A Survey on Database Performance in Virtualized Cloud Environments

Todor Ivanov, Ilia Petrov and Alejandro Buchmann
Additional contact information
Todor Ivanov: Technische Universität Darmstadt, Germany
Ilia Petrov: Technische Universität Darmstadt, Germany
Alejandro Buchmann: Technische Universität Darmstadt, Germany

International Journal of Data Warehousing and Mining (IJDWM), 2012, vol. 8, issue 3, 1-26

Abstract: Cloud Computing emerged as a major paradigm over the years. Major challenges it poses to computer science are related to latency, scale, and reliability issues. It leverages strong economical aspects and provides sound answers to questions like energy consumption, high availability, elasticity, or efficient computing resource utilization. Many Cloud Computing platform and solution providers resort to virtualization as key underlying technology. Properties like isolation, multi-virtual machine parallelism, load balancing, efficient resource utilization, and dynamic pre-allocation besides economic factors make it attractive. It not only legitimates the spread of several types of data stores supporting a variety of data modes, but also inherently requires different types of load: (i) analytical; (ii) Transactional/Update-intensive; and (iii) mixed real-time feed processing. The authors survey how database systems can best leverage virtualization properties in cloud scenarios. The authors show that read mostly database systems and especially column stores profit from virtualization in analytical and search scenarios. Secondly, cloud analytics virtualized database systems are efficient in transactional scenarios such as Cloud CRM virtualized database systems lag. The authors also explore how the nature of mixed cloud loads can be best reflected by virtualization properties like load balancing, migration, and high availability.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jdwm.2012070101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:8:y:2012:i:3:p:1-26

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().