Autonomous resource provisioning

Most B-tree papers assume that all N keys have the same size K, that f = B/K keys fit in a disk block, and therefore that the search cost is O(log f+1 N) block transfers. When keys have variable size, however, B-tree operations have no nontrivial performance guarantees. This paper provides B

of clients. These changes place the web server at the center of a gradually emerging eservice infrastructure with increasing requirements for service quality and reliability guarantees in an unpredictable and highly-dynamic environment.

Abstract — We estimate the population size by sampling uniformly from the population. Given an accuracy to which we need to estimate the population with a pre-specified confidence, we provide a simple stopping rule for the sampling process. I. SUMMARY Many applications such as species estimation [1], database sampling [2], and epidemiologic studies [3], [4], [5] call for estimating a population size based on a relatively small sample. We derive a simple, yet nearly optimal, stopping rule for sampling and an estimation formula for alphabet size from uniform samples taken from the population. We will consider an approach outlined for the species estimation problem by Good [6] further on in the summary. For a more complete survey of prior results obtained in the species estimation problem, see [1]. For problems in database sampling see [7], [2]. The results obtained in this paper are also related to capture-recapture problems [3], [4], [5], where the unknown population size is estimated given the number of samples that are recaptured (repetitions) when sampling randomly from the population. Here, we are interested in how many recaptures are necessary to estimate the population to a given accuracy with a specified confidence. Intuitively speaking, the more the number of recaptures, the better the population size can be estimated. Formally, in an n-element sample let m denote the number of distinct elements. Let r = n − m denote the number of repeated elements. For example, in c,g,c,s,g,c,v, there are n = 7 samples, there are m = 4 distinct elements, c,g,s, and v, and r = 7 − 4 = 3 repeated elements, one g and two c ′. In the following, n independent samples are drawn uniformly from a k-element population and M k n and R k n = n − M k n are the random number of distinct and repeated elements observed. We drop the subscripts and superscripts when there is no ambiguity. A. Good’s approach By linearity of expectations, E(M) = k 1 −

The performability of a system is usually evaluated as the mean of a performance measure, aggregated over all failure scenarios (both tolerated and not) through a Markov reward process. This metric can gloss over performance deficiencies in important portions of the failure scenario space

) guarantees. In order to meet the performance requirements of the applications an efficient transport protocol and congestion control mechanism becomes necessary. We propose a transport mechanism for guaranteeing application required QOS requirements over an ATM network. Leaky-bucket congestion control scheme

While today’s computer networks support only best-effort service, future packet-switching integrated-services networks will have to support real-time communication services that allow clients to transport information with performance guarantees expressed in terms of delay, delay jitter, throughput

clustering

present a novel approach to providing performance guarantees in this highly-volatile scenario, in an efficient and cost-effective way. Facade, a virtual store controller, sits between hosts and storage devices in the network, and throttles individual I/0 requests from multiple clients so that devices do

that is underloaded, i.e., whenever there exists an oline schedule meeting the deadlines of all jobs released, edf can always do so [7]. However, when the system is overloaded or involves more than one processor, edf has no performance guarantee in the sense that its performance cannot match or even be competitive