Balanced Job Scheduling Based on Ant Algorithm for Grid Network

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.

Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. In this paper, a novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm -- a bioinformatics application -- . GRS aims in reducing; Make span of the job workflow and execution time of varied size jobs. The experimental results prove that proposed job scheduling and resource matching algorithms performs better when evaluated against widely used FCFS job scheduling and biggest free resource matching strategies.

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. This research work deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. We propose an improved load balancing algorithm for job scheduling in the cloud environment using load distribution table in which the current status, current package, VM Capacity and the number of cloudlets submitted to each and every virtual machine will be stored. Submit the job of the user to the datacenter broker. Data center broker will first find the suitable Vm according to the requirements of the cloudlet and will match and find the most suitable Vm according to its availability or the machine with least load in the distribution table. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. The main contributions of the research work is to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

Grid computing provides a high performance computing platform to solve larger scale applications by coordinating and sharing computational power, data storage and network resources across dynamic and geographically dispersed organizations. Scheduling onto the Grid is NP-complete, so there is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. Job scheduling is one of the key research area in grid computing. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. Motivation of this study is to encourage and help the amateur researcher in the field of grid computing, so that they can understand easily the concept of scheduling and can contribute in developing more efficient and practical scheduling algorithm. This will benefit interested researchers to carry out further work in this thrust area of research.

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.

Objective: To develop an algorithm for job scheduling with proper utilization of resources in grid environment. Methods: This research work mainly focus on the development of a novel algorithm named Improved Hybrid Scheduling Algorithm for Grid (IHSAG) for job scheduling using a hybrid approach that can work effectively in the grid as well as cloud scenarios. In the proposed work of job scheduling, we have developed a unique and novel algorithm making use of hybrid as well as parallel approach of handling and processing the jobs in sequence. Results: In this research we have implement a different parameters like as reliability, efficiency, cost factor, complexity and performance for grid scheduling systems and the analysis is done based on the existing scheduling algorithms in grid infrastructure. Also, we implement a new algorithm that is improved hybrid scheduling algorithm which uses a parallel approaches for scheduling in grid computing. Conclusion: Using cloudsim and gridsim libraries the proposed algorithm suggests that the algorithm is more efficient, less complex, producing better performance and resource utilization of scheduling algorithms.

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.

Grid Computing is a widespread distributed computing environment that consists of heterogeneous resources and it provides huge computational power for the large scale distributed application. In this job scheduling is an essential objective to prioritize the jobs based on the job characteristics. It is a big challenge to design and implement an efficient Job Scheduling algorithm and the Job Scheduling model. This paper proposes the priority based Job Scheduling algorithm with Nonpreemptive Priority (NPRP) Job Scheduling model. The priority based job scheduling algorithm is the combination of Backfill algorithm and Earliest Deadline based algorithm. The proposed model has been integrated with Care Resource Broker (CRB) which supports virtualized grid environment. The proposed model has been integrated with CRB helps to make the intelligent scheduling decisions for prioritizing the jobs and also the proposed model minimizes the turnaround time, waiting time and response time of the jobs available in the queue and the user's quality of service has been maximized in the heterogenous and virtualized grid environment.

Grid computing is a loosely couple distributed system, and it can solve complex problem with large-scale computing and storage resources. Middleware plays important role to integrate heterogeneous computing nodes. Globus Toolkit (GT) is a popular open source middleware to build grid environment. However, a job submission has lots of complicate operations in GT especially in a large scale gird. Moreover, the information discovery component of Globus Toolkit can only provide the summarized information from Grid Head instead of each computing node. Furthermore, job scheduling is another important issue in the high performance Grid computing. An appropriate scheduling algorithm can efficiently reduce the response time, turnaround time and increase the throughput. In this paper, we develop a resource broker module for GT infrastructure, which can dynamically describe and discover the resource information of computing nodes. Moreover, we design an adaptive fuzzy logic scheduler, which utilizes the fuzzy logic control technology to select the most suitable computing node in the Grid environment. For verifying the performance of the proposed scheduling algorithm, we also implement a resource broker as well as fuzzy logic scheduler based on Globus Toolkit 4. The experimental results show our algorithm can reduce the turnaround time compared with round-robin and random dispatching methods. The experiments also show that our algorithm has better speed-up ratio than round-robin and random dispatching when number of computing nodes increasing.

