Service chaining is attracting attention as a promising technology for providing a variety of network services by applying virtual network functions (VNFs) that can be instantiated on commercial off-the-shelf servers. The data transmission for each service chain has to satisfy the quality of service (QoS) requirements in terms of the loss probability and transmission delay, and hence the amount of resources for each VNF is expected to be sufficient for satisfying the QoS. However, the increase in the amount of VNF resources results in a high cost for improving the QoS. To reduce the cost of utilizing a VNF, sharing VNF instances through multiple service chains is an effective approach. However, the number of packets arriving at the VNF instance is increased, resulting in a degradation of the QoS. It is therefore important to select VNF instances shared by multiple service chains and to determine the amount of resources for the selected VNFs. In this paper, we propose a cost-effective service chain construction with a VNF sharing model. In the proposed method, each VNF is modeled as an M/M/1/K queueing model to evaluate the relationship between the amount of resources and the loss probability. The proposed method determines the VNF sharing, the VNF placement, the amount of resources for each VNF, and the transmission route of each service chain. For the optimization problem, these are applied according to our proposed heuristic algorithm. We evaluate the performance of the proposed method through a simulation. From the numerical examples, we show the effectiveness of the proposed method under certain network topologies.

Bitcoin is one of popular cryptocurrencies widely used over the world, and its blockchain technology has attracted considerable attention. In Bitcoin system, it has been reported that transactions are prioritized according to transaction fees, and that transactions with high priorities are likely to be confirmed faster than those with low priorities. In this paper, we consider performance modeling of Bitcoin-blockchain system in order to characterize the transaction-confirmation time. We first introduce the Bitcoin system, focusing on proof-of-work, the consensus mechanism of Bitcoin blockchain. Then, we show some queueing models and its analytical results, discussing the implications and insights obtained from the queueing models.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

The 5G mobile network environment has been studied and developed, and the concept of a vEPC (Virtualized Evolved Packet Core) has been introduced as a framework for Network Functions Virtualization (NFV). Machine-to-Machine (M2M) communications in 5G networks require much faster response than are possible in 4G networks. However, if both the control plane (C-plane) and the data plane (D-plane) functions of the EPC are migrated into a single vEPC server, M2M devices and other user equipments (UEs) share the same resources. To accommodate delay-sensitive M2M sessions in vEPC networks, not only signaling performance on the C-plane but also packet processing performance on the D-plane must be optimized. In this paper, we propose a method for optimizing resource assignment of C-plane and D-plane Virtualized Network Functions (VNFs) in a vEPC server, called the vEPC-ORA method. We distinguish the communications of M2M devices and smartphones and model the vEPC server by using queueing theory. Numerical analysis of optimal resource assignment shows that our proposed method minimizes the blocking rates of M2M sessions and smartphone sessions. We also confirmed that the mean packet processing time is kept within the allowable delay for each communication type, as long as the vEPC server has enough VM resources. Moreover, we study a resource granularity effect on the optimal resource assignment. Numerical analysis under a fixed number of hardware resources of MME and S/P-GW is done for various resource granularities of the vEPC server. The evaluation results of numerical analyses showed that the vEPC-ORA method derives the optimal resource assignment in practical calculation times.

In emergency situations, telecommunication networks become congested due to large numbers of call requests. Also, some infrastructure breaks down, so undamaged communication resources must be utilized more efficiently. Therefore, several lines in telephone exchanges are generally reserved for emergency calls whose users communicate crucial information. The number of lines reserved for emergency calls is determined by a threshold, on a trunk reservation control method. To accept both required emergency calls and more general calls, the traffic intensity of arriving emergency calls should be estimated in advance, and a threshold should be configured so that the number of reserved lines becomes lower than the estimation. Moreover, we propose that the holding time for general calls should be positively limited. By guaranteeing the holding time sufficient for communicating essential information, holding time limitation reduces long-period calls so more general calls are accepted. In this paper, we propose a new CAC method to utilize undamaged communication resources more efficiently during emergencies. Our proposed method accepts more general calls by collaboratively relaxing the threshold of trunk reservation and limiting holding time of general calls. This method is targeted at not only the telephone exchange but also various systems on networks, e.g. base stations of the wireless network or SIP servers. With our method, the threshold is configured in consideration of the ratio of traffic intensities estimated in advance. We modeled the telephone exchange as a queueing loss system and calculated call-blocking rates of both emergency and general calls by using computer simulation. The comparison with the conventional holding time limitation method showed that our proposed method accepts the required number of emergency calls by appropriately relaxing the threshold, while suppressing the increase in call-blocking of general calls.

