Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scheduling and Load Balancing in Parallel and Distributed Systems
Scheduling and Load Balancing in Parallel and Distributed Systems
Proceedings of the FREENIX Track: 2001 USENIX Annual Technical Conference
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Game-Theoretic Approach for Load Balancing in Computational Grids
IEEE Transactions on Parallel and Distributed Systems
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Research on cloud storage architecture and key technologies
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Communications of the ACM
Supporting Cloud Computing with the Virtual Block Store System
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
RACS: a case for cloud storage diversity
Proceedings of the 1st ACM symposium on Cloud computing
BASIL: automated IO load balancing across storage devices
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
The Hadoop Distributed File System
MSST '10 Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)
Building a Distributed Block Storage System for Cloud Infrastructure
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
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Taking advantage of distributed storage technology and virtualization technology, cloud storage systems provide virtual machine clients customizable storage service. They can be divided into two types: distributed file system and block level storage system. There are two disadvantages in existing block level storage system: Firstly, Some of them are tightly coupled with their cloud computing environments. As a result, it's hard to extend them to support other cloud computing platforms; Secondly, The bottleneck of volume server seriously affects the performance and reliability of the whole system. In this paper we present a lightweighted block-level storage system for clouds--ORTHRUS, based on virtualization technology. We first design the architecture with multiple volume servers and its workflows, which can improve system performance and avoid the problem. Secondly, we propose a Listen-Detect-Switch mechanism for ORTHRUS to deal with contingent volume servers' failure. At last we design a strategy that dynamically balances load between multiple volume servers. We characterize machine capability and load quantity with black box model, and implement the dynamic load balance strategy which is based on genetic algorithm. Extensive experimental results show that the aggregated I/O throughputs of ORTHRUS are significantly improved (approximately two times of that in Orthrus), and both I/O throughputs and IOPS are also remarkably improved (about 1.8 and 1.2聽times, respectively) by our dynamic load balance strategy.