Performance and Reliability of Non-Markovian Heterogeneous Distributed Computing Systems

Pezoa, Jorge E.; Hayat, Majeed M.

doi:10.1109/tpds.2011.285

Cited by 6 publications

(6 citation statements)

References 34 publications

(92 reference statements)

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“…Although reliability in distributed computing has been studied before [175], standard fault tolerance and reliability approaches cannot be directly applied in Cloud computing systems. The scale and expected reliability of Cloud computing are increasingly important but hard to analyse due to the range of inter-related characteristics, e.g.…”

Section: Reliabilitymentioning

confidence: 99%

A Manifesto for Future Generation Cloud Computing

et al. 2018

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The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to * Corresponding

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Section: Reliabilitymentioning

confidence: 99%

A Manifesto for Future Generation Cloud Computing

et al. 2018

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“…These algorithms are all static on the assumption that job arrival and execution conform to queueing theory. Actually, they may fail under non‐Markovian environment . The work implements a dynamic scheduling for coordination.…”

Section: Related Workmentioning

confidence: 99%

“…Actually, they may fail under non-Markovian environment. 31 The work 32 implements a dynamic scheduling for coordination. But it focuses on making up for inaccurate run-time estimates to improve the response time.…”

Section: Related Workmentioning

confidence: 99%

Self‐adaptation and mutual adaptation for distributed scheduling in benevolent clouds

Liang

Tong

et al. 2016

Concurrency and Computation

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SUMMARY Joint service involving several clouds is an emerging form of cloud computing. In hybrid clouds, the schedulers within 1 cloud must not only self‐adapt to the job arrival processes and the workload but also mutually adapt to the scheduling polices of other schedulers. However, as a combinatorial optimization problem, scheduling is challenged by the adaptation to those dynamics and uncertain behaviors of the peers. This article studies the collaboration among benevolent clouds that are cooperative in nature and willing to accept jobs from other clouds. We take advantage of machine learning and propose a distributed scheduling mechanism to learn the knowledge of job model, resource performance, and others' policies. Without explicit modeling and prediction, machine learning guides scheduling decisions based on experiences. To examine the performance of our approach, we conducted simulation using the SP2 job workload log of the San Diego Supercomputer Center under a test bed based on agent‐based systems—SWARM. The results validate that our approach has much shorter mean response time than 5 typical dynamic scheduling algorithms—opportunistic load balancing, minimum execution time, minimum completion time, switching algorithm, and k‐percent best. A better collaboration in hybrid cloud is achieved by full adaptation.

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“…In [9], [28] we developed a flexible class of DTR policies. Each policy in the class estimates, at t ¼ t b , the amount of load imbalance, L ex j ðt b Þ, that each server has with respect to the estimated total system load,M i ðt b Þ.…”

Section: Correlated-failure-aware Distributed Task Reallocation Policymentioning

confidence: 99%

“…This paper has two contributions: 1) modeling the service reliability of applications executed on DCSs in the presence of correlated component failures by means of a hybrid analytical and MC-based approach, and 2) optimizing the service reliability by means of DTR policies. The service reliability is modeled by extending our analytical non-Markovian model in [9] to include specific group failures of CEs at each failure event. This extension enables us to calculate the reliability conditional on the occurrence of a specific realization of correlated CE failures.…”

mentioning

confidence: 99%

Reliability of Heterogeneous Distributed Computing Systems in the Presence of Correlated Failures

Pezoa

Hayat

2014

IEEE Trans. Parallel Distrib. Syst.

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Abstract-While the reliability of distributed-computing systems (DCSs) has been widely studied under the assumption that computing elements (CEs) fail independently, the impact of correlated failures of CEs on the reliability remains an open question. Here, the problem of modeling and assessing the impact of stochastic, correlated failures on the service reliability of applications running on DCSs is tackled. The service reliability is modeled using an integrated analytical and Monte-Carlo (MC) approach. The analytical component of the model comprises a generalization of a previously developed model for reliability of non-Markovian DCSs to a setting where specific patterns of simultaneous failures in CEs are allowed. The analytical model is complemented by a MC-based procedure to draw correlated-failure patterns using the recently reported concept of probabilistic shared risk groups (PSRGs). The reliability model is further utilized to develop and optimize a novel class of dynamic task reallocation (DTR) policies that maximize the reliability of DCSs in the presence of correlated failures. Theoretical predictions, MC simulations, and results from an emulation testbed show that the reliability can be improved when DTR policies correctly account for correlated failures. The impact of correlated failures of CEs on the reliability and the key dependence of DTR policies on the type of correlated failures are also investigated.

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Performance and Reliability of Non-Markovian Heterogeneous Distributed Computing Systems

Cited by 6 publications

References 34 publications

A Manifesto for Future Generation Cloud Computing

A Manifesto for Future Generation Cloud Computing

Self‐adaptation and mutual adaptation for distributed scheduling in benevolent clouds

Reliability of Heterogeneous Distributed Computing Systems in the Presence of Correlated Failures

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