The performance analysis of leaky bucket algorithm with Markov-modulated Bernoulli process

This paper presents a traffic model for an ATM multiplexer providing video, voice, image, and data services. The traffic model classifies the input traffic into two types: real-time and non-real-time. The input process for real-time traffic is a Markov modulated Bernoulli process (MMBP), while that for non-real-time traffic is a Bernoulli process with batches. The z-transform of the cell interarrival time A/sub MMBP/(z) is obtained. The average traffic load /spl rho//sub MMBP/ and the squared coefficient of variation of the time between success cell C/sup 2//sub MMBP/ are then obtained from the respective derivatives of A/sub MMBP/(z). The autocorrelation coefficient of the cell interarrival time with lag 1 /spl psi/, can be also obtained. This paper presents an iterative algorithm that can be used to calculate the MMBP probability distribution of the cell interarrival time. So far few results about discrete-time finite buffer queues with correlated inputs are known. This paper analyzes the performance of an ATM multiplexer considering the correlation and burst in cell arrivals. For the MMBP-stream, the waiting time distribution and the cell loss probability for MMBP+M/sup [X]//D/1/K finite buffer system are all exactly calculated with the use of a recursive algorithm. Calculations show the influence of burstiness and correlation on both the loss probability and waiting time. The accuracy of the analytical results has been largely validated by means of a simulation approach.

This paper presents analyses to study the persession performance of an ATM multiplexer which finds application in call admission control (CAC). We consider a scenario where a switching node is non-blocking and output buffered, links are of different speeds and the traffic is bursty with correlated arrivals. We model the session of interest as a Markov modulated Bernoulli process (MMBP) and the interfering traffic as an MMBP with batch arrivals. We propose an efficient method to obtain the exact steady-state performance results of a session. An approximation technique is also presented. This method could also be used to analyze any Markov renewal traffic model such as discrete-batch Markovian arrival process (D-BMAP), and study the transient behavior of a session.

In the ATM network, the leaky bucket scheme is a promising method that regulates input traffics for preventive congestion control. In order to satisfy the loss probability constraint of more bursty input traffic, it is known that the leaky bucket scheme with static token generation interval requires larger data buffer and token pool size. This causes an increase in the mean waiting time for input traffic to pass the leaky bucket scheme, which would be inappropriate for real time traffics such as voice and video. The authors present the leaky bucket scheme with threshold-based token generation intervals in which the token generation interval changes according to buffer occupancy. The proposed leaky bucket scheme is analysed in the discrete-time case by assuming the arrival process to be a Markov-modulated Bernoulli process (MMBP). The authors show that the quality of service (QoS) of input traffics is satisfied with smaller data buffer and token pool size in the proposed leaky bucket scheme.

How the traffic characteristics and the performance of a transport connection are affected at the link-transport layer is one of the important items to be studied to determine the end to end performance of a transport protocol. We model the link-transport layer that provides service to an XTP connection as a discrete time single server finite capacity queueing system at which two different arrival processes are allowed to merge, e.g., a Markov Modulated Bernoulli Process (MMBP) and a Bernoulli Process (BP). The MMBP models the bursty and correlated traffic from the designated XTP connection, and the BP models the external traffic, that is, the arrival of the packets from the rest of the coexisting connections at the link-transport layer. An exact analysis to obtain the queue length distributions, and the probability densities and the correlation coefficients of the interdeparture time distributions for the packets from the designated XTP connection is presented. Numerical examples are also investigated to show how the traffic characteristics of the designated XTP connection are affected by the amount of the external traffic, the degree of the burstiness and the correlation of the traffic source.KeywordsQueue LengthArrival ProcessInterarrival TimePacket ArrivalWait Time DistributionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the ATM network, various kinds of traffic will be supported. This includes traffic which requires high quality of service. For example, coded HDTV requires no cell loss because of high compression and no possibility of retransmission. CBR (constant bit rate) services require no delay jitter. Thus, it will be required to support some class of services with no cell loss and no delay jitter. This paper proposes a new transmission method Q-STM, with which no cell loss and no delay jitter can be supported in the ATM network. The Q-STM introduces two new concepts into the ATM networks; frame structure and time slot reservation. A frame structure itself is similar to the framed ATM. However, in the Q-STM, a frame is divided into some subframes and each subframe is a unit of time switching in intermediate switching nodes. This leads to smaller end-to-end delay than that of the framed ATM. Time slots in a frame are reserved for each Q-STM call in a call setup phase and this leads to no delay jitter and no cell loss. Ordinary ATM service class is also supported and this kind of cell uses an empty slot which is not reserved or is not used because of fluctuation of Q-STM call transmission rate.

