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By interacting with the environment through trial and error, a deep reinforcement learning (DRL) -based controller is able to learn the patterns underlying a highly dynamic environment, paving the way for self-optimized decision making. Leveraging this capability, we propose a Deep Q-learning (DQL) -based algorithm to perform joint service function chain (SFC) integration and routing tasks for service requests, each with specific stringent end-to-end delay and bandwidth constraints in a cloud-based distributed Network Function Virtualization (NFV).Our design goal is to maximize the number of dynamically provisioned service requests in the network over a time horizon, thus indirectly contributing to maximizing network throughput.Numerical results show that the trained DQL-based algorithm achieves over 95% of the average request acceptance rate and over 96% of the offered load. This performance result is comparable to that of a Resource-Constrained Shortest Path algorithm while achieving a much shorter execution time, approximately 10 times faster.

A connection between open and closed many-server queueing systems is examined. Two limits are considered: (i) the number and reliability of machines (customers) increase simultaneously while the offered load remains constant (Poisson limit), and (ii) the number of machines (customers) and repairmen (servers) increase while the utilization remains close to unity (QED limit). It is argued that the two limits are interchangeable.

Reducing the number of users serviced by congested cellular towers given an offered load and a minimum level of acceptable user quality is a major challenge in the operation of LTE networks. In this paper, we utilize a supervised Deep Learning (DL) technique to predict the LTE and LTE-A loading of connected users and then dynamically predict the congestion threshold of each cellular tower under offered load. We then use the predicted congestion thresholds together with quality constraints to fine-tune cellular network operating parameters leading to minimizing overall network congestion. We propose two sets of optimization algorithms to solve our formulated congestion optimization problem. Those are, namely, a variant of Simulated Annealing (SA) algorithm to which we refer as Block Coordinated Descent Simulated Annealing (BCDSA) and Genetic Algorithm (GA). We first compare the performance of integrated DL-BCDSA and DL-GA algorithms and then show that our integrated DL-BCDSA can outperform existing state-of-the-art commercial self organizing tool already deployed in actual cellular networks.

We consider a multi-class multi-server retrial queueing system. Customers of each class arrive from outside the system according to a Poisson process. The service times for each class of customers are assumed to be exponentially distributed with service rates depending on both the customers’ class and the servers. We define the offered load and provide stability and instability conditions for this retrial queueing system. The stability result can be obtained by introducing artificial primitive processes and using the fluid limit approach.

In the modern data networks, the demand is increasing for interruption free, high speed, and best quality of services to perform successful communications. This paper emphasizes on the high priority (HP) traffic like video conferencing, video streaming, telemedicine, teleconferencing, and interactive gaming for the real time communications. To achieve the best quality data communication for the HP traffic, in this paper, we propose a new dynamic bandwidth allocation (DBA) algorithm called dynamic hybrid slot-size bandwidth allocation (DHSSBA) algorithm, which is the modified version of existing hybrid slot-size/rate (HSSR) algorithm. The performance of the proposed scheme has been analyzed by using numerical simulations in terms of the end-to-end packet delay, absolute delay variation, jitter, and throughput for both the HP and best effort (BE) traffic. We have also compared the simulation results of the proposed scheme with those of the existing modified HSSR (MHSSR), delay variation guaranteed protocol (DVGP), and HSSR schemes. From the comparison of the simulation results, at the highest offered load of 1.0, it can be seen that for the HP traffic the proposed scheme provides 60%, 50%, and 20% less average end-to-end delay than the existing HSSR, DVGP, and MHSSR schemes, respectively. At the highest offered load of 1.0, the proposed scheme also provides about 35% higher throughput than both the HSSR and DVGP schemes with 45% and 35% less jitter variation than the HSSR and DVGP schemes, respectively.

Wirelessly connected sensors are increasingly used in critical applications, e.g. firefighters or drone rescuing team, raising the need to quantify performance guarantees. In this work, we characterize the end-to-end delay on a Mobile Wireless Sensor Network (MWSN) with an analytical method based on Stochastic Network Calculus (SNC) with Moment Generating Functions (MGFs). The particularity of the studied scenario is that the network is composed of two segments: one with multiple nodes connected by a contention-based channel using the Distributed Coordination Function (DCF), and another consisting of a link prone to disconnection due to the mobility pattern of the nodes in the first segment. Numerical performance bounds are provided for an example application in which the effects of per-node offered load, scheduling algorithms and connectivity on the link to the sink are quantified for different network sizes.

