The presence of diverse traffic types is well-established in IoT networks. Various probability distributions have been found to describe packets inter-arrival time contrasting with the familiar exponential distribution in traditional networks. These findings suggest the need to develop appropriate traffic models for performance analysis of IoT network systems. An essential component in IoT network is gateway as it provides connectivity to the core Internet. The IoT gateway also performs functions such as protocol translation and traffic aggregation. Therefore, efficient design of the IoT gateway is necessary for better network management. The paper presents a new analytical model, N-Gamma/M/1, for analyzing the performance of IoT gateway. The equivalence of N-Gamma/M/1 and Gamma/M/1 models is proved mathematically. Additionally, an in-depth performance evaluation of the IoT gateway under various arrival patterns is conducted through simulation. The numerical analysis of the proposed N-Gamma/M/1 model emphasizes the need for more buffers at the gateway when traffic from input devices has varying values of gamma distribution parameters. It is also noted that the IoT gateway experiences a longer mean queue length resulting in higher mean waiting time and packet loss when inter-arrival time distribution of packets follows generalized Pareto, Weibull and lognormal distributions with different parameter values. This makes the task of IoT network management challenging. Adaptive and intelligent resource allocation policies along with dynamic congestion control algorithms may provide a solution to minimize packet loss and ensure quality of service.

Abstract Component‐based software development (CBSD) is an active area of research. Ascertaining the quality of components is important for overall software quality assurance in CBSD. One of the important metrics for measuring defects, analyzability, efforts, and cost in CBSD is component size. The paper presents an analytical model based on maximization of Tsallis entropy to obtain closed form expression for component size distribution (maximum Tsallis entropy component size distribution, MTECSD) in steady state. It is found that the component size distribution follows power law asymptotically. A procedure based on generalized Jensen–Shannon measure is developed to estimate model parameters. A detailed analysis of many popular probability distributions along with MTECSD is carried out on many diverse real data sets of component‐based softwares. The analysis reveals that lognormal and MTECSD distributions fit well to component sizes in many software conforming the presence of power law behavior. The software whose component size distributions are described by MTECSD are in equilibrium implying that new defects in these software systems occur occasionally. Power law behavior in component sizes also imply high variation leading to difficulty in software analyzability. The precise knowledge of component size distribution also provides an alternative method to compute efforts and cost estimates by modified COCOMO model.

Understanding software size distribution is critical to software cost estimation using COCOMO model and design of reliable production function model. This paper proposes and validates a theoretical framework based on the maximization of Shannon entropy to learn component size distribution of software systems when partial information about the moments is given. Specification of appropriate moment constraints either in the form of shifted geometric mean or arithmetic mean or both geometric and arithmetic means are considered. The models are validated using 30 real datasets. The analysis reveals that software systems where component sizes depict power-law behavior are governed by shifted geometric mean whereas those systems in which component size distribution shows exponential behavior are described by arithmetic mean. Another type of software system is also considered where the component size distribution is found to depict gamma distribution. Such systems are characterized by specification of both arithmetic and geometric means. The study underlines that the use of modern object-oriented programming languages adheres to power-law distribution indicating the existence of team synergies leading to substantial containment of software costs when compared to the use of traditional procedural programming languages.

Tracing topology of services in real-time is a challenging problem in VoIP networks. The primary reason is that the VoIP networks operate with diverse interconnecting protocols, technologies, and multitude of access technologies. In addition, alliances, changing rules and regulations, mergers, new technologies and services impact architecture of VoIP networks consistently. All of these together results in heterogeneous, complex and dynamic VoIP networks where the network topology changes very frequently. Secondly, in case of voice, each call can follow a different path over the core IP infrastructure in the network. Even the signaling and media path of the same voice call can follow different paths to destination (callee's device). Stitching and providing integrated end to end cross layer view of the path in real-time of an ongoing service flow e.g. voice call under such unpredictable network conditions is difficult.

Presence, broadly defined as an event publish-notification infrastructure for converged applications, has emerged as a key mechanism for collecting and disseminating context attributes for next-generation services in both enterprise and provider domains. Current presence-based solutions and products lack in the ability to a) support flexible user-defined queries over dynamic presence data and b) derive composite presence from multiple provider domains. Accordingly, current uses of context are limited to individual domains/organizations and do not provide a programmable mechanism for rapid creation of context-aware services. This paper describes a presence virtualization architecture, where a Virtualized Presence Server receives customizable queries from multiple presence clients, retrieves the necessary data from the base presence servers, applies the required virtualization logic and notifies the presence clients. To support both query expressiveness and computational efficiency, virtualization queries are structured to separately identify both the XSLT-based transformation primitives and the presence sources over which the transformation occurs. For improved scalability, the proposed architecture offloads the XSLT-related processing to a high-performance XML processing engine. We describe our current implementation and present performance results that attest to the promise of this virtualization approach.

A theoretical model of loss system is proposed and analysed within the framework of maximum Tsallis entropy principle. The study provides an explicit expression for state probability distribution of packets in presence of long-range dependent traffic. The unimodal state probability distribution corresponding to well-known Erlang's loss formula is recovered for Tsallis entropy parameter q = 1. As the parameter q is lowered from unity, it is shown that the state probability distribution makes a transition from unimodal to bimodal. The emergence of bimodality can be regarded as a consequence of long-range dependence. The implication of the model in the design of loss systems is discussed.