A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Supporting Cooperative Caching in Ad Hoc Networks
IEEE Transactions on Mobile Computing
Can ISPS and P2P users cooperate for improved performance?
ACM SIGCOMM Computer Communication Review
Benefit-Based Data Caching in Ad Hoc Networks
IEEE Transactions on Mobile Computing
Ditto: a system for opportunistic caching in multi-hop wireless networks
Proceedings of the 14th ACM international conference on Mobile computing and networking
Traffic modeling and proportional partial caching for peer-to-peer systems
IEEE/ACM Transactions on Networking (TON)
Optimal node-selection algorithm for parallel download in overlay content-distribution networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Mesh-based peer-to-peer layered video streaming with taxation
Proceedings of the 20th international workshop on Network and operating systems support for digital audio and video
A Survey of BitTorrent Performance
IEEE Communications Surveys & Tutorials
Cooperative Caching in Wireless P2P Networks: Design, Implementation, and Evaluation
IEEE Transactions on Parallel and Distributed Systems
Collaborative forwarding and caching in content centric networks
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
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We propose a simple scheme for selecting objects (either for caching or picking objects that are peers in a peer-to-peer system) to maximize the long-run reward obtained by a system. Our approach does not require a detailed record of the value specific objects add to the system. Therefore, it can be implemented using less computing and memory resources as compared to machine learning and artificial intelligent algorithms. One of the main contributions we make is to show that one can still derive optimal policies. The model that we use and the policy that we present are both applicable in a variety of contexts.