Autonomous vehicles are in full development and vehicles classification is a fundamental part of this new technology in this years. The AVs is very is important solution in smart cities. The electric vehicle EV is one of the solutions recommended by the vehicle manufacturers and research organizations to reduce noise pollution, fuel consumption, real time and time execution for tasks in networking EV. The transport and mobility sector has for more than a decade seen a fundamental change in its organization due to a double technological revolution and use a new forms of mobility. This motion has profound consequences on the relationship that citizens maintain with mobility. It also proposes a physical platform able to perform a form of platooning using Artificial Intelligence (AI) with scheduler to create platoons with miniature vehicle. Platform called Autonomous Learning Intelligent Vehicles is the association of multiple physical cars coupled with an infrastructure, which handles the data of every single car and makes decisions based on the augmented environmental. In this solution, we present tracking and detection of EVs in parking or in station. So, we change communications EVs, giving priority to charging demands over other types of sms lower priority messages. Finally, we propose an efficient admission control mechanism to manage EVs traffic and to provide quality of Service to charging demand messages in terms of strict delay to avoid both a long latency of EV users and a network overload in high offered load conditions.

In this paper, we propose a novel methodology for estimating the worst-case spectral efficiency (b/s/Hz) of end-to-end paths across a Multi-Core Fiber (MCF)-enabled Flex-Grid optical network, accounting for physical link and intra-node noise, together with the inter-core crosstalk impairment appearing during the transmission of optical signals over MCFs. In particular, we employ the proposed methodology to quantify the benefits of Probabilistic Constellation Shaping (PCS) versus traditional polarization-multiplexed modulation formats in two reference backbone networks. From the obtained results, PCS yields admissible offered load gains close to 13% under 1% bandwidth blocking probability. This work has been supported by the Spanish Government through project ALLIANCE (TEC2017-90034-C2-1-R and TEC2017-90034-C2-2-R) with FEDER contribution. Moreover, it has been supported by the Spanish Thematic Network under contract RED2018-102585-T (Go2Edge).

We propose a two-stage pipelined parallel matching scheduler for alleviated scheduling time constraint. TPPM with pGA provides ~100% throughput and maximum ~42% less queuing delay when offered load is above 0.6 than single DRRM scheduler.

In this article, we develop a probabilistic framework for analyzing coded random access. Our framework is based on a new abstract receiver (decoder), called a Poisson receiver, that is characterized by a success probability function of a tagged packet subject to a Poisson offered load. We show that various coded slotted ALOHA (CSA) systems are Poisson receivers. Moreover, Poisson receivers have two elegant closure properties: (i) Poisson receivers with packet routing are still Poisson receivers, and (ii) Poisson receivers with packet coding are still Poisson receivers. These two closure properties enable us to use smaller Poisson receivers as building blocks for analyzing a larger Poisson receiver. As such, we can analyze complicated systems that are not possible by the classical tree evaluation method. In particular, for CSA systems with both spatial diversity and temporal diversity, we can use the framework of Poisson receivers to compute the exact (asymptotic) throughput. We demonstrate that our framework can be used to provide differentiated services between ultra-reliable low-latency communication (URLLC) traffic and enhanced mobile broadband (eMBB) traffic. By conducting extensive simulations, we also verify that our theoretical results match extremely well with the simulation results.

Existing approaches to dynamic scaling of streaming applications often fail to incorporate uncertainty arising from performance variability of shared computing infrastructures, and rapid changes in offered load. We explore the definition and incorporation of risk and uncertainty, and advocate for risk-adjusted measures of performance and their application in improving the robustness of autonomic scaling of streaming systems.

