically and the complexity of scheduling increases with the size of the grid. The purpose of this paper is to present a framework which combines the fuzzy C‐mean (FCM) clustering with an ant colony optimization (ACO) algorithm to improve the scheduling decision when the grid is heterogeneous.Design/methodology/approachIn the proposed model, the FCM algorithm classifies the jobs into appropriate classes, and the ACO algorithm maps the jobs to the appropriate resources. The ACO is characterized by ant‐like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents.FindingsThe simulation is done by using historical information on jobs in a grid. The experimental results show that the proposed algorithm can allocate jobs more efficiently and more effectively than the traditional algorithms for scheduling policies.Originality/valueThe paper provides a scheduling model based on FCM clustering and ACO algorithm for grid scheduling. The authors compared the performance of the proposed algorithm with the performance of various job‐scheduling algorithms in the grid computing environment. The comparison results show that the proposed algorithm outperforms other algorithms and gives optimal results.

This paper proposes a novel class of wavelength scheduling algorithms in optical burst switching networks. The proposed wavelength scheduling algorithms process a batch of data bursts together instead of processing them one by one. When a control burst with a reservation request arrives to a batch scheduler, the scheduler waits for a small amount of time, called the acceptance delay, before deciding to accept or reject the reservation request. After the acceptance delay has passed, the scheduler processes all the reservation requests that have arrived during the acceptance delay, then it accepts the requests that will maximize the utilization of the wavelength channels. We describe an optimal batch scheduler that serves as an upper bound on the performance of batch scheduling algorithms. Furthermore, we introduce four novel heuristic batch scheduling algorithms. The bursts of the proposed algorithms is evaluated using a discrete-event simulation model. Simulation results suggest that batch schedulers could decrease the blocking probability by 25% compared the best previously known wavelength scheduling algorithm.

Previously proposed wavelength scheduling algorithms in optical burst switching networks process each reservation request individually and in a greedy manner. In this paper we propose a new family of wavelength scheduling algorithms that process a batch of reservation requests together instead of processing them one by one. When a control burst with a reservation request arrives to a free batch scheduler, the scheduler waits for a small amount of time, called the acceptance delay, before deciding to accept or reject the reservation request. After the acceptance delay has passed, the scheduler processes all the reservation requests that have arrived during the acceptance delay, then it accepts the requests that will maximize the utilization of the wavelength channels. We describe an optimal batch scheduler that serves as an upper bound on the performance of batch scheduling algorithms. Furthermore, we introduce two heuristic batch scheduling algorithms. The performance of the proposed algorithms is evaluated using a discrete-event simulation model. Simulation results suggest that batch schedulers could decrease the blocking probability by 25% compared to the best previously known wavelength scheduling algorithm.