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Multi Tenancy for Cloud-Based In-Memory Column Databases [electronic resource] : Workload Management and Data Placement / by Jan Schaffner.

By: Schaffner, Jan [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: In-Memory Data Management Research: Publisher: Heidelberg : Springer International Publishing : Imprint: Springer, 2014Description: XIII, 128 p. 37 illus., 30 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319004976.Subject(s): Economics | Computer software | Computer science | Database management | Management information systems | Economics/Management Science | Business Information Systems | Database Management | Algorithm Analysis and Problem Complexity | Models and Principles | Discrete OptimizationDDC classification: 650 Online resources: Click here to access online
Contents:
1. Introduction -- 2. Background and Motivation -- 3. A Model for Load Management and Response Time Prediction -- 4. The Robust Tenant Placement and Migration Problem -- 5. Algorithms for RTP -- 6. Experimental Evaluation -- 7. Related Work -- 8. Conclusions and Perspectives.
In: Springer eBooksSummary: 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.
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1. Introduction -- 2. Background and Motivation -- 3. A Model for Load Management and Response Time Prediction -- 4. The Robust Tenant Placement and Migration Problem -- 5. Algorithms for RTP -- 6. Experimental Evaluation -- 7. Related Work -- 8. Conclusions and Perspectives.

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.

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