In this study, we investigated the effects of combining a zigzag decoder (ZD) with a coded ALOHA using the successive interference cancellation (SIC) technique to retrieve packets from collisions. We proposed zigzag decodable coded slotted ALOHA (ZDCSA) and enhanced-ZDCSA (E-ZDCSA) as a scheme which applies ZD before and after SIC, respectively. Furthermore, we derived the asymptotic analysis for the throughput and packet loss rate (PLR) performances of E-ZDCSA and validated its accuracy with the Monte Carlo simulations. Through numerical and asymptotic analyses, we showed that E-ZDCSA outperforms ZDCSA and the conventional coded ALOHA schemes in terms of throughput and PLR performances in most of the offered load regime. Moreover, we demonstrated that our proposed schemes outperform the conventional protocol in a practical scenario in which ZD cannot resolve all the collisions.

The article proposes a new method of determining traffic characteristics of multiservice overflow systems that carry adaptive traffic. When the total offered load in primary resources exceeds a certain value, this type of traffic is admitted for service with lower bitrate. A particular attention is given in the article to a method for a determination of the parameters of traffic that overflows to secondary resources as well as to the way adaptive traffic is serviced. The method takes into consideration three possible types of traffic: Erlang, Engset, and Pascal traffic. It is based on a generalization of Hayward’s concept and its application to model systems with adaptive traffic with threshold compression. The method can be used for optimal dimensioning of logical networks (slices) in modern mobile systems due to possibility of analytical determination of grade of service parameters (blocking probability, carried traffic, and network load). To verify the accuracy of the proposed model the results of analytical calculations, obtained on the basis of the proposed model, are then compared with the results of simulation experiments for a number of selected structures of overflow systems that service adaptive traffic. The results of the study demonstrate high accuracy of the proposed theoretical model.

How would the growing prevalence of real-time delay information affect a service system? We consider a single-server queueing system where customers arrive according to a Poisson process and the service time follows an exponential distribution. There are two streams of customers, one informed about real-time delay and the other uninformed. The customers’ uninformed behavior may be due to information ignorance or rational behavior in the presence of an information fee. We characterize the equilibrium behavior of customers with information heterogeneity and investigate how the presence of a larger fraction of informed customers affects the system performance measures, i.e., throughput and social welfare. We show that the effects of growing information prevalence on system performance measures are determined by the equilibrium joining behavior of uninformed customers. Perhaps surprisingly, we find that throughput and social welfare can be unimodal in the fraction of informed customers. In other words, some amount of information heterogeneity in the population can lead to more efficient outcomes, in terms of the system throughput or social welfare, than information homogeneity. For example, under a very mild condition, throughput in a system with an offered load of 1 will always suffer if there are more than 58% of informed customers in the population. Moreover, it is shown that for an overloaded system with offered load sufficiently higher than 1, social welfare always reaches its maximum when some fraction of customers is uninformed of the congestion level in real time. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2747 . This paper was accepted by Gad Allon, operations management.

It is an effective approach for comprehending network performance is to develop a mathematical model because complex relationship between system parameters and performance can be obtained explicitly. This paper presents generalized analytical expressions for end-to-end throughput of IEEE 802.11 string-topology multi-hop networks. For obtaining expressions, a relationship between the durations of the backoff-timer (BT) decrements and frame transmission is expressed by integrating modified Bianchi’s Markov-chain model and airtime expression. Additionally, the buffer queueing of each node is expressed by applying the queueing theory. The analytical expressions obtained in this paper provide end-to-end throughput for any hop number, any frame length, and any offered load, including most of analytical expressions presented in previous papers. The analytical results agree with simulation results quantitatively, which shows the verifications of the analytical expressions.

We consider a batch arrival M X ∕ G ∕ 1 queue with impatient customers. The loss probability is expressed in terms of the stationary waiting time distribution for the standard M X ∕ G ∕ 1 queue with no impatience. But this expression is only applicable when the offered load ρ is less than 1. We give a formula for the loss probability applicable for any values of ρ > 0 , by proving that the loss probability is analytic in ρ on ( 0 , ∞ ) through a Girsanov-type change of measure.

