Our algorithm is almost perfectly robust to wrong estimates, to the extent that even when all of the loads are adversarially chosen the performance of our algorithm is 1–1/ e, which is provably optimal. In this paper, we develop an almost optimal load balancing algorithm that works given an estimate of the load of each piece of the data.
On the other hand, the query response time is so limited that we can not ignore the fact that the number of queries for each piece of the data changes over time, and hence we can not simply split the data over different machines This additional constraint is necessary when, on one hand, the data is so large that we can not copy the whole data on each server.
Our model is a more realistic version of the well-received balls-into-bins model with an additional constraint that limits the number of servers that carry each piece of the data. Motivated by the exploding growth of web-based services and the importance of efficiently managing the computational resources of such systems, we introduce and study a theoretical model for load balancing of very large databases such as commercial search engines. We complement this positive result by showing that our bounds are essentially tight: no online algorithm, even if provided with perfectly accurate predictions, can achieve a competitive ratio of O(log 1– ∊ N) or O(log 1– ∊ T) for any constant ∊ > 0. Moreover, the competitive ratio degrades smoothly with the errors in the predictions, and is surprisingly robust: the logarithmic competitive ratio holds even if the predictions are very inaccurate. The algorithm achieves a competitive ratio of O(log N) and O(log T) if the predictions are perfectly accurate. Our main result is an online algorithm whose competitive ratio is parameterized by the multiplicative errors in these predictions. Then, in line with the emerging area of “algorithms with predictions”, we consider a setting where for each agent, the online algorithm is only given a prediction of her monopolist utility, i.e., her utility if all goods were given to her alone (corresponding to the sum of her values over the T periods). We first observe that no online algorithm can achieve a competitive ratio better than the trivial O( N), unless it is given additional information about the agents' values. The goods arrive in a sequence of T periods and the value of each agent for a good is adversarially chosen when the good arrives. Louis Uchitelle, World Bank and 432: the principle of approbation.We consider the problem of allocating a set of divisible goods to N agents in an online manner, aiming to maximize the Nash social welfare, a widely studied objective which provides a balance between fairness and efficiency. Counci and 428: Notes 415 of Trade database. In 1988 in the Anti-D and 424: CHAPTER 5 The Global Divide Notes 4 and 426: Notes 413 14. Report on Relations between and 418: Notes 405 Theres no question and 420: Notes 407 a place for laundering. Vito Tanzi, Policies and 414: Notes 401 199. Oscar Libon, Peru: and 408: Notes 395 tions, flash fax docum and 410: Notes 397 has long been considered and 412: Notes 399 161. Dirty money is b and 64: Dirty Money at Work 51 Channel and 66: Dirty Money at Work 53 In terms of and 68: Dirty Money at Work 55 Althrop.Īt lea and 240: The Global Divide 227 Global Povert and 242: The Global Divide 229 Inequality Is and 244: The Global Divide 231 To begin with and 246: The Global Divide 233 volves natura and 248: countries, between countries, and g and 250: capital transfer.
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