Fault Tolerance In Grid Computing: State of the Art and Open Issues (original) (raw)

A Survey on Task Checkpointing and Replication based Fault Tolerance in Grid Computing

A grid is a distributed computational and storage environment often composed of heterogeneous autonomously managed subsystems. As a result, varying resource availability becomes commonplace, often resulting in loss and delay of executing jobs. To ensure good grid performance, fault tolerance should be taken in to account. Commonly utilized techniques for providing fault tolerance in distributed systems are periodic job Checkpointing and replication. While very robust, both techniques can delay job execution if inappropriate Checkpointing intervals and replica numbers are chosen. This survey work provides several heuristics that dynamically adapt the above mentioned parameters based on information on grid status to provide high job throughput in the presence of failure while reducing the system overhead. This survey results on experiments are evaluated in a newly developed grid simulation environment SimGrid [2], which allows for easy modeling of dynamic system and job behavior. The workload and system parameters derived from logs that were collected from results have shown that adaptive approaches can considerably improve system performance, while the preference for one of the solutions depends on particular system characteristics, such as load, job submission patterns, and failure frequency.

Using replication and checkpointing for reliable task management in computational Grids

2010

In grid computing systems, providing fault-tolerance is required for both scientific computation and file-sharing to increase their reliability. In previous works, several mechanisms were proposed for grid or distributed computing systems. However, some of them used only space redundancy (hardware replication), and others used only time redundancy (checkpointing and rollback). For this reason, the existing mechanisms are inefficient in terms of their resource utilization on grid systems. In this paper, we present ART, which is an Adaptive, Reliable, and fault-Tolerant task management for grid computing environments. The main goal of ART is reducing the number of replications by using checkpointing and rollback scheme for each replication. In ART, the minimum number of replications is adaptively selected based on analysis of probability of successful execution within the given deadline and reliability requirement of each task. Our simulation results show that ART can significantly reduce the number of replications and improve scalability compared with existing mechanisms.

Adaptive Task Checkpointing and Replication: Toward Efficient Fault-Tolerant Grids

IEEE Transactions on Parallel and Distributed Systems, 2009

A grid is a distributed computational and storage environment often composed of heterogeneous autonomously managed subsystems. As a result, varying resource availability becomes commonplace, often resulting in loss and delay of executing jobs. To ensure good grid performance, fault tolerance should be taken into account. Commonly utilized techniques for providing fault tolerance in distributed systems are periodic job checkpointing and

Performance evaluation of fault tolerance techniques in grid computing system

Computers & Electrical Engineering, 2010

As fault tolerance is the ability of a system to perform its function correctly even in the presence of faults. Therefore, different fault tolerance techniques (FTTs) are critical for improving the efficient utilization of expensive resources in high performance grid computing systems, and an important component of grid workflow management system. This paper presents a performance evaluation of most commonly used FTTs in grid computing system. In this study, we considered different system centric parameters, such as throughput, turnaround time, waiting time and network delay for the evaluation of these FTTs. For comprehensive evaluation we setup various conditions in which we vary the average percentage of faults in a system, along with different workloads in order to find out the behavior of FTTs under these conditions. The empirical evaluation shows that the workflow level alternative task techniques have performance priority on task level checkpointing techniques. This comparative study will help to grid computing researchers in order to understand the behavior and performance of different FTTs in detail.

Fault Tolerance in Grids Using Job Replication

International Journal of Computing, 2014

As grids consist of a large number of resources, fault tolerance forms an important aspect of the scheduling process. In this paper, we address the problem of scheduling user jobs in grids so that failures can be avoided in the presence of resources faults. We employ job replication as an effective mechanism to achieve efficient and fault-tolerant scheduling system. Most of the existing replication-based algorithms use a fixed number of replications for each job which consumes more grid resources. We first propose an algorithm to determine adaptively the number of job replicas according to the grid failure history. Then we propose an algorithm to schedule these replicas. The proposed algorithms have been evaluated through simulation and have shown better performance in terms of grid load, throughput and failure tendency.

A hybrid fault tolerance technique in grid computing system

The Journal of Supercomputing, 2011

In order to achieve high level of reliability and availability, the grid infrastructure should be a foolproof fault tolerant. Fault tolerance plays a key role in order to assert availability and reliability of a grid system. Since the failure of resources affects job execution fatally, fault tolerance service is essential to satisfy QoS requirement in grid computing.

Fault Tolerant Scheduling Strategy for Computational Grid Environment

Computational grids have the potential for solving large-scale scientific applications using heterogeneous and geographically distributed resources. In addition to the challenges of managing and scheduling these applications, reliability challenges arise because of the unreliable nature of grid infrastructure. Two major problems that are critical to the effective utilization of computational resources are efficient scheduling of jobs and providing fault tolerance in a reliable manner. This paper addresses these problems by combining the checkpoint replication based fault tolerance echanism with Minimum Total Time to Release (MTTR) job scheduling algorithm. TTR includes the service time of the job, waiting time in the queue, transfer of input and output data to and from the resource. The MTTR algorithm minimizes the TTR by selecting a computational resource based on job requirements, job characteristics and hardware features of the resources. The fault tolerance mechanism used here s...

Job-Site Level Fault Tolerance for Cluster and Grid environments

2005 IEEE International Conference on Cluster Computing, 2005

In order to adopt high performance clusters and grid computing for mission critical applications, fault tolerance is a necessity. Common fault tolerance techniques in distributed systems are normally achieved with checkpoint-recovery and job replication on alternative resources, in cases of a system outage. The first approach depends on the system's MTTR while the latter approach depends on the availability of alternative sites to run replicas. There is a need for complementing these approaches by proactively handling failures at a job-site level, ensuring the system high availability with no loss of user submitted jobs. This paper discusses a novel fault tolerance technique * that enables the job-site recovery in Beowulf cluster-based grid environments, whereas existing techniques give up a failed system by seeking alternative resources. Our results suggest sizable aggregate performance improvement during an implementation of our method in Globus-enabled HA-OSCAR. The technique called "Smart Failover" provides a transparent and graceful recovery mechanism that saves job states in a local job-manager queue and transfers those states to the backup server periodically, and in critical system events. Thus whenever a failover occurs, the backup server is able to restart the jobs from their last saved state.

Fault Tolerance and Recovery of Scientific Workflows on Computational Grids

2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID), 2008

In this paper, we describe the design and implementation of two mechanisms for fault-tolerance and recovery for complex scientific workflows on computational grids. We present our algorithms for over-provisioning and migration, which are our primary strategies for fault-tolerance. We consider application performance models, resource reliability models, network latency and bandwidth and queue wait times for batch-queues on compute resources for determining the correct fault-tolerance strategy. Our goal is to balance reliability and performance in the presence of soft, real-time constraints like deadlines and expected success probabilities, and to do it in a way that is transparent to scientists. We have evaluated our strategies by developing a Fault-Tolerance and Recovery (FTR) service and deploying it as a part of the Linked Environments for Atmospheric Discovery (LEAD) production infrastructure. Results from real usage scenarios in LEAD show that the failure rate of individual steps in workflows decreases from about 30% to 5% by using our fault-tolerance strategies.