We have proposed a novel call admission control (CAC) method for maximizing total user satisfaction in a heterogeneous traffic network and showed their effectiveness by using the optimal threshold from numerical analysis [1],[2]. With these CAC methods, it is assumed that only selfish users exist in a network. However, we need to consider the possibility that some cooperative users exist who would agree to reduce their requested bandwidth to improve another user's Quality of Service (QoS). Under this assumption, conventional CAC may not be optimal. If there are cooperative users in the network, we need control methods that encourage such user cooperation. However, such “encourage” control methods have not yet been proposed. Therefore, in this paper, we propose novel CAC methods for cooperative users by using queueing theory. Numerical analyses show their effectiveness. We also analyze the characteristics of the optimal control parameter of the threshold.

Due to the high speed mobile environment, the aeronautical Cognitive Radio (CR) communications base on the channel with the time-variant stochastic non-continuous spectrum. The traditional fading channel models, such as Rayleigh, Rice, Nakagami-m and multipath channel models, can not describe the whole property of the channels of CR communications. In this letter, the statistical channel modeling scheme for aeronautical CR communications is proposed with a M/M/s(1) queuing model, which properly describes the random spectrum occupation of the primary users, i.e. aircrafts in aeronautical communications. The proposed channel model can be easily utilized in the channel simulation to testify the validity and efficiency of the aeronautical CR communications.

Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.

This letter presents a model with queueing theory to analyze the performance of non-saturated IEEE 802.11 DCF networks. We use the closed queueing network model and derive an approximate representation of throughput which can reveal the relationship between the throughput and the total offered load under finite traffic load conditions. The accuracy of the model is verified by extensive simulations.

The author proposes a flow control scheme which derives the optimal packet transmission rate from the ACKs of the sending packets. The optimization is based on mathematical programming such as the extremal method and least-squares method. The author proves that the proposed method is fair when the RTT and thepacket length of each sender are the same. It is also shown that the sufficient condition for the proposed method to be optimal and stable generally holds true in packet networks. The performances are examined by computer simulations, and it is found that high throughput is obtained regardless of the network structure.

We consider a finite-capacity single-server queue with constant service and vacation times, which is seen in the time division multiple access (TDMA) scheme. First we derive the probability that j customers remain in the queue when a test customer arrives. Using this probability we then evaluate the probability that the test customer who arrives during the vacation or service time has to wait in the queue for longer than a given time. From these results, we obtain the waiting time distribution for the customer arriving at an arbitrary time. We also show a practical application to wireless TDMA communications systems.

The emerging, ATM network will support permanent, semi-permanent and switched virtual channel connections (SVCC). A number of simulation studies have been carried out to study the performance of SVCCs using empirical data. The absence of an analytical model has prevented the study of SVCCs under known traffic distributions. In this paper, we develop a new delayed vacation model to facilitate the performance study of SVCCs with configurable inactivity timer in the ATM network interface card (NIC). Comparison with simulation results indicates that the proposed model is very accurate and can be effectively used to optimise the performance of SVCCs by selecting an appropriate inactivity timer.

Go-Back-N automatic repeat request (GBN ARQ) and Stop-and wait (SW) ARQ schemes are one of fundamental and widely used error control procedures for data communication and computer communication systems. The throughput and delay performances of these ARQ schemes have been analyzed for a random error channel, which could not applicable for a radio channel, for example. In this paper, considering the correlated, noisy channel, we derive the exact formula for the delay of a frame in GBN and SW ARQ schemes. First, the delay formula for the discrete time M[x]/G/1 queueing system with starter. Next, the virtual service time of a frame is found in terms of the decay factor of a two-state Markov chain. As a result, it is shown that the performance of the delay is improved with the larger decay factor.

Processors are important resources of stored program control (SPC) switching systems, and estimation of their workload level is crucial to maintaining service quality. Processor utilization is measured as processor usage per unit time, and workload level is usually estimated from measurement of this utilization during a given interval. This paper provides an approximate distribution of processor utilization of SPC switching systems, and it provides a method for designing an overload detection scheme. This method minimizes the observation interval required to keep overload detection errors below specified values. This observation interval is obtained as an optimal solution of a linear programming.