國立虎尾科技大學 |

Diagnosing User-Visible Performance Problems in Production High-Density Wi-Fi Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Diagnosing User-Visible Performance Problems in Production High-Density Wi-Fi Networks./
作者:	Mickulicz, Nathan D.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355960532

Diagnosing User-Visible Performance Problems in Production High-Density Wi-Fi Networks.
Mickulicz, Nathan D.

Diagnosing User-Visible Performance Problems in Production High-Density Wi-Fi Networks. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2018.

Includes bibliographical references

Large-scale, high-density Wi-Fi networks use hundreds of access points to serve thousands of closely-packed users within a large physical space (hundreds of thousands of square feet or more, such as in a stadium or arena). Because of their scale, these are complex and dynamic systems comprised of several layers and multiple components within each layer, and faults may be present in any one of these components. The problems that manifest from these faults are usually not network-wide and may be localized to a certain physical areas of the network. This makes these problems challenging to detect and diagnose; in most cases, only a small number of devices tend to be impacted by any given problem. However, many such problems may occur simultaneously in different areas of the network. Adding to the complexity is the dynamic nature of such networks, where the physical positions of radios (in end-user devices), human bodies, and other objects in the space are constantly changing, thereby creating a continually-changing RF environment. Taken together, these properties make problem diagnosis in large-scale, high-density Wi-Fi networks challenging. There are many existing techniques for diagnosing problems in Wi-Fi networks. Many of these approaches rely on data from only a single perspective of the network to diagnose problems, for example, either the client, the infrastructure (access points), or external Wi-Fi sensors that passively monitor the network. In addition, many of these approaches require the invasive modification of the network's components in order to collect data, through techniques such as the installation of specialized software on clients, modifying the firmware on access points, or even physically installing specialized devices in the RF environment of the Wi-Fi network. Finally, many approaches rely on offline analysis of the collected instrumentation, in which case diagnosis cannot be done in real time (minutes or less). Many others require network connectivity for real-time diagnosis, in which case the device must be able to communicate using the Wi-Fi infrastructure (that may be experiencing a problem). As a result, many of these approaches are difficult to deploy in production networks (due to the high financial cost or maintenance effort required), and those that are deployed often fail to detect and diagnose problems that are localized to a small number of devices (10 or less) or problems that are only present for a short time (minutes or less).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355960532Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Diagnosing User-Visible Performance Problems in Production High-Density Wi-Fi Networks.
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520 $a Large-scale, high-density Wi-Fi networks use hundreds of access points to serve thousands of closely-packed users within a large physical space (hundreds of thousands of square feet or more, such as in a stadium or arena). Because of their scale, these are complex and dynamic systems comprised of several layers and multiple components within each layer, and faults may be present in any one of these components. The problems that manifest from these faults are usually not network-wide and may be localized to a certain physical areas of the network. This makes these problems challenging to detect and diagnose; in most cases, only a small number of devices tend to be impacted by any given problem. However, many such problems may occur simultaneously in different areas of the network. Adding to the complexity is the dynamic nature of such networks, where the physical positions of radios (in end-user devices), human bodies, and other objects in the space are constantly changing, thereby creating a continually-changing RF environment. Taken together, these properties make problem diagnosis in large-scale, high-density Wi-Fi networks challenging. There are many existing techniques for diagnosing problems in Wi-Fi networks. Many of these approaches rely on data from only a single perspective of the network to diagnose problems, for example, either the client, the infrastructure (access points), or external Wi-Fi sensors that passively monitor the network. In addition, many of these approaches require the invasive modification of the network's components in order to collect data, through techniques such as the installation of specialized software on clients, modifying the firmware on access points, or even physically installing specialized devices in the RF environment of the Wi-Fi network. Finally, many approaches rely on offline analysis of the collected instrumentation, in which case diagnosis cannot be done in real time (minutes or less). Many others require network connectivity for real-time diagnosis, in which case the device must be able to communicate using the Wi-Fi infrastructure (that may be experiencing a problem). As a result, many of these approaches are difficult to deploy in production networks (due to the high financial cost or maintenance effort required), and those that are deployed often fail to detect and diagnose problems that are localized to a small number of devices (10 or less) or problems that are only present for a short time (minutes or less).
520 $a This dissertation takes a unique approach that contrasts with existing approaches in three key ways. First, we combine the Wi-Fi performance data from multiple layers of the Wi-Fi network and attempt to diagnose problems at all of these layers, rather than focusing on a single layer alone, and we introduce a fault model that includes faults that can occur across all layers of the system. Second, we require no invasive modification of the Wi-Fi network or its components in order collect data and perform problem diagnosis and mitigation. Third, we present an infrastructure-free approach to problem diagnosis that relies on Bluetooth communication with other devices nearby (peers) to perform diagnosis based on multiple perspectives of the Wi-Fi network. With this approach, our diagnosis algorithm is able to collect data from multiple network perspectives without relying on Wi-Fi infrastructure, which may be slow or unavailable. Our approach begins with the construction of an instrumentation and data-collection system to obtain Wi-Fi performance metrics from both the client and infrastructure perspectives of the network. We then build upon our instrumentation to determine when user-visible problems occur. We define a user-visible problem as a Wi-Fi-network-performance problem that causes users to disengage from using the network. Once we have detected a user-visible problem, we then proceed to diagnose the root cause of the problem as one of the faults in our fault model using an approach based on decision trees. Finally, based on the diagnosed fault, we apply an automated mitigation-strategy, which forces the device to associate with a different access point that will likely provide better performance.
520 $a To validate our approach and demonstrate its real-world impact, we have conducted a number of studies to collect data in support of our approach from both a laboratory testbed and real-world production Wi-Fi networks. We used our instrumentation and data-collection system to obtain data from over 25 real-world, large-scale, high-density Wi-Fi networks located within collegiate and professional stadiums. Our diagnostic system was deployed in a real-world mobile video-streaming application used over the Wi-Fi networks in these stadiums. (Abstract shortened by ProQuest.).
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