We consider a queueing system with heterogeneous customers. One class of customers is eager; these customers are impatient and leave the system if service does not commence immediately upon arrival. Customers of the second class are tolerant; these customers have larger service requirements and can wait for service. In this paper, we establish pseudo-conservation laws relating the performance of the eager class (measured in terms of the long run fraction of customers blocked) and the tolerant class (measured in terms of the steady state performance, e.g., sojourn time, number in the system, workload) under a certain partial fluid limit. This fluid limit involves scaling the arrival rate as well as the service rate of the eager class proportionately to infinity, such that the offered load corresponding to the eager class remains constant. The workload of the tolerant class remains unscaled. Interestingly, our pseudo-conservation laws hold for a broad class of admission control and scheduling policies. This means that under the aforementioned fluid limit, the performance of the tolerant class depends only on the blocking probability of the eager class, and not on the specific admission control policy that produced that blocking probability. Our psuedo-conservation laws also characterize the achievable region for our system, which captures the space of feasible tradeoffs between the performance experienced by the two classes. We also provide two families of complete scheduling policies, which span the achievable region over their parameter space. Finally, we show that our pseudo-conservation laws also apply in several scenarios where eager customers have a limited waiting area and exhibit balking and/or reneging behaviour.

The massive Internet of Things (IoT) scenario refers to a huge number of Machine Type Communications (MTC) characterized by sporadic transmissions of small-sized packets. In order to manage the uplink radio resource allocation in cellular networks, several access schemes have been proposed. These schemes are mainly based on a grant-based Random Access (RA) procedure and proper load-aware access controls, e.g., Access Class Barring (ACB) techniques, dynamic uplink radio resource allocation, and so on. The development of an efficient approach to estimate the traffic load is extremely important for the proper functioning of these access schemes. With the ever-increasing number of transmitting MTC devices, expected with Beyond 5G (B5G) and 6G networks, additional challenges to obtain a correct real-time traffic load estimation are posed. Deep learning techniques, in this context, offer learning ability and optimization capability to properly support this scenario. In this paper, we propose a current access attempts estimation, based on Deep Neural Network (DNN), which accepts as input only the information really available at the next generation Node B (gNB). The network was trained and tested with a dataset properly created and composed by more than 21 million points. The DNN-based traffic load estimation method is then compared with other benchmark schemes available in the literature, in terms of regression accuracy, both in a static analysis, which considers a stand-alone RA cycle, and through a long-term analysis with a time-varying offered load. The latter analysis was performed both by adopting a theoretical arrival process proposed by 3GPP, and by using traces of real traffic data.

Voice over Internet Protocol (VoIP) uses packet switching to transmit voice, video, data and chat services. VoIP applications depend upon various speech codecs which vary in speech quality, bandwidth and computational requirements. The key challenges for improving the VoIP Quality of Service (QoS) and Quality of Experience (QoE) is to use best codec by considering network traffic and selecting the optimal hardware. Hardware Calibration is a mechanism to aid service providers by selecting a suitable hardware for Call Manager to transmit different real-time based applications. The paper focuses on proposing a computational model which suggests the most suitable hardware for a specific load handling requirement. The selection of processor is done by taking into consideration the effect of different codecs when it takes calls per second as input and gives output as whichever hardware is recommended for Call Manager to handle different types of applications like Peer-To-Peer (P2P) application, Back-To-Back (BTB) application, enabling voice logging, etc. The emulations demonstrate that the proposed model is suitable for selection of hardware which provides better QoS and QoE for the transmission VoIP based real-time applications. Further, a multi-criteria decision-making method based on Analytic Hierarchy Process (AHP) is also proposed to decide which hardware can be chosen as Call Manager. The results obtained from AHP are used to validate the outcome of the proposed computational model. The computational model is applicable only when the tests are run on particular systems and it suggests the best hardware among them for the particular offered load. However, to suggest the best configuration corresponding to the input load from the possible configurations available in the market, benchmarking is performed. A common scale model for hardware benchmark estimation particularly has been proposed for different scenarios.

Long queues in emergency departments (EDs) lead to overcrowding, a phenomenon that can potentially compromise patient care when medical interventions are delayed. There are several causes of this problem, one of which is inadequate resource allocation. In this paper, we propose using a modified version of the square root staffing (SRS) rule to satisfy the probability of delay target. We use the concepts of kinetics and biological modeling to approximate the fluid behavior of the queueing process. We are then able to estimate the offered load and the appropriate service grade necessary to construct a staffing policy that meets the target. Additionally, we show how to utilize Pontryagin’s maximum principle to find the optimal number of providers that minimizes delay and staffing costs. Finally, we demonstrate the implementation of our model using data from a hospital in upstate New York.

