Queuing Performance of Multichannel S-ALOHA Systems With Correlated Arrivals

Cloud computing is a new economic computing paradigm in which information and computer resources can be accessed from a Web browser by customers. The process of a job entering into the cloud schedule is generally in the form of queue, so that each user needs to wait until the earlier users are being served. Whenever a new job enters the cloud buffer, the queue length increases so the waiting time. There are many methods like FIFO, Priority scheduling, Round robin etc to dispatch the job from buffer to a machine. In this paper, we developed and analyzed a cloud and provided service using Queuing analogy. The transient analysis of cloud studied by deriving various parameters like mean queue length, Utilization, Throughput and Mean Waiting Time in the cloud. It is observed that using queuing theory in the service has tremendous influence on Mean Delay, Throughput and Congestion Control.

This paper presents a new approach for analyzing the risks associated with unexpected delays of a customer in a queuing system. The customer could be a person, a machine component awaiting service, a job awaiting processing, etc. Extreme delays in service completion can have catastrophic consequences. Traditional queuing theory provides a measure of average risk (e. g. mean queue length and mean delay). Our approach treats the extreme delays explicitly through the use of conditional expectations of the time in the system. We examine the scenario where the decision maker can reduce the risk of delays in an M/M/1 queue by increasing the queuing rate of service. This leads to risk/cost tradeoffs, and we show how the decision maker can utilize the conditional expectation to aid in making this tradeoff.

The IEEE 802.15.4 standard utilizes the Binary Exponential Backoff (BEB) algorithm to control nodes' access to the shared wireless medium. The main drawback of BEB is that it updates the size of the contention window (CW) without taking into consideration the number of competing nodes and the conditions in the communications medium. Therefore, BEB has been shown to be inefficient in terms of channel utilization and fairness among the contending nodes. In this paper, we propose Adaptive Backoff Algorithm (ABA), a new backoff algorithm that adaptively determines the appropriate size of CW based on the collisions experienced by the nodes. That is, while BEB updates CW in a deterministic fashion, we introduce a probabilistic methodology to achieve that update. Our simulations compare the performance of ABA with that of BEB as well as three other algorithms proposed in the literature, namely, NO-BEB, KEB, and IBEB. The performance is studied in terms of power consumption, reliability, and channel utilization. Our results show that ABA outperforms the aforementioned algorithms while granting each node a fair access to the wireless medium.

The Low Rate Wireless Personal Area network (LR-WPAN), that is IEEE 802.15.4 is extensively used for many areas of applications. The Slotted CSMA/CA mechanism is used by the standard in its contention access period (CAP) in beacon enabled mode. The protocol supported a binary exponential backoff (BEB) algorithm. In this paper, we investigate the saturation throughput, mean frame service time or delay and energy of this standard using the existing BEB algorithm and compares it with three other backoff schemes-exponential increase exponential decrease (EIED), exponential increase linear decrease (EILD), and exponential increase multiplicative decrease (EIMD) algorithm. From simulation results, it is obtained that EIED, EILD, EIMD performs better than BEB for higher loads that is EIED, EILD, EIMD has better throughput, less delay, using minimal power. The EIED propagates the highest throughput, less delay and minimal energy used among all of these schemes.

The distributed coordination function (DCF) of IEEE 802.11 standard adopts the binary exponential backoff (BEB) for collision avoidance. In DCF, the contention window is reset to an initial value, i.e., CWmin, after each successful transmission. Much research has shown that this dramatic change of window size may degrade the network performance. Therefore, backoff algorithms, such as gentle DCF (GDCF), multiplicative increase-linear decrease (MILD), exponential increase-exponential decrease (EIED), etc., have been proposed that try to keep the memory of congestion level by not resetting the contention window after each successful transmission. This paper proposes a multichain backoff (MCB) algorithm, which allows stations to adapt to different congestion levels by using more than one backoff chain together with collision events caused by stations themselves as well as other stations as indications for choosing the next backoff chain. The performance of MCB is analyzed and compared with those of 802.11 DCF, GDCF, MILD, and EIED backoff algorithms. Simulation results show that, with multiple backoff chains and collision events as reference for chain transition, MCB can offer a higher throughput while still maintaining fair channel access than the existing backoff algorithms

