Abstract: Distributed cloud networking enables the deployment of a wide range of services in the form of interconnected software functions instantiated over general purpose hardware at multiple cloud locations distributed throughout the network. We consider the problem of optimal service delivery over a distributed cloud network, in which nodes are equipped with both communication and computation resources. We address the design of distributed online solutions that drive flow processing and routing decisions, along with the associated allocation of cloud and network resources. For a given set of services, each described by a chain of service functions, we characterize the cloud network capacity region and design a family of dynamic cloud network control (DCNC) algorithms that stabilize any service input rate inside the capacity region, while achieving arbitrarily close to minimum resource cost. The proposed DCNC algorithms are derived by extending Lyapunov drift-plus-penalty control to a novel multi-commodity-chain (MCC) queuing system, resulting in the first throughput and cost optimal algorithms for a general class of MCC flow problems that generalizes traditional multi-commodity flow by including flow chaining, flow scaling, and joint communication/computation resource allocation. We provide throughput and cost optimality guarantees, convergence time analysis, and extensive simulations in representative cloud network scenarios.

Abstract: Today, networks are still vulnerable to high latencies. An important reason for that is the Bufferbloat problem caused by big packet buffers as part of the forwarding equipment of modern networks. Even if these buffer sizes are configured in a reasonable way, they still have a fixed size which is always a compromise. Indeed, the required buffer size strongly depends on the RTT of the end-to-end network connection. In order to support dynamic buffer sizes and to reduce the negative impact of Bufferbloat, different Active Queue Management (AQM) algorithms have been proposed recently, such as CoDel and PIE. However, these algorithms are typically not available in state of the art network equipment.In this paper we show how recent AQM algorithms can be implemented using P4 programmable network equipment. In consequence, communication networks can be easily enhanced to benefit from state-of-the-art AQM algorithms. To this end, we provide an open-source available implementation of CoDel, one of the most prominent queue management algorithms, in P4. The implementation of such AQMs in P4 data plane hardware enables a massive reduction of latency in many use cases, such as traffic shaping in ISP access networks.

Abstract: Compound TCP will play a central role in future home WiFi networks supporting Internet of Things (IoT) applications. Compound TCP was designed to be fair but can manifest throughput unfairness in infrastructure-based IEEE 802.11 networks when devices at different locations experience different wireless channel quality. In this paper, we develop a comprehensive analytical model for compound TCP over WiFi. Our model captures the flow and congestion control dynamics of multiple competing long-lived compound TCP connections as well as the medium access control layer dynamics (i.e., contention, collisions, and retransmissions) that arise from different signal-to-noise ratios (SNRs) perceived by the devices. Our model provides accurate estimates for TCP packet loss probabilities and steady-state throughputs for IoT devices with different SNRs. More importantly, we propose a simple adaptive control algorithm to achieve better fairness without compromising the aggregate throughput of the system. The proposed real-time algorithm monitors the access point queue, drives the system dynamics to the desired operating point which mitigates the adverse impacts of SNR differences, and accommodates the sporadically transmitting IoT sensors in the system.

Abstract: We consider an unobservable M/M/1 queue where customers are homogeneous with respect to service valuation and cost per unit time of waiting. It is well known that left to themselves, in equilibrium, customers join the queue at a rate higher than is socially optimal. Hence, regulation schemes, under which the resulting equilibrium joining rate coincides with the socially optimal one, should be considered. We suggest a classification of regulation schemes based on a few desired properties and use it to classify schemes from the existing literature. To the best of our knowledge, none of the existing schemes possesses all of the properties, and in this paper we suggest such a scheme. Its novelty is in assigning random priorities to customers, prior to their decision whether to join or balk. We also introduce variations of this regulation scheme as well as others that are also based on randomization. The e-companion is available at https://doi.org/10.1287/mnsc.2017.2728 . This paper was accepted by Gad Allon, operations management.

