High Availability Management for Applications Services in the Cloud Container-Based Platform

Alahmad, Yanal; Agarwal, Anjali; Daradkeh, Tariq

doi:10.1109/aiccsa.2018.8612858

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The solution requires at least two private networks to enable cluster communication and one public network to facilitate heartbeat communication. There are also research projects that explore the use of existing HACs such as Pacemaker/Corosync [170] and OpenSAF [171] to support container-based applications.…”

Section: Analysis Of the Survey Resultsmentioning

confidence: 99%

High-availability clusters: A taxonomy, survey, and future directions

Somasekaram

Călinescu

Buyya

2022

Journal of Systems and Software

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Section: Analysis Of the Survey Resultsmentioning

confidence: 99%

High-availability clusters: A taxonomy, survey, and future directions

Somasekaram

Călinescu

Buyya

2022

Journal of Systems and Software

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“…Using task redundancy (backup) is considered the traditional approach to maintain task availability. In case of failure of the main (active) task for any reason, the backup (standby) task will take over and start execution [4], [5]. Otherwise, the failed task has to be resubmitted and go back again to the waiting queue until it gets scheduled.…”

Section: Introductionmentioning

confidence: 99%

Proactive Failure-Aware Task Scheduling Framework for Cloud Computing

2021

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Cloud computing is a widely adopted platform for executing tasks of different application types that belong to the end users. In the cloud, application task is prone to failure for several reasons, such as software bug or exception, virtual or physical infrastructure failure. Cloud service providers are responsible for managing availability of scheduled computing tasks in order to provide high level QoS for their customers. Protecting task against failure is a challenging and not a trivial mission due to dynamic, heterogeneous and large distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedy (post) actions. In this work, we first study and analyze three publicly available large cluster datasets from Google, Alibaba, and Trinity, to characterize task failure in cloud computing platform. We then propose a failure-aware task scheduling framework that can predict the termination status for a set of given tasks during the runtime, and take the appropriate remedy actions. The framework uses deep learning methods named Artificial and Convolutional Neural Network, ANN and CNN, for different prediction purposes. In addition, we formalize the actions selection problem as Integer Linear Programming (ILP) model and propose a heuristic optimization solution that aims to minimize the failure probability of tasks and their resources usage. The results show ANN and CNN can achieve prediction accuracy of up to 94% and 92%, respectively using Google dataset. Moreover, the framework can protect up to 40% of tasks that are predicted as failed using Alibaba dataset by taking the appropriate remedy actions, and hence save many of cluster's resources such as CPU and RAM.INDEX TERMS Task failure prediction, deep learning, task scheduling, cloud computing.

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