Aggarwal and kent  used a Genetic algorithm based scheduler for grid environment. A Directed Acyclic Graph (DAG) represented for each job, taking into account arbitrary precedence constraints and arbitrary processing time. The result showed that scheduler minimize makespan, idle time of the available computing resources. Wang and Duan  a new genetic simulated annealing (GSA) algorithm which combines genetic algorithm with simulated annealing algorithm for gridscheduling is proposed. it could avoid trapping in a local minimum effectively and get the global optimization at last. Fidanova and Durchova  introduce a jobs scheduling algorithm for gridcomputing. The algorithm is based on Ant Colony Optimization which is a Monte Carlo method. This algorithm guarantee good load balancing of the machines. Carretero and Xhafa  present the implementation of Genetic Algorithms for jobscheduling on computational grids that optimizes the makespan and the total flow time.
One of the problems in gridcomputing is jobscheduling. It is known that the jobscheduling is NP-complete, and thus the use of heuristics is the de facto approach to deal with this practice in its difficulty. The proposed is an apply FAFSA in jobscheduling and comparison between FASFA and normal AFSA
GRACE  provides middleware services needed by the resource brokers in dynamically trading resources access costs with the resource owners. It co-exists with other middle-ware systems like Globus. The main components of the GRACE infrastructure are a Trade Manager (TS), trading protocols and Trade Server (TS). TM is the GRACE client in the Nimrod-G resource broker that uses the trading protocols to interact with trade servers and negotiate for access to resources at low cost. Trade Server is the resource owner agent that negotiates with the resource users and sells access to the resources. TS use pricing algorithms as defined by the resource owner that may be driven by the demand and supply. It also interacts with the accounting system for recording resource usage. It has an extensible application-oriented scheduling policy and scheduler uses theoretical and history based predictive techniques for state estimation. Scheduler organization is decentralized and the namespace is hierarchical.
Genetic algorithmic rule primarily based meta-heuristics follow the Darwin’s natural selection law i.e. only the fittest will survive. GA a population-based meta-heuristic, was created by John Netherlands and produces consequent generation with the techniques inspired by evolutionary biology, like inheritance, mutation, crossover, and selection. GA considers associate degrees as an organism; therefore better the standard of the answer higher is the survival chance, through crossover (also called recombination) and mutation. GA will escape from the local best to search for the global best. In this paper, we have a tendency to propose a genetic algorithmic rule for job planning to address the heterogeneousness of security mechanism in a procedure grid.
The primary site then sends the job replica to its designated backup site. If any kind of failure occurs, a notice message is sent to all neighboring sites regarding the failure. The notice message also contains the identity of the backup sites. If the primary successfullycompletes a job, next it sends a discharge message to its backup to tell that it can now be reserved by other primary sites. Race condition is another problem which needs to be resolved and our algorithm introduces a timer based reservation scheme to solve this problem. This scheme ensures that a backup which is already assigned will not accept any further backup requests from other sites even in the cases of failure until a unless it is released by its primary or until the time limit expires or until its primary site successfully completes the job assigned to it.
Abstract — The purpose of gridcomputing is to produce a virtual supercomputer by using free resources available through widespread networks such as the Internet. This resource distribution, changes in resource availability, and an unreliable communication infrastructure pose a major challenge for efficient resource allocation. Because of the geographical spread of resources and their distributed management, gridscheduling is considered to be a NP-complete problem. It has been shown that evolutionary algorithms offer good performance for gridscheduling. This article uses a new evaluation (distributed) algorithm inspired by the effect of leaders in social groups, the group leaders' optimization algorithm (GLOA), to solve the problem of scheduling independent tasks in a gridcomputing system. Simulation results comparing GLOA with several other evaluation algorithms show that GLOA produces shorter makespans.
Local resource manager (LRM) which is a component of cluster (Cluster Head) monitors the status of grid resources such as TCP/IP performance, available CPU percentage, and available memory and reports them back to the Grid Information Server. The Resource Broker splits the jobs into tasks and allocates the task to the most suitable resource with the help of resource information’s available in grid information server. Once a job is completed, the time taken for executing each job will be obtained and stored in GIS.
Some different evolutionary approaches are presented for the issue of the gridscheduling. In  authors have used an evolutionary and exploratory method, named General External Optimization (GEO), for the Gridscheduling, this method is formed by two steps. The first step is to assign jobs to appropriate resources and the second step is jobscheduling in local resources independently. It is used for the gridscheduling in different resources of the genetic algorithm. The genetic algorithm which is based on Darwin theory uses selection, crossover and mutation operators in furtherance. Jiang and Chen  have used the genetic algorithm in their jobs. At first, the authors use a new initialization strategy to produce initial population and then use a set of new operators in order to achieve better performance. There is a new method which is based on PSO algorithm, has been used in . In  a newfound method includes security issues to the super exploratory scheduling algorithm, has been used to schedule jobs that involves security issues. Bee Colony Optimization (BCO) algorithm is used in . The authors have used a method according the smart binary bee colony algorithm. The balanced ant colony algorithm is used in .
