Multi-cluster Grid Research Articles

Deadline based Job Scheduling for Multi-Cluster Grid

Application areas of multi cluster grid are increases day by day because of the seamless and scalable access to wide-area distributed resources in grid environment. Multi cluster grid allows sharing, selection and aggregates of geographically resources over heterogeneous and distributed locations. But some issues arise in multi cluster grid due to its environment. Scheduling a job on the most suitable computational resource of the grid is one of the most important issues in grid environment. To handle this issue a new approach of scheduling is proposed in this paper which is based on priority of completion deadline of the jobs because some time the job execution have an importance only when it complete under the deadline define by the user on the basis of working circumstances as a real time scenario, which helps scheduling of jobs on computational environment of multi cluster grid in very effective manner.

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application's workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG's specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.

A Hierarchical Load Balancing Policy for Grid Computing Environment

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. It provides resources for solving large scientific applications. It is typically composed of heterogeneous resources such as clusters or sites at different administrative domains connected by networks with widely varying performance characteristics. The service level of the grid software infrastructure provides two essential functions for workload and resource management. To efficiently utilize the resources at these environments, effective load balancing and resource management policies are fundamentally important. This paper addresses the problem of load balancing and task migration in grid computing environments. We propose a fully decentralized two-level load balancing policy for computationally intensive tasks on a heterogeneous multi-cluster grid environment. It resolves the single point of failure problem which many of the current policies suffer from. In this policy, any site manager receives two kinds of tasks namely, remote tasks arriving from its associated local grid manager, and local tasks submitted directly to the site manager by local users in its domain, which makes this policy closer to reality and distinguishes it from any other similar policy. It distributes the grid workload based on the resources occupation ratio and the communication cost. The grid overall mean task response time is considered as the main performance metric that need to be minimized. The simulation results show that the proposed load balancing policy improves the grid overall mean task response time.

A Load Balancing Policy for Heterogeneous Computational Grids

Computational grids have the potential computing power for solving large-scale scientific computing applications. To improve the global throughput of these applications, workload has to be evenly distributed among the available computational resources in the grid environment. This paper addresses the problem of scheduling and load balancing in heterogeneous computational grids. We proposed a two-level load balancing policy for the multi-cluster grid environment where computational resources are dispersed in different administrative domains or clusters which are located in different local area networks. The proposed load balancing policy takes into account the heterogeneity of the computational resources. It distributes the system workload based on the processing elements capacity which leads to minimize the overall job mean response time and maximize the system utilization and throughput at the steady state. An analytical model is developed to evaluate the performance of the proposed load balancing policy. The results obtained analytically are validated by simulating the model using Arena simulation package. The results show that the overall mean job response time obtained by simulation is very close to that obtained analytically. Also, the simulation results show that the performance of the proposed load balancing policy outperforms that of the random and uniform distribution load balancing policies in terms of mean job response time. The improvement ratio increases as the system workload increases and the maximum improvement ratio obtained is about 72% in the range of system parameter values examined. Keywords-grid computing; resource management; load balancing; performance evaluation; queuing theory; simulation models.

An adaptive fault tolerance approach to enhance the execution of applications on multi-cluster grid configurations from mobile grid interfaces in wireless networks

Mobile grid environments can be broadly divided into two different types of configurations. In the first case, devices are used as grid interfaces. In the second configuration, mobile devices are considered to be grid resources. However, the interaction between mobile devices and wired networks still has to overcome several difficulties before it can produce an operational mobile grid infrastructure, in either type of configuration. Examples of these challenges include the submission of distributed applications to execute tasks in a coordinated manner, frequent disconnections, restrictions on power consumption and the selection of suitable grid resources for application executions. In this paper, a research study is conducted involving an adaptive fault tolerance approach for grid interfaces in wireless networks to enhance the submission and execution of distributed applications. The approach examines submission, monitoring and management functions through the use of fault tolerance and workflow mechanisms, especially when disconnections occur and the mobile devices are executing an application. The empirical results show that the proposed approach can achieve a satisfactory level to support grid interfaces in a wireless network, and provide a more robust environment for application executions with less power.

Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids

As computational Grids are increasingly used for executing long running multi-phase parallel applications, it is important to develop efficient rescheduling frameworks that adapt application execution in response to resource and application dynamics. In this paper, three strategies or algorithms have been developed for deciding when and where to reschedule parallel applications that execute on multi-cluster Grids. The algorithms derive rescheduling plans that consist of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. Using large number of simulations, it is shown that the rescheduling plans developed by the algorithms can lead to large decrease in application execution times when compared to executions without rescheduling on dynamic Grid resources. The rescheduling plans generated by the algorithms are also shown to be competitive when compared to the near-optimal plans generated by brute-force methods. Of the algorithms, genetic algorithm yielded the most efficient rescheduling plans with 9---12% smaller average execution times than the other algorithms.

