The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment
ICEBE '09 Proceedings of the 2009 IEEE International Conference on e-Business Engineering
Web Server Farm in the Cloud: Performance Evaluation and Dynamic Architecture
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Client-side load balancer using cloud
Proceedings of the 2010 ACM Symposium on Applied Computing
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Runtime measurements in the cloud: observing, analyzing, and reducing variance
Proceedings of the VLDB Endowment
Initial Findings for Provisioning Variation in Cloud Computing
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
The Computer Journal
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Future Generation Computer Systems
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To investigate challenges of multi-tier application migration to Infrastructure-as-a-Service (IaaS) clouds we performed an experimental investigation by deploying a processor bound and input-output bound variant of the RUSLE2 erosion model to an IaaS based private cloud. Scaling the applications to achieve optimal system throughput is complex and involves much more than simply increasing the number of allotted virtual machines (VMs). While scaling the application variants a series of bottlenecks were encountered unique to an application's processing, I/O, and memory requirements, herein referred to as an application's profile. To investigate the impact of provisioning variation for hosting multi-tier applications we tested four schemes of VM deployments across the physical nodes of our cloud. Performance degradation was more pronounced when multiple I/O or CPU resource intensive application components were co-located on the same physical hardware. We investigated the virtualization overhead incurred using Kernel-based virtual machines (KVM) by deploying our application variants to both physical and virtual machines. Overhead varied based on the unique characteristics of each application's profile. We observed ~112% overhead for the input/output bound application and just ~ 10% overhead for the processor bound application. Understanding an application's profile was found to be important for optimal IaaS-based cloud migration and scaling.