An adaptive memory tuning strategy with high performance for Spark
by Di Chen; Haopeng Chen; Zhipeng Jiang; Yao Zhao
International Journal of Big Data Intelligence (IJBDI), Vol. 4, No. 4, 2017

Abstract: With the rapid development of internet, people put more and more focus on data, which contains much information and are of great value. To gain better performance in data analysis, in-memory computing has been more and more popular. Spark (Zaharia et al., 2010) is a successful example of improving computing performance through in-memory computing. However, how to make full use of memory resource is still a problem for Spark. In this paper, we presented an adaptive memory tuning strategy for Spark, which enables dynamic data compression and serialisation selecting strategy to use less resource usage and obtain faster data process. We derived the strategy of selecting the optimal data compression and serialisation mathematically. It chooses proper memory tuning strategy according to resource usage and can obtain good performance in applications, which persist data frequently.

Online publication date: Tue, 03-Oct-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Big Data Intelligence (IJBDI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com