A strongly polynomial algorithm to solve combinatorial linear programs
Operations Research
A polynomial algorithm for b-matchings: an alternative approach
Information Processing Letters
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximation algorithms
On paging with locality of reference
Journal of Computer and System Sciences
Achievable catalog size in peer-to-peer video-on-demand systems
IPTPS'08 Proceedings of the 7th international conference on Peer-to-peer systems
Tight thresholds for cuckoo hashing via XORSAT
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Orientability of random hypergraphs and the power of multiple choices
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
On fairness, optimal download performance and proportional replication in peer-to-peer networks
NETWORKING'05 Proceedings of the 4th IFIP-TC6 international conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems
Push-to-Peer Video-on-Demand System: Design and Evaluation
IEEE Journal on Selected Areas in Communications
Queueing system topologies with limited flexibility
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Hi-index | 0.00 |
This paper considers large scale distributed content service platforms, such as peer-to-peer video-on-demand systems. Such systems feature two basic resources, namely storage and bandwidth. Their efficiency critically depends on two factors: (i) content replication within servers, and (ii) how incoming service requests are matched to servers holding requested content. To inform the corresponding design choices, we make the following contributions. We first show that, for underloaded systems, so-called proportional content placement with a simple greedy strategy for matching requests to servers ensures full system efficiency provided storage size grows logarithmically with the system size. However, for constant storage size, this strategy undergoes a phase transition with severe loss of efficiency as system load approaches criticality. To better understand the role of the matching strategy in this performance degradation, we characterize the asymptotic system efficiency under an optimal matching policy. Our analysis shows that -in contrast to greedy matching- optimal matching incurs an inefficiency that is exponentially small in the server storage size, even at critical system loads. It further allows a characterization of content replication policies that minimize the inefficiency. These optimal policies, which differ markedly from proportional placement, have a simple structure which makes them implementable in practice. On the methodological side, our analysis of matching performance uses the theory of local weak limits of random graphs, and highlights a novel characterization of matching numbers in bipartite graphs, which may both be of independent interest.