This paper proposes a transceiver system for automatic tracking and dynamic routing for free space optical (FSO) communication. The proposed transceiver architecture has M transmitters and M receivers in a M×M configuration that has the capability to dynamically orient themselves at different angles to establish a link between two nodes. It also addresses the problem of optimizing and designing logical topologies for FSO communications using tracking transceivers. Whereas the physical topology consists of nodes, transmitters and receivers that can orient themselves in different directions, the logical topology consists of a set of lightpaths and the set of nodes originating or terminating the lightpaths. A lightpath in a logical topology is a combination of one or more physical links using transmitters and receivers with the same wavelength. For a given physical topology and traffic pattern, the main challenge is to establish an optimal logical topology so as to minimize congestion. We formulate a mixed integer linear programming (MILP) methodology for constructing logical topologies in FSO networks with tracking transceivers. The main objective of the mathematical formulation is to minimize the maximum offered load on any physical link so as to minimize congestion in the network. Given a fixed number of resources, in comparison to a fixed FSO architecture, the proposed architecture reduces the blocking probability further as well as increases the percentage recovery of traffic affected by a single link failure.

Despite the high throughput and low complexity achieved by input scheduling based on Birkhoff-von-Neumann (BvN) decomposition, the performance of the BvN switch becomes less predictable when the input traffic is bursty. In this paper, we propose a deflection-compensated BvN (D-BvN) switch architecture to enhance the quasistatic scheduling based on BvN decomposition. D-BvN switches provide capacity guarantee for virtual circuits (VCs) and deflect bursty traffic when overflow occurs. The deflection scheme is devised to offset the excessive buffer requirement of each VC when input traffic is bursty. The design of our conditional deflection mechanism is based on the fact that it is unlikely that the traffic input to VCs is all bursty at the same time; most likely, some starving VCs have spare capacities when some other VCs are in the overflow state. The proposed algorithm makes full use of the spare capacities of those starving VCs to deflect the overflow traffic to other inputs and provide bandwidth for the deflected traffic to re-access the desired VC. Our analysis and simulation results show that this deflection-compensated mechanism can support BvN switches to achieve close to 100% throughput of offered load even with bursty input traffic, and reduces the average end-to-end delay and delay jitter. Also, our result indicates that the packet out-of-sequence probability due to deflection of overflow traffic is negligible, and thus, only a small re-sequencing buffer is needed at each output port. We also compare D-BvN with the well-established online scheduling algorithm iSLIP, and the result demonstrates that D-BvN outperforms iSLIP in terms of the throughput of offered load when the traffic is non-uniform or the traffic load is not very high.

Multiple sources submit updates to a monitor through an M/M/1 queue. A stochastic hybrid system (SHS) approach is used to derive the average age of information (AoI) for an individual source as a function of the offered load of that source and the competing update traffic offered by other sources. This work corrects an error in a prior analysis. By numerical evaluation, this error is observed to be small and qualitatively insignificant.

Sensor sources submit updates to a monitor through an unslotted, uncoordinated, unreliable multiple access collision channel. The channel is unreliable; a collision-free transmission is received successfully at the monitor with some transmission success probability. For an infinite-user model in which the sensors collectively transmit updates as a Poisson process and each update has an independent exponential transmission time, a stochastic hybrid system (SHS) approach is used to derive the average age of information (AoI) as a function of the offered load and the transmission success probability. The analysis is then extended to evaluate the individual age of a selected source. When the number of sources and update transmission rate grow large in fixed proportion, the limiting asymptotic individual age is shown to provide an accurate individual age approximation for a small number of sources., This has been submitted to ISIT 2020

Web growth has been driven by a number of techniques. In massive scale processing, cloud computing is the rising technology. It is a method that consists of varied software and Web-enabled services. As cloud computing grows speedily and purchasers are stringent a lot of services, the traffic on the cloud increases tremendously. Thus, managing load balance is very interesting and important research area nowadays. A decent load balancing algorithm can enhance the performance and resource utilization by dynamically distributing workload among a range of nodes within the system. This paper addresses a comprehensive summary on load balancing algorithms, challenges, and various presently offered load balancing software and its features.

