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Picking a partner: A fair blockchain based scoring protocol for autonomous systems
KTH, Skolan för elektroteknik och datavetenskap (EECS).ORCID-id: 0000-0002-9675-9729
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2018 (Engelska)Ingår i: ANRW 2018 - Proceedings of the 2018 Applied Networking Research Workshop, Association for Computing Machinery (ACM), 2018, s. 33-39Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We tackle the problem of enabling Autonomous Systems to evaluate network providers on the basis of their adherence to Service Level Agreements (SLAs) regarding interconnection agreements. In current Internet practices, choices of interconnection partners are driven by factors such as word of mouth, personal relationships, brand recognition and market intelligence, and not by proofs of previous performance. Given that Internet eXchange Points provide increasingly more peering choices, rudimentary schemes for picking interconnection partners are not adequate anymore. Although the current interconnection ecosystem is shrouded in confidentiality, our key observation is that recently-emerged blockchain technology and advances in cryptography enable a privacy-preserving decentralized solution based on actual performance measurements. We propose the concept of SLA score to evaluate network providers and introduce a privacy-preserving protocol that allows networks to compute and verify SLA scores.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2018. s. 33-39
Nyckelord [en]
AS ranking, Blockchain, SLA score
Nationell ämneskategori
Kommunikationssystem
Identifikatorer
URN: urn:nbn:se:kth:diva-234739DOI: 10.1145/3232755.3232785ISI: 000475842700020Scopus ID: 2-s2.0-85052738885ISBN: 9781450355858 (tryckt)OAI: oai:DiVA.org:kth-234739DiVA, id: diva2:1246820
Konferens
2018 Applied Networking Research Workshop, ANRW 2018, Montreal, Canada, 16 July 2018
Anmärkning

QC 20180910

Tillgänglig från: 2018-09-10 Skapad: 2018-09-10 Senast uppdaterad: 2019-08-01Bibliografiskt granskad

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Chiesa, Marco

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Skolan för elektroteknik och datavetenskap (EECS)
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