Grid computing can be defined as the agreement between the virtual organization to share the resources viz., data, processor, peripheral devices, network, storage, web services to achieve the high performance. The resources are dynamic and heterogeneous in nature. Hence proper mechanisms are needed to discover those resources. Various studies were available on resource discovery as centralized, distributed, hierarchical, etc. Among these, we propose an approach which uses the ACO (Ant Colony Optimization) and routing information to discover the resources in the grid environment. The LRU (Least Recently Used) is employed here, anticipating the balance in resource utilization. This paper defines the grid computing and resources and makes a literature study over the available algorithms. Finally, we propose an algorithm which is a hybrid approach with ACO and routing information with LRU policy.

Cloud Computing provides a Computing environment where different resources, infrastructures, development platforms and software are delivered as a service to customers virtually on pay per time basis. Low cost, scalability, reliability, utility-based computing are important aspects of cloud computing. Job scheduling is an essential and most important part in any cloud environment. With increasing number of users, Job scheduling becomes a strenuous task. Ordering the jobs by scheduler while maintaining the balance between quality of services (QoS), efficiency and fairness of jobs is quite challenging. Scheduling algorithms are implemented considering parameters such as throughput, resource utilization, latency, cost, priority, computational time, physical distances, performance, bandwidth, resource availability. Though there are different scheduling algorithms available in cloud computing, a very less comparative study has been done on performance of various scheduling algorithms with respect to above mentioned parameters. This paper aims at a comparative study of various types of job scheduling algorithms that provide efficient cloud services.

Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimize the overall execution time. The main issue is how jobs are partitioned in which total finishing time and waiting time is minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. There are many variations of this problem, most of which are NP-hard problem, so we must rely on heuristics to solve the problem instances. Many heuristic-based approaches have been applied to finding schedules that minimize the execution time of computing tasks on parallel processors. Particle swarm optimization (PSO) is currently employed in several optimization and search problems due to its ease and ability to find solutions successfully. A variant of PSO, called as improved particle swarm optimization (ImPSO) has been developed in this paper and is hybridized with the ant colony optimization (ACO) to achieve better solutions. The proposed hybrid algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. In addition hybrid algorithm using improved particle swarm optimization (ImPSO) with artificial immune system (AIS) is also implemented for the same set of problems to compare with the proposed hybrid algorithm (ImPSO with ACO). It was observed that the proposed hybrid approach (Improved PSO with ACO) gives better results in experiments and reduces finishing and waiting time simultaneously. Key words: Particle swarm optimization (PSO), improved particle swarm optimization (ImPSO), ant colony optimization (ACO), job scheduling, finishing time, waiting time.

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.

A variety of clusters are grouped together to form a multi-cluster environment which can tackle the computational needs of a system which cannot be addressed by a single cluster. Studying multi-cluster frameworks is turning challenging day by day as it requires contemporary tools to move alongside with rapidly development and enhanced complexity of one system. Job scheduling in considered as NP hard problem in parallel and distributed computing environments such as cluster, grid and clouds. The way jobs are scheduled by the scheduler is dependent on various factors like number of jobs, processor availability, arrival time etc. Metaheuristics techniques like Genetic Algorithms, Ant Colony Optimization, Artificial Bee Colony, Cuckoo Search, Firefly Algorithm, Bat Algorithm etc. are used by researchers to get near optimal solutions to job scheduling problems. This work addresses a scheduling problem with multiple objectives. The makespan and flowtime are minimized simultaneously solving the issue of optimal job allocation. This work also includes the detailed description of parallel computing and various types scheduling as well as scheduling environments. The performance of the multi-cluster environment is optimized by applying a novel meta-heuristic technique named Bacterial Foraging Optimization Algorithm. This algorithm has better convergence and is not affected by the size of problem. The proposed algorithm was evaluated for different job sets on 3 types of processor configurations. And the final values were compared to those of the existing algorithm. The results show that the proposed algorithm has performed better than the existing one and it can be concluded that the proposed algorithm is feasible and effective for optimal allocation of jobs.

Abstract—Grid computing is characterized by the existence of a collection of heterogeneous geographicallydistributed resources that are connected over high speed networks.Job scheduling and resource management have been a great challenge to researchers in the area of grid computing.Very often, there are applications having alarge number of fine-grainedjobs.Sending these finegrained jobsindividually to be executed on grid resources that have high processing power reduces resource utilization and is thus uneconomical. This paper presents efficient grouping-based scheduling models that group fine-grained jobs to form coarsegrained jobs which are sent for execution on grid resources. Our groupingstrategy is based on the processing capability of resources and the processing requirements of grouped jobs.A load balancing approach is also presented to achieve efficient utilization of resources. Simulation experiments were conducted using the Gridsim toolkit. Results show that thetotal simulation time and the cost are improved by grouping.Furthermore, our load balancing approach enhances resource utilization and achieves load balancing among resources. Keywords—grid computing;job scheduling; job grouping; load balancing; resource utilization; Gridsim I.