Multi Tenancy For Cloud Based In Memory Column Databases
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Multi Tenancy for Cloud-Based In-Memory Column Databases
Author: Jan Schaffner
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-07-03
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.
Building a Columnar Database on RAMCloud
This book examines the field of parallel database management systems and illustrates the great variety of solutions based on a shared-storage or a shared-nothing architecture. Constantly dropping memory prices and the desire to operate with low-latency responses on large sets of data paved the way for main memory-based parallel database management systems. However, this area is currently dominated by the shared-nothing approach in order to preserve the in-memory performance advantage by processing data locally on each server. The main argument this book makes is that such an unilateral development will cease due to the combination of the following three trends: a) Today’s network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory on a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic and provide high-availability. c) A modern storage system such as Stanford’s RAM Cloud even keeps all data resident in the main memory. Exploiting these characteristics in the context of a main memory-based parallel database management system is desirable. The book demonstrates that the advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible.
New Frontiers in Information and Software as Services
The increasing costs of creating and maintaining infrastructures for delivering services to consumers have led to the emergence of cloud based third party service providers renting networks, computation power, storage, and even entire software application suites. On the other hand, service customers demand competitive pricing, service level agreements, and increased flexibility and scalability. Service consumers also expect process and data security, 24/7 service availability, and compliance with privacy regulations. This book focuses on such challenges associated with the design, implementation, deployment, and management of data and software as a service. The 12 papers presented in this volume were contributed by leaders in academia and industry, and were reviewed and supervised by an expert editorial board. They describe cutting-edge approaches in areas like service design, service security, service optimization, and service migration.