Modelling and performance analysis of a cloud computing system using an open queueing network with multi-server queues

A queueing model of an open queueing network with multi-server queues is developed for a cloud computing system and the performance is analysed based on different system parameters such as the arrival and service rates, the number of servers in each node, the routing probability and the number of service types. By using the Laplace-Stieltjes transform (LST) technique, the close-form expression of the Cumulative Distribution Function (CDF) of the total response time is derived. On this basis, some interesting application scenarios are discussed. Detailed numerical evaluations of the developed performance metrics are conducted and simulation results are provided for the application scenario verification. The proposed system modelling and performance analysis method is expected to provide a useful reference for the design and evaluation of cloud computing systems

Performance Modeling and Optimization for a Fog-Based IoT Platform

A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. Interesting performance metrics were derived from analytical results. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Numerical evaluations for the performance and the optimization problem are provided for further understanding of the analysis. The modeling and analysis, as well as the optimization design method, are expected to provide a useful reference for the design and evaluation of fog computing systems

A Comparative Study of Power Line Communication Networks With and Without Buffer

Power line communication (PLC) has recently been receiving widespread attention due to its applications in the smart grid. We present a comparative study on the call-level performance of PLC networks with and without buffer through queueing theory. The PLC network model consists of a base station (BS) and a number of subscriber stations that are interconnected with each other and with the BS via power lines. The power line transmission channels are subject to failure during service due to disturbance. A two-dimensional Markov process is used for the modeling. Performance comparison is presented through theoretic analysis and numerical results

Performance Analysis of a Wireless Network with Opportunistic Spectrum Sharing

Abstract — We analyze the performance of a wireless system that allows opportunistic spectrum sharing. The system consists of a set of primary users sharing a set of channels over a coverage area. The resources allocated to the primary users are shared opportunistically with a set of secondary users. The secondary users are capable of detecting channels that are unused by the primary users and then making use of the idle channels. If no channel is available for a secondary call, the call waits in a buffer until either a channel becomes available or a maximum waiting time is reached. We compute the blocking probabilities, mean reconnection probability, channel utilization, and total carried traffic in the system. Our results suggest that opportunistic spectrum sharing can significantly improve the efficiency of a wireless system, without negatively impacting the performance seen by the primary users. I

Performance Modeling and Optimization for a Fog-Based IoT Platform

A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. Interesting performance metrics were derived from analytical results. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Numerical evaluations for the performance and the optimization problem are provided for further understanding of the analysis. The modeling and analysis, as well as the optimization design method, are expected to provide a useful reference for the design and evaluation of fog computing systems

Availability Modeling and Performance Improving of a Healthcare Internet of Things (IoT) System

Internet of Things (IoT) is improving human life in a more convenient and simpler way. One of the most promising IoT applications is healthcare. In this paper, an availability model of a healthcare IoT system is proposed which is composed of two groups of structures described by separate Markov state-space models. The two separate models are analyzed and combined to implement the whole IoT system modeling. The system balance equations are solved under a given scenario and some performance metrics of interest, such as probabilities of full service, degraded service, and the system unavailability, are derived. Detailed numerical evaluation of selected metrics is provided for further understanding and verification of the analytic results. An availability performance improving (API) method is also proposed for increasing the probability of system full service and decreasing the system unavailability. The proposed system modeling and performance improving method can serve as a useful reference for general IoT system design and evaluation

Performance Analysis of Unreliable Sensing for an Opportunistic Spectrum Sharing System

 Opportunistic spectrum sharing (OSS) is a promising technique to improve spectrum utilization using cognitive radios. Unreliable spectrum sensing by cognitive radios is inevitable in the OSS system. In this paper, we analyze the types of unreliable sensing and their impact on the system performance. The secondary users equipped with cognitive radios sense channels that are unused by the primary users and utilize the idle channels. An ongoing secondary user also detects when a primary user accesses its channel and then either moves to another idle channel or moves to a buffer if no idle channel is available. Unreliable spectrum sensing is modeled by false alarm and misdetection events for both initiating and ongoing secondary users. We solve the steady-state probability vector of the system and derive a set of performance metrics of interest. Numerical results are presented to highlight the analysis. The proposed modeling method can be used to evaluate the performance of future opportunistic spectrum sharing networks