OLIC: online information compression for scalable hosting infrastructure monitoring
Proceedings of the Nineteenth International Workshop on Quality of Service
CloudScale: elastic resource scaling for multi-tenant cloud systems
Proceedings of the 2nd ACM Symposium on Cloud Computing
Application-aware cross-layer virtual machine resource management
Proceedings of the 9th international conference on Autonomic computing
Proceedings of the 9th international conference on Autonomic computing
PMAX: tenant placement in multitenant databases for profit maximization
Proceedings of the 16th International Conference on Extending Database Technology
Automatic virtual machine clustering based on bhattacharyya distance for multi-cloud systems
Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds
Dynamic resource allocation with management objectives: implementation for an OpenStack cloud
Proceedings of the 8th International Conference on Network and Service Management
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
Automatic identification of application I/O signatures from noisy server-side traces
FAST'14 Proceedings of the 12th USENIX conference on File and Storage Technologies
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To reduce cloud system resource cost, application consolidation is a must. In this paper, we present a novel pattern driven application consolidation (PAC) system to achieve efficient resource sharing in virtualized cloud computing infrastructures. PAC employs signal processing techniques to dynamically discover significant patterns called signatures of different applications and hosts. PAC then performs dynamic application consolidation based on the extracted signatures. We have implemented a prototype of the PAC system on top of the Xen virtual machine platform and tested it on the NCSU Virtual Computing Lab. We have tested our system using RUBiS benchmarks, Hadoop data processing systems, and IBM System S stream processing system. Our experiments show that 1) PAC can efficiently discover repeating resource usage patterns in the tested applications; 2) Signatures can reduce resource prediction errors by 50-90% compared to traditional coarse-grained schemes; 3) PAC can improve application performance by up to 50% when running a large number of applications on a shared cluster.