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Realization of cloud computing has been possible due to availability of virtualization technologies on commodity platforms. Measuring resource usage on the virtualized servers is difficult because of the fact that the performance counters used for resource accounting are not virtualized. Hence, many of the prevalent virtualization technologies like Xen, VMware, KVM etc., use host specific CPU usage monitoring, which is coarse grained. In this paper, we present a performance monitoring tool for KVM based virtualized machines, which measures the CPU overhead incurred by the hypervisor on behalf of the virtual machine along-with the CPU usage of virtual machine itself. This fine-grained resource usage information, provided by the above tool, can be used for diverse situations like resource provisioning to support performance associated QoS requirements, identification of bottlenecks during VM placements, resource profiling of applications in cloud environments, etc. We demonstrate a use case of this tool by measuring the performance of web-servers hosted on a KVM based virtualized server.

Urban planners, authorities, and numerous additional players have to deal with challenges related to the rapid urbanization process and its effect on human mobility and transport dynamics. Hence, optimize transportation systems represents a unique occasion for municipalities. Indeed, the quality of transport is linked to economic growth, and by decreasing traffic congestion, the life quality of the inhabitants is drastically enhanced. Most state-of-the-art solutions optimize traffic in specific and small zones of cities (e.g., single intersections) and cannot be used to gather insights for an entire city. Moreover, evaluating such optimized policies in a realistic way that is convincing for policy-makers can be extremely expensive. In our work, we propose a reinforcement learning frameworks to overtake these two limitations. In particular, we use human mobility data to optimize the transport dynamics of three real-world cities (i.e., Berlin, Santiago de Chile, Dakar) and a synthesized one (i.e., SynthTown). To this end, we transform the transportation dynamics' simulator MATSim into a realistic reinforcement learning environment able to optimize and evaluate transportation policies using agents that perform realistic daily activities and trips. In this way, we can assess transportation policies in a manner that is convincing for policy-makers. Finally, we develop a model-based reinforcement learning algorithm that approximates MATSim dynamics with a Partially Observable Discrete Event Decision Process (PODEDP) and, with respect to other state-of-art policy optimization techniques, can scale on big transportation data and find optimal policies also on a city-scale.

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Recent work has begun exploring the value of domain abstractions in Monte-Carlo Tree Search (MCTS) algorithms for probabilistic planning. These algorithms automatically aggregate symmetric search nodes (states or state-action pairs) saving valuable planning time. Existing algorithms alternate between two phases: (1) abstraction computation forcomputing node aggregations, and (2) modified MCTS that use aggregate nodes. We believe that these algorithms do not achieve the full potential of abstractions because of disjoint phases – e.g., it can take a while to recover from erroneous abstractions, or compute better abstractions based on newly found knowledge.In response, we propose On-the-Go Abstractions (OGA), a novel approach in which abstraction computation is tightlyintegrated into the MCTS algorithm. We implement these on top of UCT and name the resulting algorithm OGA-UCT.It has several desirable properties, including (1) rapid use of new information in modifying existing abstractions, (2) elimination of the expensive batch abstraction computationphase, and (3) focusing abstraction computation on important part of the sampled search space. We experimentally compare OGA-UCT against ASAP-UCT, a recent state-of-the-art MDP algorithm as well as vanilla UCT algorithm. We find that OGA-UCT is robust across a suite of planning competition and other MDP domains, and obtains up to 28 % quality improvements.