A Scalable Data Platform for Cloud Computing Systems

Liang Liu; Tian Yu Wo

doi:10.4028/www.scientific.net/AMM.577.860

Paper Titles

A Gossip-Based AOA Distributed Localization Algorithm for Wireless Sensor Networks
p.841

A New Approach for Task Allocation in Complex Network
p.847

A New Disassembly Approach for Binary Code Using Dynamic Multiple-Path Exploration and Static Disassembly
p.852

A Novel Approach for RFID Data Cleansing Based on Bayesian Inference
p.856

A Scalable Data Platform for Cloud Computing Systems
p.860

A WCET Test Method Based on Associative Process Communication
p.865

Algorithm for Wireless Sensor Network Data Fusion Based on Radial Basis Function Neural Networks
p.873

An Analysis of the UAV Relay Coverage in Mobile Ad-Hoc Network
p.879

Analysis of Broadband Access Solution for Intelligent Community
p.884

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 577A Scalable Data Platform for Cloud Computing...

A Scalable Data Platform for Cloud Computing Systems

Abstract:

With cloud computing systems becoming popular, it has been a hotspot to design a scalable, highly available and cost-effective data platform. This paper proposed such a data platform using MySQL DBMS blocks. For scalability, a three-level (system, super-cluster, cluster) architecture is applied, making it scalable to thousands of applications. For availability, we use asynchronous replication across geographically dispersed super clusters to provide disaster recovery, synchronous replication within a cluster to perform failure recovery and hot standby or even process pair mechanism for controllers to enhance fault tolerance. For resource utility, we design a novel load balancing strategy by exploiting the key property that the throughput requirement of web applications is flucatuated in a time period. Experiments with NLPIR dataset indicate that the system can scale to a large number of web applications and make good use of resources provided.