Book Image

Distributed Computing with Python

Book Image

Distributed Computing with Python

Overview of this book

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
Table of Contents (15 chapters)
Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Creating a private cloud


AWS is a great option for a large number of people and companies. At the same time, however, one might realize that the cost of using cloud services from AWS or other providers sometimes adds up to unsustainable levels. Other times, company policies or even data privacy requirements might discourage or even outright forbid the use of cloud resources altogether.

In these cases, one solution could be the creation of an internal, private cloud. This private cloud would use in-house hardware to provide the infrastructure to provision and run virtual machines (a la EC2) as well as a data-storage middleware (similar to what S3 offers), together with other services such as load balancers, database servers, and so on.

There are a number of free, open source, and actively maintained software stacks that make the creation and operation of a private cloud simple (or at least simpler). OpenStack (http://www.openstack.org), CloudStack (https://cloudstack.apache.org), and Eucalyptus...