Paper Info
Title
Highly available component sharing in large-scale multi-tenant cloud systems
Abstract
A multi-tenant cloud system allows multiple users to share a common physical computing infrastructure in a cost-effective way. Component sharing is highly desired in such a shared computing infrastructure, where different tenants can leverage each other's information and expertise to fulfill their own tasks. However, it is challenging to maintain the availability of sharable component resources in a large-scale cloud infrastructure, as cloud tenants are fully autonomous and highly dynamic. In this paper, we present a novel highly available component sharing system for large-scale multi-tenant cloud systems. We describe a component availability prediction scheme to identify endangered components (i.e., components at risk of extinction) within the infrastructure. The system then performs predictive replication based on the availability prediction results to preserve those endangered components. Thus, our system can preserve the availability of all component resources with low cost. Theoretical analysis and large-scale simulation are used to quantify the accuracy of our component availability prediction, and the efficiency of predictive replication. Experimental results show that our scheme can predict endangered components with high accuracy, and achieve up to 99% availability with about 15% of the full replication cost.
Year
DOI
Venue
2010
10.1145/1851476.1851487
HPDC
Keywords
Field
DocType
component resource,large-scale multi-tenant cloud system,cloud tenant,sharable component resource,component sharing,component availability prediction scheme,endangered component,component availability prediction,availability prediction result,available component sharing system,predictive replication,cost effectiveness,high availability,cloud computing
Cloud systems,Computer science,High availability,Physical computing,Cloud computing,Distributed computing
Conference
Citations 
PageRank 
References 
3
0.41
18
Authors
3
Name
Order
Citations
PageRank
Juan Du1965.10
Xiaohui Gu21975103.57
Douglas S. Reeves31487107.38