Abstract: The last several years has seen a renewed interest in smart queue management to curb excessive network queueing delay, as people have realised the prevalence of bufferbloat in real networks. However, for an effective deployment at today's last mile connections, an improved queueing algorithm is not enough in itself, as often the bottleneck queue is situated in legacy systems that cannot be upgraded. In addition, features such as per-user fairness and the ability to de-prioritise background traffic are often desirable in a home gateway.In this paper we present Common Applications Kept Enhanced (CAKE), a comprehensive network queue management system designed specifically for home Internet gateways. CAKE packs several compelling features into an integrated solution, thus easing deployment. These features include: bandwidth shaping with overhead compensation for various link layers; reasonable DiffServ handling; improved flow hashing with both per-flow and per-host queueing fairness; and filtering of TCP ACKs. Our evaluation shows that these features offer compelling advantages, and that CAKE has the potential to significantly improve performance of last-mile internet connections.

Abstract: In recent times, smart communities such as-smart grid, smart healthcare, and smart manufacturing units consists of large number of connected devices equipped with advanced processing and communication capabilities. The focus of these smart communities have shifted towards the use of intelligent processing and control for providing better quality of service (QoS) to the end user domain. To support this aspect, software defined networking (SDN) is being widely deployed in different domains such as-data center networks, fog/edge computing, smart grid, and vehicular networks. The variable requirements of different applications in smart communities make it necessary to deploy flexible and scalable SDN. The dynamic flow management capability of SDN has lots of potential that needs to be effectively explored in order to provide QoS guarantee for traffic generated from different smart applications. In this direction, in this paper, an ensembled scheme for QoS-aware traffic flow management in SDN is designed. The proposed scheme works in three phases: 1) a linear ordering scheme for dependency removal of the incoming packets is designed, 2) an application-specific traffic classification scheme is designed, and 3) a queue management scheme is designed for efficient scheduling of traffic flow. The proposed scheme is evaluated over an experimental setup. The results obtained shows that the proposed scheme behaves effectively with respect to different QoS parameters.

Abstract: Congestion stability and prevention is the main aim of any decent network traffic management scheme. The protocol that drives congestion control in IP networks, called The Transmission Control Protocol (TCP), is plagued by bandwidth underutilization when implemented on wireless networks. The management of network trunk queue size is the goal of this paper, along with its antecedent delay issues with Active Queue Management (AQM) algorithms that overcomes the bandwidth underutilization issue faced by TCP flows in wireless networks. Two adaptive TCP models, specifically built for wireless networks, were created. This made it possible to implement feedback control techniques in the chosen AQM design for queue management. The two AQM policies were created with the help of Model Predictive Control (MPC) and Self-Tuning Control (STC). A very thorough comparison was made between the developed AQM schemes and two Early Detection Controllers, namely, Random Early Detection (RED) and Fixed-Parameter Proportional Integral (PI). Under varying wireless network conditions, the results obtained from simulating the proposed queue management scheme in MATLAB® confirmed that the fixed PI and RED controllers were, by comparison, inferior in performance. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Abstract: We consider a two-node tandem queueing network in which the upstream queue is M/G/1 and each job reuses its upstream service requirement when moving to the downstream queue. Both servers employ the first-in-first-out policy. We investigate the amount of work in the second queue at certain embedded arrival time points, namely when the upstream queue has just emptied. We focus on the case of infinite-variance service times and obtain a heavy traffic process limit for the embedded Markov chain.

Abstract: The last several years has seen a renewed interest in smart queue management to curb excessive network queueing delay, as people have realised the prevalence of bufferbloat in real networks. However, for an effective deployment at today's last mile connections, an improved queueing algorithm is not enough in itself, as often the bottleneck queue is situated in legacy systems that cannot be upgraded. In addition, features such as per-user fairness and the ability to de-prioritise background traffic are often desirable in a home gateway. In this paper we present Common Applications Kept Enhanced (CAKE), a comprehensive network queue management system designed specifically for home Internet gateways. CAKE packs several compelling features into an integrated solution, thus easing deployment. These features include bandwidth shaping with overhead compensation for various link layers; reasonable DiffServ handling; improved flow hashing with both per-flow and per-host queueing fairness; and filtering of TCP ACKs. Our evaluation shows that these features offer compelling advantages, and that CAKE has the potential to significantly improve performance of last-mile internet connections.

