Enforceable and efficient service provisioning
Provisioning resources for network services introduces the conflicting requirement for both deterministic traffic models to isolate and police users, and statistical multiplexing to efficiently utilize and share network resources. We address this issue by introducing two complimentary schemes for QoS management for deterministically policed flows. The first is adversarial mode resource allocation: here we bound the stochastic envelopes of policed flows and achieve a statistically multiplexed QoS-controlled service, even in the case that all flows are independently adversarial, i.e. when all flows are non-collusively behaving in a worst-case manner at all time scales within the constraints of their policing functions. The second scheme is non-adversarial mode, maximum-entropy allocation: here we determine the maximum-entropy stochastic envelopes of policed (but non-worst-case) flows. Consequently, this scheme exploits a further statistical multiplexing gain via a characterization of the ''most likely'' behavior of policed flows. Our key technique is to study the problem within the domain of deterministic and stochastic traffic envelopes, which allows us to explicitly consider sources with rate variations over multiple time scales, obtain results for any deterministic traffic model, and design accurate admission control tests for buffered priority schedulers. We evaluate the schemes' performance with experiments using traces of compressed video and single and dual time-scale periodic sources and show that substantial statistical multiplexing gains are achieved.

title: Efficient mobile content delivery by exploiting user interest correlation
abstract: Accelerating multimedia content delivery to mobile devices is challenging because we must not only address long and unpredictable latency created by the error-prone air interface, but also meet mobility-specific requirements such as efficient terminal power consumption. In this paper, we present a novel architecture for content delivery in the mobile environment (ACME) that significantly accelerates interactive mobile services such as Web browsing. ACME exploits the key concept of user interest correlation (the interest in the same content among different users) to push content to mobile users efficiently. Trace-driven simulations show that ACME achieves 50% effectiveness of content broadcasting by, on average, pushing content to only 0.7% to 6% of all users, thus substantially reducing mobile terminal power consumption. Furthermore, ACME has superior scalability as the push group size increases by only 62% when the total number of users increases by 16 times

title: On the effects of content compression on Web cache performance
abstract: Compression is widely used to reduce data transmission costs, but its impact on the performance of network entities such as Web caches has not yet been studied. We evaluate the impact of compression on cache performance when the cache compresses content and the client decompresses it. We further determine the performance bounds by benchmarking the best-case (server compression-client decompression) and the worst-case (cache compression and decompression) scenarios. Our results show that caching compressed content significantly improves the cache's hit ratio, reduces user latency and increases cache throughput. With a compression ratio of 1.69:1, we see a 43% increase in hit ratio, a 12% decrease in average end-to-end latency and a 29% latency reduction for cache hits. We also find that cache hit ratio is critical to average end-to-end latency. Even in the worst-case, the cost of compressing and decompressing content is outweighed by the benefits at a reasonable compression ratio of 1.58:1.

title: Modelling and heuristics of FMS scheduling with multiple objectives
abstract: The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, we make use of one of the multiple objective decision-making methods, a global criterion approach, to develop a multi-objective model for solving FMS scheduling problems with consideration of three performance measures, namely minimum mean job flow time, mean job tardiness, and minimum mean machine idle time, simultaneously. In addition, hybrid heuristics, which are a combination of two common local search methods, simulated annealing and tabu search, are also proposed for solving the addressed FMS scheduling problems. The feasibility and adaptability of the proposed heuristics are investigated through experimental results.

title: Preserving traffic privacy in wireless mesh networks
abstract: In this paper, the traffic confidentiality and study of the traffic pattern concealment problem via routing control is focused. First, the number of nodes in a WMN is limited. Second, traffic forwarding relationship among nodes is strongly dependent on their locations and the network topology, which is static and known a priori in WMN. To better utilize the wireless channel resource and enhance the data delivery performance, a short path is usually selected. In light of these problems, it is aimed to design a light weight traffic privacy preserving mechanism for WMN. The penalty-based routing algorithm is proposed to achieve the goal of hiding traffic pattern by exploiting the richness of available paths between two nodes in WMN. The algorithm operates in three phases, path pool generation, candidate path selection and individual packet routing. These results show that with this algorithm, the destination node is able to consistently limit the proportion of mutual information it shares with the observing nodes

title: Regression analysis of multiple protein structures
abstract: A general framework is presented for analyzing multiple protein structures using statistical regression methods. The regression approach can superimpose protein structures rigidly or with shear. Also, this approach can superimpose multiple structures explicitly, without resorting to pairwise superpositions. The algorithm alternates between matching corresponding landmarks among the protein structures and superimposing these landmarks. Matching is performed using a robust dynamic programming technique that uses gap penalties that adapt to the given data. Superposition is performed using either orthogonal transformations, which impose the rigid-body assumption, or affine transformations, which allow shear. The resulting regression model of a protein family measures the amount of structural variability at each landmark. A variation of our algorithm permits a separate weight for each landmark, thereby allowing one to emphasize particular segments of a protein structure or to compensate for variances that differ at various positions in a structure. In addition, a method is introduced for finding an initial correspondence, by measuring the discrete curvature along each protein backbone. Discrete curvature also characterizes the secondary structure of a protein backbone, distinguishing among helical, strand, and loop regions. An example is presented involving a set of seven globin structures. Regression analysis, using both affine and orthogonal transformations, reveals that globins are most strongly conserved structurally in helical regions, particularly in the mid-regions of the E, F, and G helices.

title: Regression Analysis of Multiple Protein Structures

title: Fast probabilistic analysis of sequence function using scoring matrices
abstract: We present techniques for increasing the speed of sequence analysis using scoring matrices. Our techniques are based on calculating, for a given scoring matrix, the quantile function, which assigns a probability, or p, value to each segmental score. Our techniques also permit the user to specify a p threshold to indicate the desired trade-off between sensitivity and speed for a particular sequence analysis. The resulting increase in speed should allow scoring matrices to be used more widely in large-scale sequencing and annotation projects.



We develop three techniques for increasing the speed of sequence analysis: probability filtering, lookahead scoring, and permuted lookahead scoring. In probability filtering, we compute the score threshold that corresponds to the user-specified p threshold. We use the score threshold to limit the number of segments that are retained in the search process. In lookahead scoring, we test intermediate scores to determine whether they will possibly exceed the score threshold. In permuted lookahead scoring, we score each segment in a particular order designed to maximize the likelihood of early termination. Our two lookahead scoring techniques reduce substantially the number of residues that must be examined. The fraction of residues examined ranges from 62 to 6%, depending on the p threshold chosen by the user. These techniques permit sequence analysis with scoring matrices at speeds that are several times faster than existing programs. On a database of 12 177 alignment blocks, our techniques permit sequence analysis at a speed of 225 residues/s for a p threshold of 10-6, and 541 residues/s for a p threshold of 10-20. In order to compute the quantile function, we may use either an independence assumption or a Markov assumption. We measure the effect of first- and second-order Markov assumptions and find that they tend to raise the p value of segments, when compared with the independence assumption, by average ratios of 1.30 and 1.69, respectively. We also compare our technique with the empirical 99. 5th percentile scores compiled in the BLOCKSPLUS database, and find that they correspond on average to a p value of 1.5 x 10-5.


AVAILABILITY
The techniques described above are implemented in a software package called EMATRIX. This package is available from the authors for free academic use or for licensed commercial use. The EMATRIX set of programs is also available on the Internet at http://motif.stanford.edu/ematrix.