Paper Info
Title
Workload Analysis for the Scope of User Demand Prediction Model Evaluations in Cloud Environments
Abstract
Alongside the healthy development of the Cloud-based technologies across various application deployments, their associated energy consumptions incurred by the excess usage of Information and Communication Technology (ICT) resources, is one of the serious concerns demanding effective solutions with immediate effect. Effective auto scaling of the Cloud resources in accordance to the incoming user demand and thereby reducing the idle resources is one optimum solution which not only reduces the excess energy consumptions but also helps maintaining the Quality of Service (QoS). Whilst achieving such tasks, estimating the user demand in advance with reliable level of accuracy has become an integral and vital component. With this in mind, this research work is aimed at analyzing the Cloud workloads and further evaluating the performances of two widely used prediction techniques such as Markov modelling and Bayesian modelling with 7 hours of Google cluster data. An important outcome of this research work is the categorization and characterization of the Cloud workloads which will assist leading into the user demand prediction parameter modelling.
Year
DOI
Venue
2014
10.1109/UCC.2014.144
UCC '14 Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing
Keywords
Field
DocType
hidden markov models,resource management,pattern,markov processes,computational modeling,mathematical model,predictive models,prediction
Data science,Resource management,Markov process,Industrial engineering,Workload,Computer science,Markov chain,Quality of service,Information and Communications Technology,Hidden Markov model,Cloud computing
Conference
ISSN
Citations 
PageRank 
2373-6860
9
0.52
References 
Authors
14
4
Name
Order
Citations
PageRank
John Panneerselvam15415.13
Lu Liu213425.39
Nick Antonopoulos353148.72
Yuan Bo4324.24