Browsing by Subject "Network performance"

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    Data dissemination for distributed computing.
    (2010-02) Kim, Jinoh
    Large-scale distributed systems provide an attractive scalable infrastructure for network applications. However, the loosely-coupled nature of this environment can make data access unpredictable, and in the limit, unavailable. This thesis strives to provide predictability in data access for data-intensive computing in large-scale computational infrastructures. A key requirement for achieving predictability in data access is the ability to estimate network performance for data transfer so that computation tasks can take advantage of the estimation in their deployment or data source selection. This thesis develops a framework called OPEN (Overlay Passive Estimation of Network Performance) for scalable network performance estimation. OPEN provides an estimation of end-to-end accessibility for applications by utilizing past measurements without the use of explicit probing. Unlike existing passive approaches, OPEN is not restricted to pairwise or a single network in utilizing historical information; instead, it shares measurements between nodes without any restrictions. As a result, it achieves n2 estimations by O(n) measurements. In addition, this thesis considers data dissemination in two specific environments. First, we consider a parallel data access environment in which multiple replicated servers can be utilized to download a single data file in parallel. To improve both performance and fault tolerance, we present a new parallel data retrieval algorithm and explore a broad set of resource selection heuristics. Second, we consider collective data access in applications for which group performance is more important than individual performance. In this work, we employ communication makespan as a group performance metric and propose server selection heuristics to maximize collective performance.
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    Inside The Lines: Essays on the Performance of Whole Organizational Networks
    (2019-05) Kim, Keyman
    This dissertation is focused on the study of heterogeneous network performance. For decades, most strategy and organizational research has focused on understanding how networks influence a single “node,” typically an organization or individual. In contrast, I shift my perspective to view a whole network as the unit of analysis. This approach is designed to deepen scholarly understanding of strategic outcomes and collective performance that only exist at the higher level – the whole network level. The motivation for this dissertation is the realization many of society’s most complex problems and Grand Challenges require the concerted efforts of organizations towards shared goals, which may not always coincide with local (organization level) incentives. As such, I use the context of healthcare reform in the United States to examine how analyzing the complex patterns of interorganizational patient care may help us better understand the determinants of emergent outcomes at the whole network level. Specifically, the Affordable Care Act of 2010 led to the formation of hundreds of new interorganizational networks, called Accountable Care Organizations, within the Medicare system. Using patient treatment networks based on claims data, I examine two research questions. First, what are the relationships among various network level properties and collective performance? Second, how did the formation of Accountable Care Organizations alter existing patient care patterns and outcomes, if at all? In sum, this dissertation makes theoretical contributions to the study of organizational networks, particularly with regards to network level outcomes. Moreover, this research offers insights into how network studies may inform policy and practice in healthcare.