Abstract
MapReduce is a famous model for data-intensive parallel computing in shared-nothing clusters. One of the main issues in MapReduce is the fact of depending its performance mainly on data distribution. MapReduce contains simple load balance technique based on FIFO job scheduler that serves the jobs in their submission order but unfortunately it is insufficient in real world cases as it missed many factors that impact the performance such as heterogeneity factor and data skewness, so Load balancing is important to make all resources utilized evenly and more efficiently. There are two main schemes in load balancing aStatic Load Balancing Schemes bDynamic load balancing. The main aim of this work is to study and compare existing Load Balance algorithms also to illustrate the features of Load Balance algorithms Keyword Static Load Balance, Map reduce,Dynamic Load Balance,static load balance,comparative study
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
