A Key-Value Store Based Data Center Resource Scheduling System

Abstract

Efficiently scheduling resources in large scale data center is a key problem that distributed resource management systems face. In the cloud computing environment, the quantity of resources expand and the size of users raises dramatically. It brought up following challenges such as accessing to vast amount of resource information, handling of highly concurrent user requests, tremendous pressure of the system brought up by the update of the mass resources and so on. Traditional resource management systems based on centralized or hierarchical structure have pool expansibility and can’t satisfy the new large scale applications. And existing distributed resource scheduling methods (such as P2P routing based and DHTs based resource scheduling methods) can’t process user requests with high concurrency rate and high-frequency resource updates well enough. We propose a resource schedule model based on Key-Value Store. The model solves the problem of storing mass resources and efficiently accessing resource information using Key-Value Store. Distributed resource scheduling method based on range-partition can locate the appropriate resources rapidly and also reduce the cost of resource updates by extended invalid push protocol. The evaluation on data from Planetlab shows that, compared to current, and to improve the scheduling efficiency.