BigData Applications as SelfAdaptive Systems of Systems Virtualization technologies have enabled a new way of thinking of computing resources and cloud computing frameworks offer many payperuse solutions for renting these resources. Conventional physical servers had to be acquired, provisioned, and configured beforehand; virtual resources can be allocated on demand, and changes can be managed quickly. Deploying systems on virtualized resources allows one to allocate resources given the actual workload and KPIs of interest, but it requires that resource management be part of the system itself. Traditional application components must be augmented with probes and actuators to sense the application behavior and provision resources accordingly. Big data applications are a prominent example of these modern systems, and the paper discusses dynaSpark, that is, the work done by the authors to extend Spark standalone xe2x80x94a wellknown framework widely used for parallel processing and big data applicationsxe2x80x94 and augment it with resource management capabilities. It also introduces the key problems the integration and the particular batch applications bring in, and identifies additional aspects that are still to be taken into account and that would lead to a better solution.

Advanced Validation of the DVMS Approach to Fully Distributed VM Scheduling The holy grail for Infrastructure as a Service (IaaS) providers is to maximize the utilization of their infrastructure while ensuring the quality of service (QoS) for the virtual machines they host. Although the frameworks in charge of managing virtual machines (VM) on pools of physical ones (PM) have been significantly improved, enabling to manage large scale infrastructures composed of hundreds of PMs, most of them do not efficiently handle the aforementioned objective. The main reason is that advanced scheduling policies are subject to important and hard scalability problems, that become even worse when VM image transfers have to be considered. In this article, we provide a new validation of the Distributed VM Scheduler approach (DVMS) in a twofold manner. First, we provide a formal proof of the algorithm based on temporal logic. Second, we discuss largescale evaluations involving up to 4.7K VMs distributed over 467 nodes of the Gridu00275000 testbed. As far as we know, these experiments constitute the largest in vivo validation that has been performed so far with decentralized VM schedulers. These results show that a cooperative approach such as ours permits to fix overload problems in a reactive and scalable way.