Paper Info
Title
Measuring data-centre workflows complexity through process mining: the Google cluster case
Abstract
Data centres have become the backbone of large Cloud services and applications, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining techniques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces.
Year
DOI
Venue
2020
10.1007/s11227-019-02996-2
The Journal of Supercomputing
Keywords
DocType
Volume
Cloud computing, Business process management, Scheduling, Process mining, Process discovery, High performance computing
Journal
76
Issue
ISSN
Citations 
4
0920-8542
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Damián Fernández-Cerero1235.40
Angel Jesus Varela-Vaca2146.09
Alejandro Fernández-Montes3428.87
María Teresa Gómez López43214.26
José Antonio Alvárez-Bermejo500.34