By M. Enachescu, A. Goel, R. Govindan, R. Motwani (auth.), Alejandro López-Ortiz, Angèle M. Hamel (eds.)
This booklet constitutes the refereed court cases of the 1st workshop on Combinatorial and Algorithmic facets of Networking, held in Banff, Alberta, Canada in August 2004.
The 12 revised complete papers including invited papers offered have been conscientiously reviewed and chosen for inclusion within the ebook. the subjects lined variety from the net graph to video game idea to thread matching, all within the context of large-scale networks. This quantity includes additionally five survey articles to around out the presentation and provides a complete creation to the topic.
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Additional info for Combinatorial and Algorithmic Aspects of Networking: First Workshop on Combinatorial and Algorithmic Aspects of Networking, CAAN 2004, Banff, Alberta, Canada, August 5-7, 2004, Revised Selected Papers
It is our hope that this line of research would ultimately lead to protocols which are comparable to TCP in simplicity (our algorithm is not) but perform well across multiple objectives. The class of utility functions we consider is not arbitrarily chosen. This is a large class, and contains the important subclass i f (xi ) where f is a uni-variate concave function (f must also be non-decreasing and f (0) must be 0). Concavity is a natural restriction, since it corresponds to the “law of diminishing returns” from economics.
C. graphs. A homomorphism from the digraph G to H is an edge-preserving mapping from V (G) to V (H). The digraphs G and H are homomorphically equivalent, written G ↔ H, if there is a homomorphism from G to H and from H to G. Note that isomorphic digraphs are homomorphically equivalent, although the converse fails. The following theorem (whose proof relies on K¨ onig’s inﬁnity lemma and the back-and-forth method, and so is omitted) characterizes near-ARO digraphs up to homomorphic equivalence. Theorem 1.
One serious drawback of this algorithm is that it requires complete knowledge about the graph G. Even though it can be implemented distributedly so that each client computes the same optimal solution, but still every one of them would have to know the entire graph. By contrast, the greedy scheme uses only local information—each client knows only about its permissible set. Suppose clients ﬁrst use the greedy to ﬁnd an initial assignment; then they execute some rounds of server switches, until a Nash equilibrium is reached; in each round, a client is allowed one switch.