Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: A proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks is proposed.
Abstract: To address the vast multimedia traffic volume and requirements of user Quality of Experience (QoE) in the next generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing (MEC) can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named Learning based Cooperative Caching (LECC) strategy based on MEC architecture to reduce transmission cost while improving user QoE for future mobile networks. In LECC, we exploit a Transfer Learning (TL)-based approach for estimating content popularity, and then formulate the proactive caching optimization model. As the optimization problem is NP- hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate, decrease content transmission cost in comparison with known existing caching strategies.
85 citations
IBM1
TL;DR: It is shown that a tree partitioning problem of the type described here can arise in the allocation of data in hierarchical files to physical blocks of storage and a heuristic method of partitioning a general graph based on this algorithm is suggested.
Abstract: This paper describes an algorithm for partitioning a graph that is in the form of a tree. The algorithm has a growth in computation time and storage requirements that is directly proportional to the number of nodes in the tree. Several applications of the algorithm are briefly described. In particular it is shown that the tree partitioning problem frequently arises in the allocation of computer information to blocks of storage. Also, a heuristic method of partitioning a general graph based on this algorithm is suggested.
85 citations
TL;DR: An optimal on-line algorithm is presented for the following optimization problem, which constitutes the special case of the k-track assignment problem with identical time windows, and performs as well as the optimal greedy k-coloring algorithm due to Faigle and Nawijn and, independently, to Carlisle and Lloyd for the same problem under full a priori information.
85 citations
TL;DR: An improved implementation of a simulated annealing algorithm, called ISA, for constructing CAs of strengths three through six over a binary alphabet, and the results show that the algorithm attains 104 new bounds and equals the best-known solutions for the other 23 instances consuming reasonable computational time.
85 citations
TL;DR: Matching upper and lower bounds for the competitive ratio of the on-line greedy algorithm for this problem are derived, namely, [(3n)23/2](1+o(1)), and a lower bound is derived, Ω(n12), for any other deterministic or randomized on- line algorithm.
85 citations