Since the IEEE 802.11 DCF adopted the Binary Exponential Back-off (BEB) algorithm in its contention window adjustment scheme, many alternative back-off algorithms were proposed in order to enhance the performance. Among them is an Exponential Increase Exponential Decrease (EIED) back-off algorithm, whose performance was simulated and analyzed in a special case of doubling or halving the contention window size. This paper provides an analytical analysis for the general EIED algorithm. A closed form saturation throughput and a closed form saturation delay are derived. Our analytical results further establish the performance improvement of the general EIED algorithm over the BEB algorithm and the basic EIED algorithm.

Effects of Selection for Body Weight, Egg Weight and Heterozygosis on Laying House Performance

Multipath routes are claimed to improve throughput, end-to-end delay and the reliability of data transport in wireless sensor networks (WSNs). However we have observed that multipath routing does not perform better than single path routing because the default parameter settings in 802.11 cause high rates of packet collisions. In this paper we report the results of a series of simulations based on a full-factorial experimental design. Our results show that congestion window size and retry limit are the key factors. Simulations with the tuned parameter values show significant improvement in both zone disjoint and node disjoint multipath routing schemes.

In the emerging standard IEEE 802.11e, the Enhanced Distributed Channel Access (EDCA) is specified to support quality-of-service (QoS) in wireless local area networks (WLAN). This paper aims at providing a thoroughly analytical model that captures the operation of the AIFSs, contention window sizes and retry limits differentiation for different services of the EDCA mechanism under saturation condition based on a three-dimensional Markov Chain model. The analytical model is validated and provides an in-depth understanding and insights into the EDCA protocol, and effectiveness of different parameters on the system performance in terms of system throughput and frame dropping probability for differentiation services traffic is investigated through extensive numerical and simulation results. Moreover, the analytical model can be cooperated into the admission control algorithm with different parameters in terms of AIFS, contention window sizes and retry limits.

The minority cytotype exclusion principle describes how random mating between diploid and autotetraploid cytotypes hinders establishment of the rare cytotype. We present deterministic and stochastic models to ascertain how selfing, inbreeding depression, unreduced gamete production, and finite population size affect minority cytotype exclusion and the establishment of autotetraploids. Results demonstrate that higher selfing rates and lower inbreeding depression in autotetraploids facilitate establishment of autotetraploid populations. Stochastic effects due to finite population size increase the probability of polyploid establishment and decrease the mean time to tetraploid fixation. Our results extend the minority cytotype exclusion principle to include important features of plant reproduction and demonstrate that variation in mating system parameters significantly influences the conditions necessary for polyploid establishment.

Coevolution fails to maintain genetic variation in a host–parasite model with constant finite population size

We introduce a method for systematically reducing the dimension of biophysically realistic neuron models with stochastic ion channels exploiting time-scales separation. Based on a combination of singular perturbation methods for kinetic Markov schemes with some recent mathematical developments of the averaging method, the techniques are general and applicable to a large class of models. As an example, we derive and analyze reductions of different stochastic versions of the Hodgkin Huxley (HH) model, leading to distinct reduced models. The bifurcation analysis of one of the reduced models with the number of channels as a parameter provides new insights into some features of noisy discharge patterns, such as the bimodality of interspike intervals distribution. Our analysis of the stochastic HH model shows that, besides being a method to reduce the number of variables of neuronal models, our reduction scheme is a powerful method for gaining understanding on the impact of fluctuations due to finite size effects on the dynamics of slow fast systems. Our analysis of the reduced model reveals that decreasing the number of sodium channels in the HH model leads to a transition in the dynamics reminiscent of the Hopf bifurcation and that this transition accounts for changes in characteristics of the spike train generated by the model. Finally, we also examine the impact of these results on neuronal coding, notably, reliability of discharge times and spike latency, showing that reducing the number of channels can enhance discharge time reliability in response to weak inputs and that this phenomenon can be accounted for through the analysis of the reduced model.

