Reader small image

You're reading from  Mastering Mesos

Product typeBook
Published inMay 2016
PublisherPackt
ISBN-139781785886249
Edition1st Edition
Tools
Right arrow
Authors (2):
Dipa Dubhashi
Dipa Dubhashi
author image
Dipa Dubhashi

Dipa Dubhashi is an alumnus of the prestigious Indian Institute of Technology and heads product management at Sigmoid. His prior experience includes consulting with ZS Associates besides founding his own start-up. Dipa specializes in envisioning enterprise big data products, developing their roadmaps, and managing their development to solve customer use cases across multiple industries. He advises several leading start-ups as well as Fortune 500 companies about architecting and implementing their next-generation big data solutions. Dipa has also developed a course on Apache Spark for a leading online education portal and is a regular speaker at big data meetups and conferences.
Read more about Dipa Dubhashi

Akhil Das
Akhil Das
author image
Akhil Das

Akhil Das is a senior software developer at Sigmoid primarily focusing on distributed computing, real-time analytics, performance optimization, and application scaling problems using a wide variety of technologies such as Apache Spark and Mesos, among others. He contributes actively to the Apache Spark project and is a regular speaker at big data conferences and meetups, MesosCon 2015 being the most recent one.
Read more about Akhil Das

View More author details
Right arrow

Scaling and efficiency


Mesos aims to provide a highly scalable and efficient mechanism to enable various frameworks to share cluster resources effectively. Distributed applications are varied, can have different priorities in different contexts, and are continuously evolving, a fact that led Mesos' design philosophy towards providing for customizable resource allocation policies that users can define and set as per their requirements.

Resource allocation

The Mesos resource allocation module contains the policy that the Mesos master uses to determine the type and quantity of resource offers that need to be made to each framework. Organizations can customize it to implement their own allocation policy, for example, fair sharing, priority, and so on, which allow for fine-grained resource sharing. Custom allocation modules can be developed to address specific needs.

The resource allocation module is responsible for making sure that the resources are shared in a fair manner among competing frameworks...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
Mastering Mesos
Published in: May 2016Publisher: PacktISBN-13: 9781785886249

Authors (2)

author image
Dipa Dubhashi

Dipa Dubhashi is an alumnus of the prestigious Indian Institute of Technology and heads product management at Sigmoid. His prior experience includes consulting with ZS Associates besides founding his own start-up. Dipa specializes in envisioning enterprise big data products, developing their roadmaps, and managing their development to solve customer use cases across multiple industries. He advises several leading start-ups as well as Fortune 500 companies about architecting and implementing their next-generation big data solutions. Dipa has also developed a course on Apache Spark for a leading online education portal and is a regular speaker at big data meetups and conferences.
Read more about Dipa Dubhashi

author image
Akhil Das

Akhil Das is a senior software developer at Sigmoid primarily focusing on distributed computing, real-time analytics, performance optimization, and application scaling problems using a wide variety of technologies such as Apache Spark and Mesos, among others. He contributes actively to the Apache Spark project and is a regular speaker at big data conferences and meetups, MesosCon 2015 being the most recent one.
Read more about Akhil Das