The basics of the traffic theory, as related to communication protocols, are presented; the traffic intensity, offered load, throughput and delay are defined. The usual performance measures the protocol efficiency and the delay are defined and explained. Additionally, the relative efficiency of a protocol is defined and described. While the protocol efficiency can usefully be employed to assess the protocol performance in a nonmultiplexed communication system, the relative efficiency is shown to be an especially useful measure to assess the performance of protocols in multiplexed systems. The discussion of the relative efficiency is a special feature of this book; this performance measure was proposed by the author of this book and is not presented in the other books on protocols and their performance. The performance of a protocol stack is also discussed and the nominal transfer rate of a whole communication system is assessed. There are six figures in this chapter.

Slotted ALOHA is known to have poor channel utilization (a maximum of 37% when average offered load is one packet per time slot). Reinforcement learning has recently been proposed as a technique that allows nodes to learn to coordinate their transmissions in order to attain much higher network utilization. All reinforcement learning schemes proposed to date assume immediate feedback on the outcome of a packet transmission. We introduce ALOHA-dQT, a reinforcement-learning protocol that achieves high utilization by having nodes broadcast short summaries of the channel history as known to them along with their packets. Our simulation results show that ALOHA-dQT leads to network utilization above 75%, with fair bandwidth allocation among nodes. ALOHA-dQT is the first reinforcement-learning approach applied to slotted ALOHA suitable for ad-hoc networks without centralized repeaters.

Problem Definition: We determine the jointly optimal service capacity and retrial intervals between unsuccessful service attempts for a major provider of cloud computing services. Allocating sufficient capacity to cloud services is a challenging task because demand is time-varying. Thus, most firms have been expanding their capacity with little regard to the consequences associated with idle resources, such as excessive energy consumption and excess costs. Academic/Practical Relevance: We model the system as a multi-station queueing network where the arrival rate of jobs is time-varying and the servers represent CPU cores. Jobs are infinitely impatient and those that are not immediately serviced may retry several times before permanently abandoning the system. We introduce an offered load approximation that allows us to construct a recursive representation of the offered load function which describes the fluid dynamics of the system. Methodology: We develop a calculus-of-variation approach to minimize the total functional variation of the constructed offered load function. We show that an optimal policy can be efficiently obtained and prove that it is similar to maximizing the penalized system throughput. Results: Using a data set of cloud computing requests over a representative 24-hour period from a typical service of our partner organization, we show that our optimal policy results in a 10% reduction in capacity. We also demonstrate that small changes to their service-level agreements may elicit additional savings. Managerial Implications: Our model can help reduce idle capacity and has implications for managing more sustainable and environmentally friendly cloud computing services. It may also help to explain why so much global cloud capacity is typically idle. That is, in order to satisfy service level agreements encouraging retrial jobs to be processed during off-peak periods while also ensuring that they have short wait times, providers must provision large amounts of capacity.

While 3GPP has been developing NB-IoT, the market of Low Power Wide Area Networks has been mastered by cheap and simple Sigfox and LoRa/LoRaWAN technologies. Being positioned as having an open standard, LoRaWAN has attracted also much interest from the research community. Specifically, many papers address the efficiency of its PHY layer. However MAC is still underinvestigated. Existing studies of LoRaWAN do not take into account the acknowledgement and retransmission policy, which may lead to incorrect results. In this paper, we carefully take into account the peculiarities of LoRaWAN transmission retries and show that it is the weakest issue of this technology, which significantly increases failure probability for retries. The main contribution of the paper is a mathematical model which accurately estimates how packet error rate depends on the offered load. In contrast to other papers, which evaluate LoRaWAN capacity just as the maximal throughput, our model can be used to find the maximal load, which allows reliable packet delivery.

As the Internet of Things (IoT)/machine-to-machine market is rapidly growing, a special role is expected to be played by satellite communications in that they offer ubiquitous coverage and therefore enable typical monitoring, telemetry, and control services also in areas with a poor terrestrial infrastructure. In this respect, the case of massive IoT devices deployment calls for random access solutions, which have been long analyzed by the scientific satellite community in the last ten years. This article further elaborates on the relation between the normalized offered load and the achievable performance in terms of packet loss rate, which was not much addressed so far at high loads. The proposed theoretical framework has been validated through extensive simulation campaigns, which show an excellent match at different loads and number of interfering packets configurations, by significantly improving the results achievable through other existing theoretical frameworks.