On the Benefit of Processor Coallocation in Multicluster Grid Systems

In multicluster grid systems, parallel applications may benefit from processor coallocation, that is, the simultaneous allocation of processors in multiple clusters. Although coallocation allows the allocation of more processors than available in a single cluster, it may severely increase the execution time of applications due to the relatively slow wide-area communication. The aim of this paper is to investigate the benefit of coallocation in multicluster grid systems, despite this drawback. To this end, we have conducted experiments in a real multicluster grid environment, as well as in a simulated environment, and we evaluate the performance of coallocation for various applications that range from computation-intensive to communication-intensive and for various system load settings. In addition, we compare the performance of scheduling policies that are specifically designed for coallocation. We demonstrate that considering latency in the resource selection phase improves the performance of coallocation, especially for communication-intensive parallel applications.

A comparison of utility-oriented algorithms for scheduling parallel tasks in multi-cluster grid

Scheduling parallel tasks in multi-cluster grid can be seen as two interdependent problems: cluster allocation and scheduling parallel task on the allocated cluster. In this paper both rigid and moldable parallel tasks are considered. We propose a theoretical model of utility-oriented parallel task scheduling in multi-cluster grid with advance reservations. On the basis of the model we present an approximation algorithm, a repair strategy based genetic algorithm and greedy heuristics MaxMax, T-Sufferage and R-Sufferage to solve the two interdependent problems. We compare the performance of these algorithms in aspect of utility optimality and timing results. Simulation results show on average the (1+?)-approximation algorithm achieves the best trade-off between utility optimality and timing. Genetic algorithm could achieve better utility than greedy heuristics and approximate algorithm at expensive time cost. Greedy heuristics do not perform equally well when adapted to different utility functions while the approximation algorithm shows its intrinsic stable performance.

Parallel TID-based frequent pattern mining algorithm on a PC Cluster and grid computing system

The mining of frequent patterns from transaction-oriented databases is an important subject. Frequent patterns are fundamental in generating association rules, time series, etc. Most frequent pattern mining algorithms can be classified into two categories: generate-and-test approach (Apriori-like) and pattern growth approach (FP-tree). In recent years, many techniques have been proposed for frequent pattern mining based on the FP-tree approach since it only needs two database scans. However, for pattern growth methods, the execution time increases rapidly when the database size increases or when the given support is small. Therefore, parallel-distributed computing is a good strategy for solving this problem. Some parallel algorithms have been proposed, but the execution time is still costly when the database size is large. In this paper, two parallel mining algorithms are proposed; Tidset-based Parallel FP-tree ( TPFP-tree) and Balanced Tidset-based Parallel FP-tree ( BTP-tree) for frequent pattern mining on PC Clusters and multi-cluster grids. In order to exchange transactions efficiently, a transaction identification set ( Tidset) was used to directly select transactions instead of scanning the database. Since a Grid system is a heterogeneous computing environment, the proposed BTP-tree can balance the loading according to the computing ability of the processors. BTP-tree, TPFP-tree and PFP-tree were implemented, and datasets generated with an IBM Quest Synthetic Data Generator were used to verify the performance of TPFP-tree and BTP-tree. The experimental results showed that the TPFP-tree needed less execution time on a PC Cluster than the PFP-tree when the database increased. Moreover, the BTP-tree shortened the execution time significantly and had a better load balance capability than both the TPFP-tree and PFP-tree on a multi-cluster grid.

Data load balance model for multi-cluster grid

To improve the utilization efficiency of heterogeneous resources in Multi-Cluster Grid (MCG) composed of many computer clusters, a data load balance model was proposed. According to computation power, storage power, and communication power of query node, the performance model of query node was studied. By using data saturation, matrix of data load balance and data migration technology, system's data load balance mechanism was described. Experiment results show:The model can be fit for querying massive data.

Experimental Study of Distributed SWAP-GA Models on the Grid

Recently, the phenomenon which quantifies the agricultural and water management practices from remote sensing (RS) imagery, has been adapted to help the policy makers and farm/water managers to make better operational decisions. SWAP-GA is a combined model of the SWAP (Soil Water Atmosphere and Plant) crop model and the Remote Sensing (RS) data assimilation technique, which is optimized by Genetic Algorithm (GA). However, to run the SWAP-GA model on a single PC requires a massive amount of processing time. Based on the above observation, distributed or parallel computing can be a preeminent and convincing solution. At present, Multi-cluster Grids have emerged as the most popular type of distributed computing system. However, the performances of different parallelization methodologies on the Grid have not been discussed thoroughly. This paper presents the implementation of the SWAP-GA application and discusses its impact on the performance of parallelization methods.