Browsing by Subject "Sink nodes"

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Open Access
Network-aware virtual machine placement in cloud data centers with multiple traffic-intensive components
(Elsevier BV, 2015) Ilkhechi, A. R.; Korpeoglu, I.; Ulusoy, Özgür
Following 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.
Open Access
Rule-based in-network processing in wireless sensor networks
(IEEE, 2009-07) Şanlı, Ö.; Körpeoğlu, İbrahim; Yazıcı, A.
Wireless sensor networks are application-specific networks, and usually a new network design is required for a new application. In event-driven wireless sensor network applications, the sink node of the network is generally concerned with the higher level information describing the events happening in the network, not the raw sensor data of individual sensor nodes. As the communication is a costly operation in wireless sensor networks, it is important to process the raw data triggering the events inside the network instead of bringing the raw data to the sink and processing it there. This helps reducing the total amount of packets transmitted and total energy consumed in the network. In this paper, we propose a new method that distributes the information processing into the sensor network for event-driven applications. We also describe an application scenario, healthcare monitoring application, that can benefit from our approach. © 2009 IEEE.