The layer3 switch enables us to fast transmit IP datagrams using the cut-through technique. The current layer3 router would become a bottleneck in terms of delay performance, as the amount of traffic injected into high speed networks gets relatively large. Thus, the layer3 switch should be an important element in constructing the next generation Internet backbone. We analyze the cut-through rate, the datagram waiting time and the mis-ordered rate of a layer3 switch in case of flow-driven connection setup. In the analysis, by using a 3-state Markov modulated Bernoulli process (MMBP), we model the arrival process of IP flow and IP datagram from each source. Furthermore, we investigate impacts of the arrival rate and the average datagram length on performance.

In this paper, we examine the queuing performance of terminals in a multichannel centralized S-ALOHA system with a finite terminal population and finite queue size in each terminal, employing a uniform backoff (UB) algorithm with retry limit for collision resolution. The performance evaluations focus on uplink traffic from web browsing, which is modeled as a Markov-modulated Bernoulli process with correlated arrivals. We analyze the system performance in terms of system throughput, mean queue length, mean delay, the probability that a packet is dropped by retry limit, the probability that a packet is blocked by a full queue, and the mean and variance of packet retransmission time, in relation to the source correlation, number of channels, window size, and retry limit. For comparison, we also consider bufferless terminals with correlated arrivals. In addition, we evaluate by simulations the performance of terminals with finite queue size when they have perfect knowledge of the backlog size. Results from our study allow the parameters of the UB algorithm to properly be chosen to meet the access-level quality-of-service requirements.

Random Access techniques are many, most of which are designed for a limited number of mobile nodes. However, in a 5G Internet of Things environment, the design of random access must accommodate a very large number of devices which are heterogeneous in nature, and packets are in general short and come in a variety of lengths. Variable lengths of short packets may waste bandwidth if each packet transmits at the beginning of a time slot. In this paper, packet squeezing allows for a packet to make a selection between aligning to the beginning and aligning to the ending of a time slot. Two or more nodes transmitting at the same time slot may not result in a collision if two conditions are met simultaneously. First, one node selects an alignment mode different from that of the remaining nodes. Secondly, the sum of the time durations of two “representative” packets is shorter than the duration of a time slot. The performance analysis demonstrates that the throughput gain due to the packet squeezing is significant. The gain of packet squeezing can be enhanced by time slot management, where a transmitting node randomly selects a set of points of Finite Projective Plane and transmits at the time slots corresponding to the points of the chosen set. Due to the rather uniform distribution of the transmitting nodes and fewer empty time slots, the gain of packet squeezing is further improved for the entire frame. Based on the simulation results and analysis, we have observed that the time slot management extends the range of packet arrival rate of peak throughput for packet squeezing. Lastly, we compare and contrast the combining of packet squeezing and time slot management with well-known random access protocols of IoT such as slotted ALOHA and NOMA, and simulate real systems to verify the results of performance analysis.

An asynchronous transfer mode ATM network is a high-speed multimedia network that handles various kinds of traffic with different bit rates and different qualities of services QoS. To maintain QoS for each traffic source and to avoid a possible congestion problem, an ATM network requires highly sophisticated and flexible controllers to insure that the demanding performance can be achieved under unexpected changes in traffic conditions. In this article we propose an intelligent architecture using neural networks for traffic congestion control in an ATM network. The congestion control using neural networks is suitable for an ATM because neural networks can learn the offered traffic characteristics and the dynamic changes of the traffic. The proposed mechanism is based on the adaptive prediction of the future value of the offered traffic and the flow rate for each traffic source. At every given time slot, the controllers in the proposed architecture predict whether the congestion will happen or not and regulate the volume of input traffic for each traffic source before the congestion happens, maintaining the user-required QoS for each traffic source based on the predefined rules. Consequently, the mechanism guarantees the QoS for each traffic source and efficiently prevents congestion.

