a b s t r a c tFollowing a shift from computing as a purchasable product to computing as a deliverable service to consumers over the Internet, cloud computing has emerged as a novel paradigm with an unprecedented success in turning utility computing into a reality. Like any emerging technology, with its advent, it also brought new challenges to be addressed. This work studies network and traffic aware virtual machine (VM) placement in a special cloud computing scenario from a provider's perspective, where certain infrastructure components have a predisposition to be the endpoints of a large number of intensive flows whose other endpoints are VMs located in physical machines (PMs). In the scenarios of interest, the performance of any VM is strictly dependent on the infrastructure's ability to meet their intensive traffic demands. We first introduce and attempt to maximize the total value of a metric named "satisfaction" that reflects the performance of a VM when placed on a particular PM. The problem of finding a perfect assignment for a set of given VMs is NP-hard and there is no polynomial time algorithm that can yield optimal solutions for large problems. Therefore, we introduce several off-line heuristic-based algorithms that yield nearly optimal solutions given the communication pattern and flow demand profiles of subject VMs. With extensive simulation experiments we evaluate and compare the effectiveness of our proposed algorithms against each other and also against naïve approaches.

A B S T R A C TLocation service is an essential prerequisite for mobile wireless ad hoc networks (MANETs) in which the underlying routing protocol leverages physical location information of sender and receiver nodes. Fulfillment of this requirement is challenging partly due to the mobility and unpredictability of nodes in MANETs. Moreover, scalability and location information availability under various circumstances are also substantial factors in designing an effective location service paradigm. By and large, utilizing centralized or distributed location servers responsible for storing the location information of all, or a subset of participant mobile devices, is a method employed in a significant portion of location service schemes. However, from the fairness point of view, it is more suitable to employ a location service scheme that treats participant nodes fairly, without mandating an unlucky subset to undertake the responsibility of serving as location server(s). In this work, we propose a scalable and fully decentralized location service scheme (PETAL) in which the burden of location update and inquiry tasks is almost evenly distributed among the nodes, resulting in an improvement in resilience against individual node failures. PETAL does not require hashing which results in more complexity, it is resilient against swarm mobility pattern, it requires minimal periodic location update messages when nodes do not move, and finally it does not require too many parameter configurations on all nodes. Our simulation results reveal that PETAL performs efficiently, particularly in environments densely populated by wireless devices. http://dx.