Tuning Struggle Strategy in Genetic Algorithms for Scheduling in 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 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.

Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems. However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time. This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem. The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task. The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime. Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan. However, for flowtime, ant system and genetic algorithm perform better.

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

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.

Grid is a service aggregation of both the information and the computational resources. Scheduling becomes a challenging job in such a complex and dynamic environment as both the application and the computational resources are heterogeneous. The problem is further complicated by the fact that these resources may fail at any point of time. Thus a scheduling strategy which schedules the job based on the failure possibility of the grid constituents becomes very important for the reliable execution of the job. Genetic algorithm has evolved as an effective search tool to solve the optimization problems consisting of large search space. It is a type of evolutionary search strategies applied on the search space and is based on the principle of “survival of the fittest”. The model addresses the important issue of scheduling to provide the most reliable environment for the job execution by scheduling the job, ensuring maximum reliability to the job execution using Genetic Algorithm. The model presents a realistic picture of the grid by scheduling the job based on the reliability of the computational resources, networking resources and application (job) along with the preassigned workload on the nodes in order to provide the most reliable environment to the job. Simulation study proves the effectiveness of the model by comparing the performance of the model with another similar model.

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

Computational grid is a parallel and distributed infrastructure involving heterogeneous resources providing dependable, pervasive and consistent access of compute intensive resources from multiple organizations that are not subject to centralization at the administrative level delivering optimized QoS parameters. Job scheduling (i.e. mapping) is the core issue in computational grid. The problem of mapping jobs onto heterogeneous computational resources to optimize one or more QoS parameters has been proven to be NP-Complete. This work discusses the Static Level based Batch Scheduling Strategy (SLBSS) and other state of art batch scheduling algorithms viz. Min Min, Max Min, Sufferage and LJFR-SJFR along with their design motivation and limitations. A performance evaluation and analysis of SLBSS with its peers is done to evaluate its significance in the middleware.

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.

SummaryIn today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such asMakespan, is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem withMakespanandenergy consumptionas the main criteria. We apply thedynamic voltage and frequency scalingmodel for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.

MapReduce framework based gridlet allocation technique in computational grid

The emerging computational grid infrastructure consists of heterogeneous resource in widely distributed autonomous domains, which makes resource scheduling even more challenging. In this paper, we propose an adaptive resource scheduling algorithm for computational grid called BLBD (based on load balancing and demand). According to the computational grid job's personal resource requirements, the system load of computational grid nodes and the load balancing of computational grid system, the algorithm choose an appropriate node self adaptively for computational grid job from the candidates. It has been successfully tested in NPU campus computational grid environment. The experimental result shows that the new task scheduling algorithm can lead to significant performance gain for a variety of applications

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.