Traffic in optical backbone networks is evolving rapidly in terms of type, volume, and dynamicity following the rapid growth of cloud-based services, ongoing adoption of 5G communications, and explosion of the Internet of Things (IoT). The elastic optical network (EON), by adopting a flexible grid, can provide the required capacity and flexibility to handle these rapid changes. However, operators rarely perform greenfield deployments, so to limit upfront investment, a gradual migration from fixed-grid to flexible-grid switching equipment is preferable. For gradual migration, switching nodes can be upgraded (starting from bottleneck network links) while keeping the rest of the traditional fixed-grid network operational. We refer to the coexistence of fixed-grid and flex-grid optical equipment as a “mixed-grid” network. Traditional algorithms for dynamic resource assignment in EONs will not effectively be applicable in a mixed-grid network due to interoperability issues among fixed- and flex-grid nodes. In this study, we propose a new algorithm, called Mixed-grid-aware Dynamic Resource Allocation, to solve the route, spectrum, and modulation-format allocation problem in a mixed-grid network while considering interoperability constraints. Our numerical results (on representative network topologies) show that the proposed method achieves 50% less blocking (for 50% offered load) compared to the traditional approach.

Latency-sensitive edge-native applications may be the key to commercial success of edge infrastructure. However, success in the form of widespread deployment of such applications poses its own challenges. These applications are edge-dependent by definition, and therefore cannot simply fail over to the cloud if the edge is overloaded. In this paper, we propose an adaptation-based strategy to allow scaling up the number of concurrent edge-native applications on a resource-limited cloudlet and wireless network. We demonstrate up to 40% reduction in offered load with minimal impact on latency on a variety of cognitive assistance tasks over non-adaptive approaches. Our approach is able to gracefully degrade and maintain quality of service for a subset of applications in the face of severely loaded conditions.

Wireless full-duplex (FD) communication allows a node to send and receive data frames simultaneously within the same frequency channel. Generally, FD transmission is effective when a pair of FD transmission nodes have the same data-transmission duration. The difference in the time duration of the frames transmitted by a pair of the nodes wastes the channel resources where more data could be transmitted. The wasted time duration decreases the system throughput in networks. To adjust the data-frame transmission duration in a pair of FD nodes, previous work has proposed the application of frame aggregation technique to FD communication. However, the work has assumed the network is in saturated condition where each node has enough number of data-frames in its queue. In real networks, each node does not always have enough data frames. For applying the frame aggregation to FD networks, therefore, it is necessary to consider the networks under non-saturated condition. This paper proposes throughput and delay analysis for FD WLANs with frame aggregation. As the contributions, this paper provides the analytical expressions of FD networks under non-saturated condition. From the proposed model, the appropriate aggregation size for both the network throughput and the delay can be derived for each offered load, packet payload size, and the number of nodes in the networks.

We show that the IEEE 802.11 distributed coordination function (DCF) wireless LAN (WLAN) exhibits bistable behavior when the total load provided to a WLAN by wireless stations is almost equal to the WLAN capacity. In one metastable equilibrium state, each station has no or only a few frames awaiting transmission in the butter and the throughput is equal to the offered load. In the other metastable equilibrium state, each station has a long queue of frames awaiting transmission and the throughput is less than the offered load. In the present study, we simulated this bistable behavior and confirmed it with an analysis based on the M/PH/1/K queueing model.

Contention at the storage nodes is the main cause of long and variable data access times in distributed storage systems. Offered load on the system must be balanced across the storage nodes in order to minimize contention, and load balancing should be robust against the skews and fluctuations in content popularities. Data objects are replicated across multiple nodes in practice to allow for load balancing. However redundancy increases the storage requirement and should be used efficiently. We evaluate load balancing performance of natural storage schemes in which each data object is stored at d different nodes and each node stores the same number of objects. We find that load balance in a system of n nodes improves multiplicatively with d as long as d = o (log(n)), and improves exponentially as soon as d = Θ(log(n)). We show that load balance improves the same way with d when the service choices are created with XORs of r objects rather than object replicas, which also reduces the storage overhead multiplicatively by r. However, unlike accessing an object replica, access through a recovery set composed by an XOR’ed copy requires downloading content from r nodes, which increases load imbalance additively by r.

