Performance controls for distributed telecommunication services

As the Internet and Telecommunications domains merge, open telecommunication service architectures such as TINA, PARLAY and PINT are becoming prevalent. Distributed Computing is a common engineering component in these technologies and promises to bring improvements to the scalability, reliability and flexibility of telecommunications service delivery systems. This distributed approach to service delivery introduces new performance concerns. As service logic is decomposed into software components and distnbuted across network resources, significant additional resource loading is incurred due to inter-node communications. This fact makes the choice of distribution of components in the network and the distribution of load between these components critical design and operational issues which must be resolved to guarantee a high level of service for the customer and a profitable network for the service operator. Previous research in the computer science domain has addressed optimal placement of components from the perspectives of minimising run time, minimising communications costs or balancing of load between network resources. This thesis proposes a more extensive optimisation model, which we argue, is more useful for addressing concerns pertinent to the telecommunications domain. The model focuses on providing optimal throughput and profitability of network resources and on overload protection whilst allowing flexibility in terms of the cost of installation of component copies and differentiation in the treatment of service types, in terms of fairness to the customer and profitability to the operator. Both static (design-time) component distribution and dynamic (run-time) load distribution algorithms are developed using Linear and Mixed Integer Programming techniques. An efficient, but sub-optimal, run-time solution, employing Market-based control, is also proposed. The performance of these algorithms is investigated using a simulation model of a distributed service platform, which is based on TINA service components interacting with the Intelligent Network through gateways. Simulation results are verified using Layered Queuing Network analytic modelling Results show significant performance gains over simpler methods of performance control and demonstrate how trade-offs in network profitability, fairness and network cost are possible

Equivalent random analysis of a buffered optical switch with general interarrival times

We propose an approximate analytic model of an optical switch with fibre delay lines and wavelength converters by employing Equivalent Random Theory. General arrival traffic is modelled by means of Gamma-distributed interarrival times. The analysis is formulated in terms of virtual traffic flows within the optical switch from which we derive expressions for burst blocking probability, fibre delay line occupancy and mean delay. Emphasis is on approximations that give good numerical efficiency so that the method can be useful for formulating dimensioning problems for large-scale networks. Numerical solution values from the proposed analysis method compare well with results from a discrete-event simulation of an optical burst switch

Simulation of a distributed CORBA-based SCP

This paper examines load balancing issues relating to a distributed CORBA-based Service Control Point. Two types of load balancing strategies are explored through simulation studies: (i) a novel ant-based load balancing algorithm, which has been devised specically for this type of system. This algorithm is compared to more traditional algorithms, (ii) a method for optimal distribution of the computational objects composing the service programs. This is based on mathematically minimising the expected communication ows between network nodes and message-level processing costs. The simulation model has been based on the recently adopted OMG IN/CORBA Interworking specication and the TINA Service Session computational object model

Multicast routing from a set of data centers in elastic optical networks

This paper introduces the Multi-Server Multicast (MSM) approach for Content Delivery Networks (CDNs) delivering services offered by a set of Data Centers (DCs). All DCs offer the same services. The network is an Elastic Optical Network (EON) and for a good performance, routing is performed directly at the optical layer. Optical switches have heterogeneous capacities, that is, light splitting is not available in all switches. Moreover, frequency slot conversion is not possible in any of them. We account for the degradation that optical signals suffer both in the splitting nodes, as well as across fiber links to compute their transmission reach. The optimal solution of the MSM is a set of light-hierarchies. This multicast route contains a light trail from one of the DCs to each of the destinations with respect to the optical constraints while optimizing an objective (e.g., minimizing a function). Finding such a structure is often an NP-hard problem. The light-hierarchies initiated from different DCs permit delivering the multicast session to all end-users with a better utilization of the optical resources, while also reducing multicast session latencies, as contents can be delivered from such DCs closer to end-users. We propose an Integer Linear Programming (ILP) formulation to optimally decide on which light-hierarchies should be setup. Simulation results illustrate the benefits of MSM in two reference backbone networks.Peer ReviewedPostprint (author's final draft

AgileDCN:An Agile Reconfigurable Optical Data Center Network Architecture

This paper presents a detailed examination of a novel data center network (DCN) that can satisfy the high capacity and low latency requirements of modern cloud computing applications. This reconfigurable architecture called AgileDCN uses fast-switching optical components with a centralized control function and workload scheduler. By providing a highly flexible optical network fabric between server racks, very high network efficiencies can be achieved even under imbalanced loading patterns. Our simulation results show that, at high (70%) loads, TCP flow completion times in the AgileDCN are significantly lower than in an equivalent electronic leaf-spine network

Perforated carcinoma of the caecum presenting as necrotising fasciitis of the abdominal wall, the key to early diagnosis and management

BACKGROUND: Necrotising Fasciitis is a life threatening soft tissue infection which requires aggressive, early surgical management. CASE PRESENTATION: We present a rare case of a retroperitoneal perforation of a carcinoma of the caecum presenting as a necrotising fasciitis of the anterior abdominal wall. CONCLUSION: This case highlights the importance of early aggressive debridement to healthy tissue limits, the consideration of a rare underlying cause, and the scope for plastic surgical reconstruction in order that aggressive initial surgery can be adequately performed

Decomposition of multivariate phenotypic means in multigroup genetic covariance structure analysis

Observed differences in phenotypic means between groups such as parents and their offspring or male and female twins can be decomposed into genetic and environmental components. The decomposition is based on the assumption that the difference in phenotypic means is due to a difference in the location of the normal genetic and environmental distributions underlying the phenotypic individual differences. Differences between the groups in variance can be accommodated insofar as they are due to differences in unique variance or can be modeled using a scale parameter. The decomposition may be carried out in the standard analysis of genetic covariance structure using, for instance, LISREL. Illustrations are given using simulated data and twin data relating to blood pressure. Other possible applications are mentioned. KEY WORDS: group differences in phenotypic means; genetic means; environmental means; genetic and environmental covariance structure; twin data; parent-offspring data

Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on sbc occasions. KEY WORDS: repeated measures; genetic and environmental covariance structure; mean trend; longitudinal twin data; genetic simplex mode; LISREL

Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design

Considerable effort has been devoted to the analysis of genotype by environment (G × E) interactions in various phenotypic domains, such as cognitive abilities and personality. In many studies, environmental variables were observed (measured) variables. In case of an unmeasured environment, van der Sluis et al. (2006) proposed to study heteroscedasticity in the factor model using only MZ twin data. This method is closely related to the Jinks and Fulker (1970) test for G × E, but slightly more powerful. In this paper, we identify four challenges to the investigation of G × E in general, and specifically to the heteroscedasticity approaches of Jinks and Fulker and van der Sluis et al. We propose extensions of these approaches purported to solve these problems. These extensions comprise: (1) including DZ twin data, (2) modeling both A × E and A × C interactions; and (3) extending the univariate approach to a multivariate approach. By means of simulations, we study the power of the univariate method to detect the different G × E interactions in varying situations. In addition, we study how well we could distinguish between A × E, A × C, and C × E. We apply a multivariate version of the extended model to an empirical data set on cognitive abilities

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe