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Proactive Autonomic Management of Cloud Resources (PANACEA)
Start date: Oct 1, 2013, End date: Mar 31, 2016 PROJECT  FINISHED 

The main objective of the project "PANACEA" is to provide Proactive Autonomic Management of Cloud Resources as a remedy to the exponentially growing complexity.If you look at the system resources (Internet) at the bottom of the stack, that system resource can be servers, storage, data centres, and network resources, the concept is then to build a level of virtualization of those resources so that any given event is not tied to one box necessarily or to one storage disk. Once you get that kind of leverage, you can build the set of functions that relate to autonomic self-* properties: configuring, healing, optimizing and protecting. The design that you have to have holistically has to deal with the fact that components are going to fail. The aim of a Cloud Computing platform is to support redundant, self-recovering, highly scalable programming models that allow workloads to recover from many inevitable hardware/software failures and monitoring resource use in real time for providing physical and virtual servers, on which the applications can run.It will propose innovative solutions for autonomic management of cloud resources, which will be based on a set of advanced Machine Learning Techniques and virtualization. A Machine Learning (ML) framework will be created for a proactive autonomic management of cloud resources. It will allow predicting the failure time of software, or user applications running on Virtual Machines (VM) and the violation of expected response time of services.To deal with the vast number of possible resources to monitor, our main approach will consider the use of mobile agents, which will move on the cloud, interacting with other agents, reading computing and network sensors, and making autonomous decisions on what to measure, when to report and to whom. Distributed Machine Learning, based on Reinforcement Learning and Neural Networks, will be used to enforce "self-organizing paths".
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