Adaptive exception handling for scientific workflows

Tolosana-Calasanz, Rafael; Bañares, José Ángel; Rana, Omer F.; Álvarez, Pedro; Ezpeleta, J.; Hoheisel, Andreas

doi:10.1002/cpe.1487

Cited by 18 publications

(14 citation statements)

References 34 publications

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“…Renew 2 [12] is a Java-based Reference net interpreter and a Reference net graphical modelling tool used in this work. Petri nets and Reference nets have been used for specifying scientific workflows [13,14]. Reference nets along with Java inscriptions have been used for implementing a service-oriented workflow engine, DVega [15].…”

Section: Reference Netsmentioning

confidence: 99%

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

Tolosana-Calasanz

Bañares

Pham

et al. 2012

Journal of Computer and System Sciences

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The ability to support Quality of Service (QoS) constraints is an important requirement in some scientific applications. With the increasing use of Cloud computing infrastructures, where access to resources is shared, dynamic and provisioned on-demand, identifying how QoS constraints can be supported becomes an important challenge. However, access to dedicated resources is often not possible in existing Cloud deployments and limited QoS guarantees are provided by many commercial providers (often restricted to error rate and availability, rather than particular QoS metrics such as latency or access time). We propose a workflow system architecture which enforces QoS for the simultaneous execution of multiple scientific workflows over a shared infrastructure (such as a Cloud environment). Our approach involves multiple pipeline workflow instances, with each instance having its own QoS requirements. These workflows are composed of a number of stages, with each stage being mapped to one or more physical resources. A stage involves a combination of data access, computation and data transfer capability. A token bucket-based data throttling framework is embedded into the workflow system architecture. Each workflow instance stage regulates the amount of data that is injected into the shared resources, allowing for bursts of data to be injected while at the same time providing isolation of workflow streams. We demonstrate our approach by using the Montage workflow, and develop a Reference net model of the workflow.

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Section: Reference Netsmentioning

confidence: 99%

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

Tolosana-Calasanz

Bañares

Pham

et al. 2012

Journal of Computer and System Sciences

Self Cite

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“…DVega is focused on integrating fault-tolerance mechanisms namely exception handling [8] and checkpointing [9] within the workflow language. By use of the dynamic features of Reference nets, DVega allows the structure of workflows to be selfmodified at runtime in case of failure, leading to enactments becoming more flexible.…”

Section: Petri Netsmentioning

confidence: 99%

Autonomic streaming pipeline for scientific workflows

Tolosana-Calasanz

Bañares

Rana

2011

Concurrency and Computation

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SUMMARYInterest in data streaming within scientific workflow has increased significantly over the recent yearsmainly due to the emergence of data-driven applications. Such applications can include data streaming from sensors and data coupling between scientific simulations. To support resource management to enact such streaming-based workflow, autonomic computing techniques for transmission have been combined with in-transit processing, so that data elements may be processed in advance, enroute, prior to arrival at the destination. We propose the integration of an autonomic data streaming service (ADSS) with in-transit processing into a workflow specification. This integration may imply that the associated runtime resource allocation is dependent on environmental conditions and can change for different enactments of the same workflow. In our proposal, our workflow specifications are independent of the constraints imposed by the resource allocation. We express our solutions in terms of Reference nets. We also implement an ADSS utilizing a timed Reference net simulation for predicting future states of the ADSS. There are two advantages: the Reference net which implements the ADSS and the timed model are coincident, and second, token distribution obtained from the Petri net implementation can be utilized to better understand the demand for particular types of resources in the system.

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“…Nevertheless, the work presented in [17] proposes an adaptive exception handling at definition level that is comparable to our programming abstractions defined for adaptiveness. The authors propose two patterns to manage the exception handling based on the Reference Nets-within-Nets formalism: propagation and replacement.…”

Section: Related Workmentioning

confidence: 99%

Chemistry-Inspired Adaptive Stream Processing

2015

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Abstract. Stream processing engines have appeared as the next generation of data processing systems, facing the needs for low-delay processing. While these systems have been widely studied recently, their ability to adapt their processing logics at run time upon the detection of some events calling for adaptation is still an open issue. Chemistry-inspired models of computation have been shown to ease the specification of adaptive systems. In this paper, we argue that a higherorder chemical model can be used to specify such an adaptive SPE in a natural way. We also show how such programming abstractions can get enacted in a decentralised environment.

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Adaptive exception handling for scientific workflows

Cited by 18 publications

References 34 publications

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

Autonomic streaming pipeline for scientific workflows

Chemistry-Inspired Adaptive Stream Processing

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