We consider a single-hop, random-access, wireless sensor network (WSN) performing sequential distributed detection under a bandwidth constraint. At each time slot, the sensor nodes will make their decisions on whether the observed event is happening. The sensor censoring strategy is applied such that only the local decisions equal to one (i.e., the event happens) will be sent to the fusion center (FC). Because only one transmission channel between the FC and the sensor nodes is assumed, if two or more local decisions are sent at a time slot, a packet collision happens. We design a sequential probability ratio test (SPRT) at the FC that is aware of the collisions. The performance measures of the collision-aware SPRT are analyzed based on a Markov chain. In addition, we derive the asymptotic relative efficiency of the collision-aware SPRT relative to another SPRT. Numerical results show some interesting characteristics of the collision-aware SPRT. For example, the collision-aware SPRT prefers the scenario that many local decisions are likely to be sent per time slot.

EAPRAD: A MAC protocol for Enhancing Access Probability and Reducing Access Delay in VANETs

This article investigates how to exploit the cooperative mechanism between non-orthogonal multiple access (NOMA) user pairs to enhance the security of the mobile-edge computing (MEC) system. Considering the different delay requirements of Internet of Things (IoT) users, we propose a two-slot hybrid cooperative NOMA security (THCNS) scheme that utilizes the cooperative interference between NOMA user pairs to enhance the security of offloading. In the first time slot, the interference to the eavesdropper comes from the task signals (TSs) transmitted by the two users, while in the second time slot comes from jamming signal (JS) transmitted by the user who has completed the offloading tasks. The weighted sum secrecy outage probability (wSOP) and secrecy computation probability (SCP) are utilized to measure the security performance of users in the proposed scheme. To confirm the feasibility and applicability of the proposed scheme, we analyze the impact of some key parameters on users’ security, including the users’ local computation time and offloading time, transmit power, task allocation coefficients of two time slots. The analysis results indicate that the proposed scheme is more suitable for the case where the wiretap channel outperforms the main channel, and it can enhance the system security when selecting the optimal parameters. Finally, the numerical simulation results reveal how the analysis parameters affect the security performance of users and validate the theoretical analysis, it provides good insights about how to design parameters of IoT users to enhance their security performance in MEC system.

A phenomenological interpolation model for the transition operator of a stationary Markov process is shown to be equivalent to the simplest difference approximation in the master equation for the conditional density. Comparison with the formal solution of the Fokker-Planck equation yields a criterion for the choice of the correlation time in the approximate solution. The interpolation model is shown to be form-invariant under an iteration-cum-rescaling scheme. Next, we go beyond Markov processes to find the effective time-development operator (the counterpart of the conditional density) in the following very general situation: the stochastic interruption of the systematic evolution of a variable by an arbitrary stationary sequence of randomizing pulses. Continuous-time random walk theory with a distinct first-waiting-time distribution is used, along with the interpolation model for the transition operator, to obtain the solution. Convenient closed-form expressions for the ‘averaged’ time-development operator and the autocorrelation function are presented in various special cases. These include (i) no systematic evolution, but correlated pulses; (ii) systematic evolution interrupted by an uncorrelated (Poisson) sequence of pulses.

A discrete time queueing model for the performance of an ATM system is analyzed using matrix analytic methods. Time is segmented into slots with each slot equal to the transmission time of one ATM cell. The ATM system is modeled as a single server queue with Markovian arrivals and service time equal to one slot. The arrival process includes as a special case the superposition of on-off sources, possibly heterogeneous. The queueing model is of the M/G/1 type. By exploring the structure of the M/G/1 type Markov chain the complexity of the solution to the problem is reduced to only the inversion of a 2/spl times/2 matrix irrespective of the size of the Markov chain. This simplification allows us to solve problems of large sizes. In this paper, we generalize this basic model without losing much of the computational simplifications to the case where there are multiple states corresponding to k (k/spl ges/1) arrivals in a time slot (as opposed to a one-to-one mapping between the number of arrivals and the number of states in the Markov chain) providing a semi-Markov model for the number of arrivals.

Performance analysis of statistical multiplexing of heterogeneous discrete-time Markovian arrival processes in an ATM network