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Anoop George Ninan

dblp:84/3401 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2003
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 2 first-authorSystems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Content delivery and video streaming · 94% Internet architecture and protocols · 6%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Distributed systems · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming › caching
cache consistency
0.122003
Scalable Consistency Maintenance in Content Distribution Networks Using Cooperative Leases · IEEE Trans. Knowl. Data Eng. 2003
Cooperative leases: scalable consistency maintenance in content distribution networks · WWW 2002
Content delivery and video streaming
content delivery network
0.122003
Scalable Consistency Maintenance in Content Distribution Networks Using Cooperative Leases · IEEE Trans. Knowl. Data Eng. 2003
Cooperative leases: scalable consistency maintenance in content distribution networks · WWW 2002
Distributed systems › group communication
application-level multicast
0.012003
Scalable Consistency Maintenance in Content Distribution Networks Using Cooperative Leases · IEEE Trans. Knowl. Data Eng. 2003
Distributed systems
distributed coordination
0.012003
Scalable Consistency Maintenance in Content Distribution Networks Using Cooperative Leases · IEEE Trans. Knowl. Data Eng. 2003
Internet architecture and protocols › multicast
application-layer multicast
0.012002
Cooperative leases: scalable consistency maintenance in content distribution networks · WWW 2002

Methods — techniques the papers use, named apart from their topics

application-level multicast · 0.2lease-based consistency · 0.1leases · 0.1
YearPublicationVenuePosition
2003 Scalable Consistency Maintenance in Content Distribution Networks Using Cooperative Leases
abstract
We argue that cache consistency mechanisms designed for stand-alone proxies do not scale to the large number of proxies in a content distribution network and are not flexible enough to allow consistency guarantees to be tailored to object needs. To meet the twin challenges of scalability and flexibility, we introduce the notion of cooperative consistency along with a mechanism, called cooperative leases, to achieve it. By supporting /spl Delta/-consistency semantics and by using a single lease for multiple proxies, cooperative leases allow the notion of leases to be applied in a flexible, scalable manner to CDNs. Further, the approach employs application-level multicast to propagate server notifications to proxies in a scalable manner. We implement our approach in the Apache Web server and the Squid proxy cache and demonstrate its efficacy using a detailed experimental evaluation. Our results show a factor of 2.5 reduction in server message overhead and a 20 percent reduction in server state space overhead when compared to original leases albeit at an increased interproxy communication overhead.
Anoop George Ninan, Purushottam Kulkarni, Prashant J. Shenoy, Krithi Ramamritham, Renu Tewari
IEEE Trans. Knowl. Data Eng.1
2002 Cooperative leases: scalable consistency maintenance in content distribution networks
abstract
In this paper, we argue that cache consistency mechanisms designed for stand-alone proxies do not scale to the large number of proxies in a content distribution network and are not flexible enough to allow consistency guarantees to be tailored to object needs. To meet the twin challenges of scalability and flexibility, we introduce the notion of cooperative consistency along with a mechanism, called cooperative leases, to achieve it. By supporting Δ-consistency semantics and by using a single lease for multiple proxies, cooperative leases allows the notion of leases to be applied in a flexible, scalable manner to CDNs. Further, the approach employs application-level multicast to propagate server notifications to proxies in a scalable manner. We implement our approach in the Apache web server and the Squid proxy cache and demonstrate its efficacy using a detailed experimental evaluation. Our results show a factor of 2.5 reduction in server message overhead and a 20% reduction in server state space overhead when compared to original leases albeit at an increased inter-proxy communication overhead.
Anoop George Ninan, Purushottam Kulkarni, Prashant J. Shenoy, Krithi Ramamritham, Renu Tewari
WWW1
2001 Maintaining Mutual Consistency for Cached Web Objects
abstract
Existing Web proxy caches employ cache consistency mechanisms to ensure that locally cached data is consistent with that at the server. We argue that techniques for maintaining consistency of individual objects are not sufficient; a proxy should employ additional mechanisms to ensure that related Web objects are mutually consistent with one another. We formally define the notion of mutual consistency and the semantics provided by a mutual consistency mechanism to end users. We then present techniques for maintaining mutual consistency in the temporal and value domains. A novel aspect of our techniques is that they can adapt to the variations in the rate of change of the source data, resulting in judicious use of proxy and network resources. We evaluate our approaches using real-world Web traces and show that: (i) careful tuning can result in substantial savings in the network overhead incurred without any substantial loss in fidelity, of the consistency guarantees, and (ii) the incremental cost of providing mutual consistency guarantees over mechanisms to provide individual consistency guarantees is small.
Bhuvan Urgaonkar, Anoop George Ninan, M. S. Raunak 0001, Prashant J. Shenoy, Krithi Ramamritham
ICDCS2