Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Memory resource management in VMware ESX server
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
PowerNap: eliminating server idle power
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Memory buddies: exploiting page sharing for smart colocation in virtualized data centers
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Dynamic memory balancing for virtual machines
ACM SIGOPS Operating Systems Review
VMCTune: A Load Balancing Scheme for Virtual Machine Cluster Using Dynamic Resource Allocation
GCC '10 Proceedings of the 2010 Ninth International Conference on Grid and Cloud Computing
Overdriver: handling memory overload in an oversubscribed cloud
Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
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With the advent of virtualization and cloud computing, virtualized systems can be found from small companies to service providers and big data centers. All of them use this technology because of the many benefits it has to offer, such as a greener ICT, cost reduction, improved profitability, uptime, flexibility in management, maintenance, disaster recovery, provisioning and more. The main reason for all of these benefits is server consolidation which can be even further improved through dynamic resource allocation techniques. Out of the resources to be allocated, memory is one of the most difficult and requires proper planning, good predictions and proactivity. Many attempts have been made to approach this problem, but most of them are using traditional statistical mathematical methods. In this paper, the application of discrete Bayesian networks is evaluated, to offer probabilistic predictions on system utilization with focus on memory. The tool Bayllocator is built to provide proactive dynamic memory allocation based on the Bayesian predictions, for a set of virtual machines running in a single hypervisor. The results show that Bayesian networks are capable of providing good predictions for system load with proper tuning, and increase performance and consolidation of a single hypervisor. The modularity of the tool gives a great freedom for experimentation and even results to deal with the reactivity of the system can be provided. A survey of the current state-of-the-art in dynamic memory allocation for virtual machines is included in order to provide an overview.