Abstract: Energy consumption is a growing concern in cloud data centers because underutilization of servers results in significant wasted power. Thus, improving server utilization for optimal energy use is now an urgent issue. We propose an energy-saving strategy based on multi-server vacation queuing theory that switches servers between on and sleep in groups. The strategy incorporates both synchronous and asynchronous strategies. When the number of idle servers reaches to a given threshold, idle servers enter sleep mode synchronously as a group. Varying workloads cause groups of servers to sleep asynchronously. We model the data center with our strategy as an M/M/H vacation queuing system and construct a two-dimensional continuous-time Markov chain to formulate the queuing system. Using a powerful matrix-geometric method, we obtain the stationary probability distribution for the system states. We use results from theoretical and simulated experiments to estimate the performance of our approach. The results are valuable for studying the power-performance trade-off in cloud data centers.

Abstract: Hub locations are NP-hard problems used in transportation systems. In this paper, we focus on a single-allocation hub covering location problem considering a queue model in which the number of servers is a decision variable. We propose a model enhanced with a queue estimation component to determine the number and location of hubs and the number of servers in each hub, and to allocate non-hub to hub nodes according to network costs, including fixed costs for establishing each hub and server, transportation costs, and waiting costs. Moreover, we consider the capacity for a queuing system in any hub node. In addition, we present a metaheuristic algorithm based on particle swarm optimization as a solution method. To evaluate the quality of the results obtained by the proposed algorithm, we establish a tight lower bound for the proposed model. Genetic programming is used for lower bound calculation in the proposed method. The results showed better performance of the proposed lower bound compared to a lower bound obtained by a relaxed model. Finally, the computational results confirm that the proposed solution algorithm performs well in optimizing the model with a minimum gap from the calculated lower bound.

Abstract: Elastic Optical Networks (EONs) represent a new approach for dealing with the enormous traffic demand in core networks as they can offer bandwidth granularities closer to those requested by the user and hence improve spectral utilization. In current literature there is a lack of dynamic strategies for service degradation which is a possible measure to address problems related to network congestion and consists in reducing the amount of resources provided. Since services of different classes can be requested, we propose in this paper an online strategy for service degradation using proportional Quality of Service (QoS). Our proposed strategy aims at minimizing the number of blocked requests due to lack of resources while provides throughput and delay guarantees for provisioned lightpaths. Thus, in order to quantify the impact of the degradation on the lightpaths we modeled source-destination pairs in an EON as a queuing system working under the Generalized Processor Sharing (GPS) service discipline with admission control of Leaky Bucket policy. The obtained results show that the proposed algorithm can reduce the blocking probability and give network operators more control between different degraded service classes.

Abstract: Customers often get attracted by lucrative deals and discounts offered by firms. These attracted customers are termed as encouraged arrivals. Four multi-server Markovian queuing models with encouraged arrivals are developed in this paper. Model-1 deals with multi-server finite capacity queuing system with encouraged arrivals. Model-2 is an extension of model-1 with reneging. Model-2 is then extended with feedback in Model-3. Model-4 is a further extension of Model-3 with retention of reneged customers. The stationary system size probabilities are obtained recursively for each model and various underlying special cases are discussed.

Abstract: Waiting period is a global problem that almost everyone has to face, which causes a great waste of time for everyone. It is well known that all these waiting line problems critically restrict further development. The focus of this study is to deal with passengers' queue issues of the international airport terminals of Kerala. Queuing theory is a mathematical approach to the study of waiting period in queues. This study evaluates the effectiveness of multi-server queuing model. The multi server approach of modelling was adopted in this cram to develop a mathematical model to solve problem of queuing of air transport passengers at the international airports in Kerala. The airport in the aviation industry of the country faces problems of many passengers queuing for boarding, departure with different arrival rate due to non availability of state of the art logistics management mechanisms for predicting the nature and service demands of travellers. The passengers' average wait time for reaching the gate area measures system performance. A mathematical queuing model was developed in this study and comparisons are made using analysis of variance (ANOVA).

