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Optimizing Peer Selection among Internet Service Providers (ISPs)

Connections among Internet Service Providers (ISPs) form the backbone of the Internet. This enables communications across the globe. ISPs are represented as Autonomous Systems (ASes) in the global Internet and inter-ISP traffic exchange takes place via inter-AS links, which are formed based on inter-ISP connections and agreements. In addition to customer-provider agreements, a crucial type of inter-ISP agreement is peering. ISP administrators use various platforms like AP-NIC and NANOG networking events for establishing new peering connections in accordance with their business and technical needs. Such methods are often inefficient and slow, potentially resulting in missed opportunities or sub-optimal routes. The process can take several months with excessive amounts of paperwork. We investigate developing tools and algorithms that can help make the inter-AS connection formation more dynamic and reliable by helping ISPs make informed decisions, in line with their needs. We analyze the largest public datasets from CAIDA and PeeringDB to identify common trends and requirements that ISPs have in the context of peering. Using this analysis, we develop a simple yet effective peering predictor model, that identifies ISP pairs that show promising signs of forming a good peering relation. For motivating research and development in this area, we develop an Internet eXchange Point (IXP) emulator that ISP admins can use as a testbed for analyzing different peering policies within an IXP. We further extend our ideas about peering to wireless cellular network and design a working wireless peering model, and present how optimal agreements can be reached and best wireless peering partners and areas can be chosen. With the exponential increase in traffic volume and dependency on the Internet, it is crucial that the underlying network is dynamic and robust. To this end, we address issues with peering from multiple angles and develop novel models for automation and optimization.

Identiferoai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1903
Date01 January 2021
CreatorsMustafa, Shahzeb
PublisherSTARS
Source SetsUniversity of Central Florida
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceElectronic Theses and Dissertations, 2020-

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