Authors:
Ivan Carrera Izurieta
and
Cláudio Resin Geyer
Affiliation:
Federal University of Rio Grande do Sul - UFRGS, Brazil
Keyword(s):
Cloud Computing, Capacity Planning, Virtualization, Performance Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Databases and Information Systems Integration
;
Distributed Database Systems
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Model Driven Architectures and Engineering
;
Non-Relational Databases
;
Requirements Analysis And Management
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Cloud computing is a model that relies on virtualization and can lower costs to the user by charging only for the computational resources used by the application. There is a way to use the advantages of cloud computing in data-intensive applications like MapReduce and it is by using a virtual machine (VM) cluster in the cloud. An interesting challenge with VM clusters is determining the size of the VMs that will compose the cluster, because with an appropriate cluster and VM size, users will be able to take a full advantage of resources, i.e., reducing costs by using idle resources and gaining performance. This position paper is intended to bring to consideration the necessity for accurate capacity planning at user level, in order to take fully advantage of cloud resources and will focus specially for data-intensive applications users.