CDMCR: multi‐level fault‐tolerant system for distributed applications in cloud

Wang, Qiang; Jiang, Changqing; Ran, Longbo; Zou, Deqing; Jin, Hai

doi:10.1002/sec.1187

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…However, focusing on checkpointing, Qiang et al . presented a multi‐level fault‐tolerant system for distributed applications in cloud named distributed application‐oriented multi‐level checkpoint/restart for cloud. The system backs up complete application states periodically as a snapshot‐based‐distributed checkpointing protocol, including file system state.…”

Section: Related Workmentioning

confidence: 99%

Failover strategy for fault tolerance in cloud computing environment

et al. 2017

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Summary Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault‐tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault‐tolerant techniques exist for building self‐autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault‐tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Failover strategy for fault tolerance in cloud computing environment

et al. 2017

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“…By capturing the global state of resources periodically, artificial intelligence applications can resume the computation from the latest snapshot, not from the beginning, while guaranteeing the service level agreement (SLA). However, capturing the global state of resources in cloud computing environments is not a trivial task since individual resources are independent units and, therefore, the snapshot protocol can only be done by passing messages due to the lack of shared memory between the nodes in the system [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments

Lim

Gil

2018

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Many artificial intelligence applications often require a huge amount of computing resources. As a result, cloud computing adoption rates are increasing in the artificial intelligence field. To support the demand for artificial intelligence applications and guarantee the service level agreement, cloud computing should provide not only computing resources but also fundamental mechanisms for efficient computing. In this regard, a snapshot protocol has been used to create a consistent snapshot of the global state in cloud computing environments. However, the existing snapshot protocols are not optimized in the context of artificial intelligence applications, where large-scale iterative computation is the norm. In this paper, we present a distributed snapshot protocol for efficient artificial intelligence computation in cloud computing environments. The proposed snapshot protocol is based on a distributed algorithm to run interconnected multiple nodes in a scalable fashion. Our snapshot protocol is able to deal with artificial intelligence applications, in which a large number of computing nodes are running. We reveal that our distributed snapshot protocol guarantees the correctness, safety, and liveness conditions.

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