CONCLUSIONS AND RESEARCH OUTLOOK In this paper, various jobscheduling algorithms in GridComputing have been analyzed. The discussion on the various papers reviewed with respect to parameters such as topology, scheduling policy, simulation environment, and performance was done to get feedback on different types of jobscheduling. These facts can be adopted by researchers to develop better and robust scheduling algorithms. It was evident that the gap between jobscheduling and data replication is narrowing down as studies revealed some researchers focused their attention to developing combined replication and scheduling algorithms. As the primary aim of data replication is to create load balance amongst the various nodes in a site, it could also mean a positive direction towards developing algorithms that are capable of jobscheduling and load balancing simultaneously. Also, the fact that some jobs are typically resource-hungry, computing jobs requirement in terms of memory and storage capacity is vital and will have the tendency of making sure jobs are completed within time and at lesser costs. Thus, the study explored jobscheduling techniques in terms of topology, performance and scheduling policies that are used to schedule jobs in grid environments. The main contribution
Grid Simulator (Gridsim) is used for simulation. It is a tool which is used to simulate the GridComputing Envi- ronment. This simulation contains entities for users, resources, information service, etc. Jobs are described with information like job’s execution time, necessary machine architectures. In this simulation, the dataset for job’s configuration is created randomly. Six jobs are initially used for checking the performance of the proposed algo- rithm. Then, worked out with 25 jobs and slowly increased and checked with 175 jobs. The simulation experi- ments are done 8 times and in each experiment the scheduling algorithms FCFS, MET, LTF and proposed JSC algorithm is worked out on the specific number of jobs by using 4 to 8 matching resources. The Turn Around time is calculated in every simulation experiment for different scheduling algorithms. For implementing and evaluating proposed algorithm, random number of modules are generated for each job and they are grouped in the form of sets. Each set holds the interdependent modules. The results and performance is evaluated using the performance metrics mentioned in Section 5.1.
Abstract: When the trend in human culture advances, the problems in their science and engineering is increased to solve those problems lot of computing power is needed. Grid is a heterogeneous system that allows sharing of resources. Gridcomputing is a technology that works what super computer does. Efficient utilization of the grid environment requires large amount of computing power. Scheduling independent jobs to the resources is not an easy task. Scheduling schedules maximum number of jobs to the minimum amount of resources which is a very tedious task. Many scheduling algorithms exist to focus either on the job side or on the resource side. Schedulers either target system-centric metrics, such as utilization and throughput, or prioritize jobs based on utility metrics provided by the users. In the proposed work, to utilize the power of grid completely, both the job and resources are taken into account. Jobs are prioritized based on the common location sum that considers both user and system priority Resource management and jobscheduling are two important and difficult tasks in gridcomputing. Scheduling and execution of jobs in a dynamic environment like Grid often calls for efficient algorithms to schedule the resources required for successful execution of the jobs.
We carried out a simulation-based study. We created our own Grid simulation environment in Department of Computer Science & IT in University of Jammu .The simulated grid environment is carried out by using Turbo C. We simulate the gridcomputing environment by using, 10 nodes with different load and computing power. Each node consists of 4 processors. The specification of each processor is Intel(R) Core(TM)2 Duo CPU with different computing power. The load and frequency of each processor is used by calling the random function. Table1 and Table2 give the analysis for the minimum load of a node and maximum ACR (available CPU Resources) of a processor respectively.
The aim of this paper is to present a decentralized hybrid jobscheduling algorithm and decentralized divisible jobscheduling algorithm adapted to heterogeneous gridcomputing environment. In this paper, we compare two classes of scheduling algorithms centralized and decentralized jobscheduling algorithms. We model the grid as a group of clusters. Group of users submit jobs to the various clusters. In centralized scheduling, the scheduler of each cluster is responsible for scheduling of submitted jobs. In decentralized scheduling, jobs though submitted locally can be migrated to another cluster in order to reduce the processing time of the jobs.
Abstract: In this paper we are going to design and implement the jobscheduling algorithm for Grid environment using IPv6. We are establishing a cross regional scheduling mechanism to accomplish the jobscheduling and job management, that chooses the operating location and mode automatically through the scheduling system and users request, due to which in gridcomputing the resources of will be used more efficiently and more reasonably The idea is that Grid technology managing this large and heterogeneous environment will allow an easy access to its resources for various users, by means of allowing them to submit their jobs into the system, guaranteeing them nontrivial Quality of Service (QoS) while hiding the complexity of the system itself by providing powerful but simple interfaces for the end user of the Grid. A grid environment is of two types: Data grids and Computing grids. Load Balancing is a technique in which the workload is distributed equally across multiple computers so that resource utilization is enhanced and the response time in grid environment get reduced. Main goal of load balancing is balancing load across all the processors which improves the throughput of grid resources. A good Scheduling algorithm works as it should balance the system load and assign jobs to resources efficiently. Hierarchical Load Balanced Algorithm is used to solve the problem in grid environment. The proposed system Enhanced Hierarchical Load Balance Algorithm is designed to schedule the jobs and also to improve the overall performance of the system in terms of resource utilization and user satisfaction. We will be using First Come First Serve(FCFS) approach so as to achieve the most efficient and optimized solution for our problem definition.
