An End-to-End Performance Analysis for Service Chaining in a Virtualized Network

Future mobile networks supporting Internet of Things are expected to provide both high throughput and low latency to user-specific services. One way to overcome this challenge is to adopt Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). Besides latency constraints, these services may have strict function chaining requirements. The distribution of network functions over different hosts and more flexible routing caused by service function chaining raise new challenges for end-to-end performance analysis. In this paper, as a first step, we analyze an end-to-end communications system that consists of both MEC servers and a server at the core network hosting different types of virtual network functions. We develop a queueing model for the performance analysis of the system consisting of both processing and transmission flows. We propose a method in order to derive analytical expressions of the performance metrics of interest. Then, we show how to apply the similar method to an extended larger system and derive a stochastic model for such systems. We observe that the simulation and analytical results coincide. By evaluating the system under different scenarios, we provide insights for the decision making on traffic flow control and its impact on critical performance metrics.Comment: 30 pages. arXiv admin note: substantial text overlap with arXiv:1811.0233

Bounds and Approximations for Stochastic Fluid Networks

The success of modern networked systems has led to an increased reliance and greater demand of their services. To ensure that the next generation of networks meet these demands, it is critical that the behaviour and performance of these networks can be reliably predicted prior to deployment. Analytical modeling is an important step in the design phase to achieve both a qualitative and quantitative understanding of the system. This thesis contributes towards understanding the behaviour of such systems by providing new results for two fluid network models: The stochastic fluid network model and the flow level model. The stochastic fluid network model is a simple but powerful modeling paradigm. Unfortunately, except for simple cases, the steady state distribution which is vital for many performance calculations, can not be computed analytically. A common technique to alleviate this problem is to use the so-called Heavy Traffic Approximation (HTA) to obtain a tractable approximation of the workload process, for which the steady state distribution can be computed. Though this begs the question: Does the steady-state distribution from the HTA correspond to the steady-state distribution of the original network model? It is shown that the answer to this question is yes. Additionally, new results for this model concerning the sample-path properties of the workload are obtained. File transfers compose much of the traffic of the current Internet. They typically use the transmission control protocol (TCP) and adapt their transmission rate to the available bandwidth. When congestion occurs, users experience delays, packet losses and low transfer rates. Thus it is essential to use congestion control algorithms that minimize the probability of occurrence of such congestion periods. Flow level models hide the complex underlying packet-level mechanisms and simply represent congestion control algorithms as bandwidth sharing policies between flows. Balanced Fairness is a key bandwidth sharing policy that is efficient, tractable and insensitive. Unlike the stochastic fluid network model, an analytical formula for the steady-state distribution is known. Unfortunately, performance calculations for realistic systems are extremely time consuming. Efficient and tight approximations for performance calculations involving congestion are obtained

Performance and economic analysis of hybrid microhydro systems

Microhydro (MHP) systems usually employ unregulated turbines and an electronic load controller, a demand-side control device. Existing analytical models for such systems are lacking details, especially supply-side flow control, for performance simulation at hourly or sub-hourly scales. This work developed stochastic models for downscaling of streamflow and an empirical model of MHP systems. We integrated these models within the framework of Hybrid2 tool to simulate the long-term performance of a tri-hybrid system consisting of hydropower, solar PV and wind turbine. Based on an additive model of time series decomposition, we develop a Multiple Input Single Output (MISO) model in order to synthesize an hourly time series of streamflow. The MISO model takes into account daily precipitation dataset as well as regional hydrological characteristics. The model employs a constrained Monte-Carlo Markov Chain (MCMC) algorithm which is validated against an hourly time series of flow data at Blue River at Blue, Oklahoma. A non-dimensional performance model of MHP systems is developed based on empirical data from Nepal. Three design configurations are presented for a case study. The results show that, along with a small pond that can store water for an hour at the rated capacity of MHP system, a hybrid system with half the size of the battery bank can supply the load year around at Thingan Project in Nepal. This system meets the availability requirements of the Multi-Tier Framework for measuring energy access for household supply. The new proposed system is marginal in the economic sense as well. This project can never recover the initial capital cost at a current rate of the tariff which is about 7 cents/kWh. Other O&M risks aside, the sensitivity analysis suggests that the system may barely recover the initial capital cost, excluding the subsidy, at twice the existing rate of tariff and half the interest rate. This study aspires to come up with better techniques to simulate hybrid microhydro systems and enhance their design and operation through more effective utilization of resources

Stochastic MPC Design for a Two-Component Granulation Process

We address the issue of control of a stochastic two-component granulation process in pharmaceutical applications through using Stochastic Model Predictive Control (SMPC) and model reduction to obtain the desired particle distribution. We first use the method of moments to reduce the governing integro-differential equation down to a nonlinear ordinary differential equation (ODE). This reduced-order model is employed in the SMPC formulation. The probabilistic constraints in this formulation keep the variance of particles' drug concentration in an admissible range. To solve the resulting stochastic optimization problem, we first employ polynomial chaos expansion to obtain the Probability Distribution Function (PDF) of the future state variables using the uncertain variables' distributions. As a result, the original stochastic optimization problem for a particulate system is converted to a deterministic dynamic optimization. This approximation lessens the computation burden of the controller and makes its real time application possible.Comment: American control Conference, May, 201