Authors:
- A background chapter on column databases and multi tenancy summarizes the key concepts of these technologies in a compact manner
- A dedicated chapter on related work provides a detailed survey of the state of the art in workload management, data placement and multi tenant databases in general
- A validation of the algorithmic results is conducted using traces from a production data center running one of SAP's on-demand applications, and the particularities of such realistic data are being discussed and generalized
Part of the book series: In-Memory Data Management Research (IMDM)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
About this book
With the proliferation of Software-as-a-Service (SaaS) offerings, it is becoming increasingly important for individual SaaS providers to operate their services at a low cost. This book investigates SaaS from the perspective of the provider and shows how operational costs can be reduced by using “multi tenancy,” a technique for consolidating a large number of customers onto a small number of servers.
Specifically, the book addresses multi tenancy on the database level, focusing on in-memory column databases, which are the backbone of many important new enterprise applications. For efficiently implementing multi tenancy in a farm of databases, two fundamental challenges must be addressed, (i) workload modeling and (ii) data placement. The first involves estimating the (shared) resource consumption for multi tenancy on a single in-memory database server. The second consists in assigning tenants to servers in a way that minimizes the number of required servers (and thus costs) based on the assumed workload model. This step also entails replicating tenants for performance and high availability. This book presents novel solutions to both problems.
Authors and Affiliations
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IT Systems Engineering, Hasso Plattner Institute for, Potsdam, Germany
Jan Schaffner
About the author
Bibliographic Information
Book Title: Multi Tenancy for Cloud-Based In-Memory Column Databases
Book Subtitle: Workload Management and Data Placement
Authors: Jan Schaffner
Series Title: In-Memory Data Management Research
DOI: https://doi.org/10.1007/978-3-319-00497-6
Publisher: Springer Cham
eBook Packages: Business and Economics, Business and Management (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-00496-9Published: 16 July 2013
Softcover ISBN: 978-3-319-03344-0Published: 06 August 2015
eBook ISBN: 978-3-319-00497-6Published: 03 July 2013
Series ISSN: 2196-8055
Series E-ISSN: 2196-8063
Edition Number: 1
Number of Pages: XIII, 128
Topics: IT in Business, Database Management, Algorithm Analysis and Problem Complexity, Models and Principles, Discrete Optimization