
Open access
Date
2023-11Type
- Journal Article
Abstract
Internet service providers (ISPs) have a variety of quality attributes that determine their attractiveness for data transmission, ranging from quality-of-service metrics such as jitter to security properties such as the presence of DDoS defense systems. ISPs should optimize these attributes in line with their profit objective, i.e., maximize revenue from attracted traffic while minimizing attribute-related cost, all in the context of alternative offers by competing ISPs. However, this attribute optimization is difficult not least because many aspects of ISP competition are barely understood on a systematic level, e.g., the multi-dimensional and cost-driving nature of path quality, and the distributed decision making of ISPs on the same path. In this paper, we improve this understanding by analyzing how ISP competition affects path quality and ISP profits. To that end, we develop a game-theoretic model in which ISPs (i) affect path quality via multiple attributes that entail costs, (ii) are on paths together with other selfish ISPs, and (iii) are in competition with alternative paths when attracting traffic. The model enables an extensive theoretical analysis, surprisingly showing that competition can have both positive and negative effects on path quality and ISP profits, depending on the network topology and the cost structure of ISPs. However, a large-scale simulation, which draws on real-world data to instantiate the model, shows that the positive effects will likely prevail in practice: If the number of selectable paths towards any destination increases from 1 to 5, the prevalence of quality attributes increases by at least 50%, while 75% of ISPs improve their profit. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000637619Publication status
publishedExternal links
Journal / series
Performance evaluationVolume
Pages / Article No.
Publisher
ElsevierSubject
Internet economics; ISP competition; Quality of service; Game theory; Quality competitionOrganisational unit
03975 - Perrig, Adrian / Perrig, Adrian
Funding
182005 - ESCALATE: Efficient and Scalable Algorithms for Large Flow Detection (SNF)
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