Towards Autonomic Fault Recovery in System-S

Jacques-Silva, Gabriela; Challenger, Jim; Degenaro, Lou; Giles, James R.; Wagle, Rohit

doi:10.1109/icac.2007.40

Cited by 25 publications

(17 citation statements)

References 13 publications

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“…[17], [19] are in the context of System S [2], a stream processing system developed at IBM Research. [17] studies how to provide high availability for the system component of JMN by checkpointing related job state information. It does not study high availability for jobs, which have different requirements due to load spikes and tight coupling of subjobs across machines.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to High Availability in Stream Processing Systems

Zhang

et al. 2010

2010 IEEE 30th International Conference on Distributed Computing Systems

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Abstract-Stream processing is widely used by today's applications such as financial data analysis and disaster response. In distributed stream processing systems, machine fail-stop events are handled by either active standby or passive standby. However, existing HA schemes have not sufficiently addressed the situation when a machine becomes temporarily unavailable due to data rate spikes, intensive analysis or job sharing, which happens frequently but lasts for short time. It is not clear how well active and passive standby fare against such transient unavailability. In this paper, we first critically examine the suitability of active and passive standby against transient unavailability in a real testbed environment. We find that both approaches have advantages and drawbacks, but neither is ideal to provide fast recovery at low overhead as required to handle transient unavailability. Based on the insights gained, we propose a novel hybrid HA method that switches between active and passive standby modes depending on the occurrence of failure events. It presents a desirable tradeoff that is different from existing HA approaches: low overhead during normal conditions and fast recovery upon transient or permanent failure events. We have implemented our hybrid method and compared it with existing HA designs with comprehensive evaluation. The results show that our hybrid method can reduce two-thirds of the recovery time compared to passive standby and 80% message overhead compared to active standby, allowing applications to enjoy uninterrupted processing without paying a high premium.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to High Availability in Stream Processing Systems

Zhang

et al. 2010

2010 IEEE 30th International Conference on Distributed Computing Systems

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“…The primary control mechanism of the JMN is the Finite State Machine (FSM) engine [14]. The FSM engine itself does not define any automata.…”

Section: Job Orchestration Overviewmentioning

confidence: 99%

Self healing in System-S

et al. 2008

Self Cite

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Faults in a cluster are inevitable. The larger the cluster, the more likely the occurrence of some failure in hardware, in software, or by human error. System-S software must detect and self-repair failures while carrying out its prime directive-enabling stream processing program fragments to be distributed and connected to form complex applications. Depending on the type of failure, System-S may be able to continue with little or no disruption to potentially tens of thousands of interdependent and heterogeneous program fragments running across thousands of nodes.We extend the work we previously presented on the self healing nature of the job manager component in System-S by presenting how it can handle failures of other system components, applications and network infrastructure. We also evaluate the recoverability of the job management orchestrator component of System-S, considering crash failures with and without error propagation.

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“…Each System S site runs an instance of each of these system components, possibly as a distributed and fault-tolerant service [14]. Each site may belong to and be managed by a distinct organization; administrators who manage one site generally have no control over another site.…”

Section: System Smentioning

confidence: 99%

CLASP: Collaborating, Autonomous Stream Processing Systems

Branson

Douglis

Fawcett

et al. 2007

Lecture Notes in Computer Science

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Abstract. There are currently a number of streaming data analysis systems in research or commercial operation. These systems are generally large-scale distributed systems, but each system operates in isolation, under the control of one administrative authority. We are developing middleware that permits autonomous or semi-autonomous streaming analysis systems (called "sites") to interoperate, providing them opportunities for data access, performance improvements, and reliability far exceeding that available in a single system. Unique characteristics of our system include an architecture for the management of multiple cooperation paradigms depending on the degree of trust and dependencies among the participating sites; a multisite planner that converts user-specified declarative queries into specifications of distributed jobs; and a mechanism for automatic recovery of site failures by redispatching failed pieces of a distributed job. We evaluate our architecture via experiments on a running prototype, and the results demonstrate the advantages of multisite cooperation: collaborative jobs that share resources, even across only a few sites, can produce results 50% faster than independent execution, and jobs on failed sites can be recovered within a few seconds.

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Towards Autonomic Fault Recovery in System-S

Cited by 25 publications

References 13 publications

A Hybrid Approach to High Availability in Stream Processing Systems

A Hybrid Approach to High Availability in Stream Processing Systems

Self healing in System-S

CLASP: Collaborating, Autonomous Stream Processing Systems

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