Maternally inherited Wolbachia transinfections are being introduced into natural mosquito populations to reduce the transmission of dengue, Zika, and other arboviruses. Wolbachia-induced cytoplasmic incompatibility provides a frequency-dependent reproductive advantage to infected females that can spread transinfections within and among populations. However, because transinfections generally reduce host fitness, they tend to spread within populations only after their frequency exceeds a critical threshold. This produces bistability with stable equilibrium frequencies at both 0 and 1, analogous to the bistability produced by underdominance between alleles or karyotypes and by population dynamics under Allee effects. Here, we analyze how stochastic frequency variation produced by finite population size can facilitate the local spread of variants with bistable dynamics into areas where invasion is unexpected from deterministic models. Our exemplar is the establishment of wMel Wolbachia in the Aedes aegypti population of Pyramid Estates (PE), a small community in far north Queensland, Australia. In 2011, wMel was stably introduced into Gordonvale, separated from PE by barriers to A. aegypti dispersal. After nearly 6 years during which wMel was observed only at low frequencies in PE, corresponding to an apparent equilibrium between immigration and selection, wMel rose to fixation by 2018. Using analytic approximations and statistical analyses, we demonstrate that the observed fixation of wMel at PE is consistent with both stochastic transition past an unstable threshold frequency and deterministic transformation produced by steady immigration at a rate just above the threshold required for deterministic invasion. The indeterminacy results from a delicate balance of parameters needed to produce the delayed transition observed. Our analyses suggest that once Wolbachia transinfections are established locally through systematic introductions, stochastic "threshold crossing" is likely to only minimally enhance spatial spread, providing a local ratchet that slightly-but systematically-aids area-wide transformation of disease-vector populations in heterogeneous landscapes.

Under several assumptions such as infinite population size with unlinked loci at linkage equilibrium (LD) under mutation-selection (M-S) balance, the rate (U), and the average effects (dominance and selection coefficients h and s) of deleterious genomic mutations (DGM) can be estimated by the Deng-Lynch method in some natural populations. However, all natural populations are finite in size and many of them are not large enough to be considered as approximately infinite. In the absence of an analytical estimation approach to characterize DGM in finite populations, we test the robustness and applicability of the Deng-Lynch method in finite populations with computer simulations. The results indicate that the estimation obtained by the Deng-Lynch method in finite populations with LD is generally robust when population size is greater than 400. With constant mutation effects, in outcrossing populations, the estimates U and ĥ are unbiased or only slightly upwardly biased, and ŝ is unbiased for most cases. In highly selfing populations, U and ĥ are upwardly biased, U is no more than 1.5U and ĥ is less than 1.1 h, and ŝ is either unbiased or slightly downwardly biased. With variable mutation effects, U ranges from 0.56 to 0.72U, and s ranges from 1.4 to 1.8s. Generally speaking, with the same finite population size, the estimation in outcrossing populations is better than in highly selfing populations. Given that even the order of the magnitude of the parameters of DMG (U in particular) is controversial, our investigation here may provide a basis for using the Deng-Lynch method to characterize DGM in finite populations of size greater than 400 in the presence of LD.

Analysis of genome sequences of 159 isolates of Plasmodium falciparum from Senegal yields an extraordinarily high proportion (26.85%) of protein-coding genes with the ratio of nonsynonymous to synonymous polymorphism greater than one. This proportion is much greater than observed in other organisms. Also unusual is that the site-frequency spectra of synonymous and nonsynonymous polymorphisms are virtually indistinguishable. We hypothesized that the complicated life cycle of malaria parasites might lead to qualitatively different population genetics from that predicted from the classical Wright-Fisher (WF) model, which assumes a single random-mating population with a finite and constant population size in an organism with nonoverlapping generations. This paper summarizes simulation studies of random genetic drift and selection in malaria parasites that take into account their unusual life history. Our results show that random genetic drift in the malaria life cycle is more pronounced than under the WF model. Paradoxically, the efficiency of purifying selection in the malaria life cycle is also greater than under WF, and the relative efficiency of positive selection varies according to conditions. Additionally, the site-frequency spectrum under neutrality is also more skewed toward low-frequency alleles than expected with WF. These results highlight the importance of considering the malaria life cycle when applying existing population genetic tools based on the WF model. The same caveat applies to other species with similarly complex life cycles.