Abstract: We study a finite capacity Markovian queuing system with two heterogeneous servers, reverse balking, and reneging. The stationary system size probabilities are obtained by using iterative method. The measures of performance like expected system size, the average rate of reverse balking, and the average rate of reneging are obtained. Finally, the sensitivity analysis of the model is carried out.

Abstract: In this paper, we analyse a single server polling model with two queues. Customers arrive at the two queues according to two independent Poisson processes. There is a single server that serves both queues with generally distributed service times. The server spends an exponentially distributed amount of time in each queue. After the completion of this residing time, the server instantaneously switches to the other queue, i.e., there is no switch-over time. For this polling model we derive the steady-state marginal workload distribution, as well as heavy traffic and heavy tail asymptotic results. Furthermore, we also calculate the joint queue length distribution for the special case of exponentially distributed service times using singular perturbation analysis.

Abstract: The number of people entering or leaving a building is an essential piece of information that has a lot of practical applications in intelligent building, queue management, and customer service. Vision-based technologies are widely installed in real-time occupancy monitoring systems due to accuracy and reliability. However, monitoring occupancy through unprotected video may disclose privacy of innocent people. Therefore, protecting confidentiality and accurately counting the number of people in real-time scenarios is a severe challenge. Encrypting such videos is one of the promising solutions for maintaining privacy. In this paper, we propose a real-time occupancy monitoring system with Region of Interest (ROI) based light-weight video encryption. People movement is detected through a widely used background model, i.e., Gaussian Mixture Model (GMM) and Kalman filter. Instead of encrypting the whole frame including background, the main idea is to encrypt people present in video via Tangent Delay Ellipse Reflecting Cavity Map System (TD-ERCS). Compared to existing schemes which are mainly based on complete encryption, the proposed method provides partial encryption though cryptographically secure and low-cost computation. The proposed scheme is tested with several different parameters such as correlation, entropy, contrast, energy, Number of Pixel Change Rate (NPCR) NPCR, Unified Average Change Intensity (UACI) and key space. Results from all security parameters have highlighted sufficient security of the proposed scheme.

Abstract: In this paper, we consider a real-time queuing system with rewards and deadlines. We assume that the packet processing time is known upon arrival, as is the case in communication networks. This assumption allows us to demonstrate that the well-known earliest-deadline-first policy performance can be improved. We then propose a scheduling policy that provides excellent results for packets with rewards and deadlines. We prove that the policy is optimal under deterministic service time and binomial reward distribution. In the more general case, we prove that the policy processes the maximal number of packets while collecting rewards higher than the expected reward. We present simulation results that show its high performance in more generic cases compared to the most commonly used scheduling policies.

Abstract: The paper considers both the basic mechanisms of queue management on the router and the principle of their operation. A comparative analysis of the following queuing mechanisms: FIFO (First-In First-Out), CQ (Custom Queuing), PQ (Priority Queuing), WFQ (Weighted Fair Queuing), CBWFQ (Class Based Weighted Fair Queuing) and LLQ (Low Latency Queuing) based on a number of parameters such as traffic volume, traffic type, service of mixed traffic, enable on router and number of queues was made. The results of analysis showed that each of the queuing mechanisms has its advantages when dealing with certain types of traffic. Whereas in the process of load balancing several interfaces of the router are involved simultaneously the choice of queuing mechanism on each of them will depend on QoS for real-time traffic. Thus in this work the effect of various queue management mechanisms (such as FIFO, PQ, CQ, WFQ, LLQ) to QoS for real-time traffic in the process of load balancing on the router in an IP network was also investigated. The simulation results showed that most of queuing mechanisms (except FIFO and CQ) in the process of load balancing allowed to provide high QoS for real time traffic by reducing the values of delays and jitter and a significant reduction of lost packets for such traffic flows. At the same time the results showed that in the load balancing process, the most efficient queues servicing mechanism is LLQ, which provides the lowest delay for VoIP and the end to-end delay commensurable with the PQ mechanism for video traffic.

