Proactive Failure-Aware Task Scheduling Framework for Cloud Computing

Abstract

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.

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

“…Also, a multiple feedback mechanism is used to obtain the optimal solution, which increases the sum rate and maximizes the network utility. Artificial neural network, CNN‐based QoS aware task scheduling is reported in Alahmad et al 22 to handle resource requests in large‐scale cloud computing environments. Initially, the resource selection problem is formulated as an integer linear program and presented as an optimization solution based on neural network models to improve the prediction accuracy.…”

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

“…It utilizes machine learning algorithms including Random Forest, Neural Network, Conditional Tree, Boost Tree, and General Learning Model to predict potential task failures in applications. Reference 151 presented a task scheduling framework that anticipates task failures during runtime by using deep learning techniques, such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), to predict termination status and implement appropriate remedial actions. Marahatta et al 152 presented an energy‐aware proactive fault‐tolerant scheduling mechanism for cloud computing based on Artificial Intelligence.…”

Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning

“…Earlier research on job failure has mostly focused on the study and characterization of failures. However, little research has been published on the prediction of job/task failure [1,8,10,[31][32][33][34]. Samak et al [35] have applied the Naive Bayes classification algorithm to the execution logs of scientific processes to predict the failure of tasks.…”