Gridcomputing is considered as a very high performance computing environment to solve complex computational problems. It comprises of jobscheduling, security, resource management, information management. The scheduling concept is needed for better management of resources. Scheduling refers to order of jobs which satisfy the metrics like satisfaction of user, completion time etc. Better scheduling policy in gridcomputing helps in improving performance, computational cost, load balancing and increases reliability and availability of resources. This paper end up with brief description of existing scheduling algorithms by considering different metrics used to prove importance of existing techniques.
Gridcomputing is an extension of distributed computing where every computer on the network shares all its resources turning the network more powerful. A network can be hardwired or wireless (over the internet). System can also be homogenous or heterogeneous. If two gridcomputing systems do not follow same set of protocols then they may not be compatible with each other. Many organizations are working together in the same direction to create standard protocols that make it easier to set up grid environments. In gridcomputing, at least one computer acts as a server which allocate the resources to all jobs that are ready to execute. The emergence of grids is due to the needs of large-scale computing infrastructures for solving major computing and data-intensive problems in the fields of science, engineering, industry and business. A gridjob is typically submitted for execution by an appropriate node on the grid. It may perform a calculation, execute one or more system commands, operate machinery or move or collect data. Sometimes the job has one of many other names such as “transaction", “work unit", “submission", all of which mean the same thing. In the system, users need to describe their job requirements. These include job name, required software applications, required data, execution time and resource specifications (CPU count, speed, operating system, physical and virtual memory).
Cloud Computing is an innovation technology which utilizes mainly internet along with centralized remote- servers to keep data as well as applications. The idea of cloud computing is established on a basic principal of “re-usability of Information Technology capabilities”. The difference that cloud computing brings is compared to the traditional concepts of “gridcomputing”, “utility computing”, “autonomic computing” or “distributed computing”, and broaden horizons across organizational boundaries. Through virtualization, cloud computing is capable to address with the same type of physical infra-structure with dissimilar computational requirements. In cloud computing, Jobscheduling is a time based approach in which maximum number of jobs should be executed in a given interval of time. The main problem which is often faced is managing the jobs in appropriate fashion so that they can be executed on time. Cloud network is not expensive; hence memory management also adds a
In future, research on jobscheduling can be carried out in various directions depending upon minimizing complexity of the scheduling algorithm, load balancing at local site, various load factors, tolerant, user‟s demand and price etc. Future work may involve developing a more comprehensive job grouping-based scheduling system that takes into account QoS (Quality of Service) requirements of each user job before performing the grouping method and handle more complicated scenario involving dynamic factors such as dynamically changing grid environment for e.g. network failure, hardware failure at a node etc. The above constraints and issues can be taken into account in designing a more efficient and practical scheduler, that will help the society to realize the benefit and implementation of the real gridcomputing system.
Abstract: Gridcomputing is a form of distributed computing that provides a platform for executing large-scale resource intensive applications on a number of heterogeneous computing systems across multiple administrative domains. Therefore, Grid platforms enable sharing, exchange, discovery, selection, and aggregation of distributed heterogeneous resources such as computers, databases and visualization devices. Job and resource scheduling is one of the key research area in gridcomputing. Jobscheduling is used to schedule the user jobs to appropriate resources in grid environment. The goal of scheduling is that it achieves highest possible system throughput and match the application need with the available computing resources. In this paper, we will review the definition of gridcomputing, types of Grids, architecture of Gridcomputing, characteristics of Computational Grid and job grouping. Grid is a system in which machines are distributed across various organizations. It involves sharing of resources that are heterogeneous and geographically distributed to solve various complex problems and develop large scale applications. Gridcomputing is broad in its domain of application and raises research questions that span many areas of distributed computing and of computer science in general. In this paper, we will explain job grouping and resource scheduling algorithms that will benefit the researchers to carry out their further work in this area of research. Keywords: Gridcomputing, Job grouping, JobScheduling
capabilities of the available resources, and proceed with the jobscheduling and deployment activities . This grouping based jobscheduling strategy reduces communication time resulting increase in computation-communication ratio (CCR), which encourages distributing grouped jobs for processing on remote resources . But it is yet to be tested where jobs may be fine-grained but their length differs considerably from one another. And scheduling can be done when one resource or more than one resource is available at the time of scheduling. Hence, scheduling should be addressed by developing a grouping strategy suitable to both type of grid environment. The motivation of this paper is to develop an enhanced grouping based job scheduler and grid resource allocation algorithm that must be efficient and effective in reducing the total processing time of jobs. The rest of this paper is organized as follows. Section 2 analyzes related works in the field of parallel and distributed memory system and gridcomputing systems. Section 3 describes the grid system and scheduling components (broker). Section 4 presents proposed dynamic grouping- based jobscheduling model. Section 5 analyses simulation results made through various observations and section 6 gives conclusion and future work and lastly, the references.