Abstract: An Airport-Sector Network Delays model is developed in this paper for flight delay estimation within air transport network. This model takes both airports and airspace capacities into account by iterating among its three main components: a queuing engine, which treats each airport in the network as a queuing system and is used to compute delays at individual airport, a Link Transmission Model, which computes delays at individual sector and transmits all air delays into ground delays, and a delay propagation algorithm that updates flight itineraries and demand rates at each airport on the basis of the local delays computed by the queuing engine and flow control delays computed by the Link Transmission Model. The model has been implemented to a network consisting of the 21 busiest airports in China and 2962 links that represent to 151 enroute control sectors in mainland China, and its performance is evaluated by comparing with the actual delay data and results of Airport Network Delays model. It is found that the proposed model is well-suited for simulating delays in air transport system where either airports or airspace could be the bottleneck of the system.

Abstract: A single clock-regulated queue is fed by two Bernoulli streams of messages. The distributions of queue length, waiting time, and output for such a system are obtained. This is generalized as well to the case of a queue of finite capacity.

Abstract: Microgrids enable a network of distributed energy generators to sustain energy needs off-the-grid. Microgrids can experience islanded operational mode, being this mode a time sensitive event that affects costs of power generation and distribution. The detection of time-sensitive events is important because the control unit needs to be aware of changes in the grid to avoid losses in power quality and costs. This requires a quality of service (QoS)-aware data aggregation and queuing mechanism in the core of the network infrastructure to convey microgrid data to a central server (considered as a macro base station). This paper investigates the impact of time sensitivity-based microgrid data aggregation on message delivery under different priority and time-sensitivity levels. Hence, we propose a framework to cluster the electrical data based on the time sensitivity criteria using unsupervised machine learning. We introduce a multi-class queuing system in the pico-cells to ensure that clustering reduces the processing time for high priority data. The results show that the proposed approach significantly reduces the delivery delay of messages carrying time sensitive events from the microgrid.

Abstract: In Patient Queue Management System, we are going to introduce a new token system which can successfully reduce waiting time of the patients in the hospitals. The main aim is to implement a model that initialize alert notification via SMS. It will minimize the queue of patients in the waiting area of hospital and also patient can book an appointment from anywhere at anytime. Patients can book an appointment via android application and accordingly patients can select a doctor with a particular specialization and also will provide navigation towards that nearest hospital.

Abstract: The invention provides a queuing method and a queuing system, which are used for the queuing of users which dial a customer service hotline. The invention belongs to the technical field of communications, and can solve a problem that a conventional queuing method cannot give comprehensive consideration of the conditions of the users. The method comprises the steps: obtaining the queuing information of a current user according to the received call request of the current user, wherein the queuing information comprises the identity level, business level and waiting time level of the current user;determining the priority of the current user according to the identity level, business level and waiting time level of the current user through a first preset algorithm; adding the current user to anoriginal queue according to the priority of the current user and the priority information of the users in the original queue, and obtaining a current queue.

Abstract: The flow-based mathematical model of queue management on routers of telecommunication networks on the basis of optimal aggregation of flows and bandwidth allocation in queues has been further developed. The novelty of the model is that when flows are queued, they are aggregated based on the comparison of the classes of flows and queues in the course of analyzing the set of classification characteristics. Moreover, the result of calculating the percentage of unused queues in the course of optimal aggregation of flows provided assuming the hypothesis of a uniform or normal distribution of flow service classes within the framework of the model under consideration is presented. Applying the uniform distribution law, it was possible to reduce the number of unused queues by 20%, and by 30% for the normal distribution. Research results confirmed the efficiency of the proposed model.