The random access erasure collision channel captures, in an abstracted manner, several important features of a wireless environment shared by uncoordinated radios. The radios employ random access and, when contending, transmit over independent heterogeneous erasure channels with the common access point. The access point is capable of only receiving a single message at a time, and so any colliding messages are lost. The combined effects of the channel heterogeneity and the collision rule give rise to a natural question: how does the expected sum throughput vary with the subset of radios that are active? The subset of radios achieving the optimal throughput is found by a simple greedy packing procedure — add the radios, sorted by nonerasure probability, until a target offered load is exceeded.

We introduce the concept of "edge-native applications" that fully exploit the potential of edge computing and have a deeply symbiotic relationship with it. Such an application is custom-designed to take advantage of one or more of the unique attributes of edge computing such as (a) bandwidth scalability, (b) low-latency offload, (c) privacy-preserving denaturing, and (d) WAN-failure resiliency. The application may also contribute to scalability through adaptation to reduce offered load. We contrast edge-native applications with shallower uses of edge computing in a taxonomy that spans "edge-enhanced, device-native applications", "edge-accelerated, cloud-native applications", and "device-only applications". We close with a case study that illustrates these concepts in the context of cognitive assistance for automotive safety.

We study the relationship between capacity and performance for a service firm with spatial operations, in the sense that requests arrive with origin-destination pairs. An example of such a system is a ride-hailing platform in which each customer arrives in the system with the need to travel from an origin to a destination. We propose a parsimonious representation of a spatial multiserver system through a state-dependent queueing model that captures spatial frictions as well as spatial economies of scale through the service rate. In a classical [Formula: see text] queueing model, the square root safety (SRS) staffing rule is known to balance server utilization and customer wait times. By contrast, we find that the SRS rule does not lead to such a balance in spatial systems. In a spatial environment, pick-up times increase the load in the system; furthermore, they are an endogenous source of extra workload that leads the system to only operate efficiently if there is sufficient imbalance between supply and demand. In heavy traffic, we derive the mapping from load to operating regimes and establish implications on various metrics of interest. In particular, to obtain a balance of utilization and wait times, the service firm should use a higher safety factor, proportional to the offered load to the power of [Formula: see text]. We also discuss implications of these results for general systems.

We consider a single server queueing system with two classes of jobs: eager jobs with small sizes that require service to begin almost immediately upon arrival, and tolerant jobs with larger sizes that can wait for service. While blocking probability is the relevant performance metric for the eager class, the tolerant class seeks to minimize its mean sojourn time. In this article, we analyse the performance of each class under dynamic scheduling policies, where the scheduling of both classes depends on the instantaneous state of the system. This analysis is carried out under a certain fluid limit, where the arrival rate and service rate of the eager class are scaled to infinity, holding the offered load constant. Our performance characterizations reveal a (dynamic) pseudo-conservation law that ties the performance of both classes to the standalone blocking probabilities associated with the scheduling policies for the eager class. Furthermore, the performance is robust to other specifics of the scheduling policies. We also characterize the Pareto frontier of the achievable region of performance vectors under the same fluid limit, and identify a (two-parameter) class of Pareto-complete scheduling policies.

Understanding delay performance is essential for successful development and deployment of real-time networked sensing applications. In this work, we characterize the end-to-end delay on a multi-hop Wireless Sensor Network (WSN) with an analytical method based on Stochastic Network Calculus (SNC) with Moment Generating Functions (MGF). The particularity of the sensing scenario is that all nodes generate and cooperatively forward their traffic through the network to a main location, known as the sink. We tackled the problem by modeling the service process of fading wireless channels from a high-layer perspective with Finite-State Markov Chains (FSMC). Numerical performance bounds are provided for an example WSN with IEEE 802.11g ad-hoc links in which the effects of delay bound violation probability, per-node offered load, data rate and fading speed are quantified for different network sizes. Finally, the presented analysis is validated through a comparison between analytical and numerical simulation results.

The performance of Wireless Local Area Network (WLAN) is highly dependent on the processes that are implemented in the Medium Access Control (MAC) sublayer regulated by the IEEE 802.11 standard. In turn, various parameters affect the performance of the MAC sublayer, the most important of which is the number of stations in the network and the offered load. With the massive growth of multimedia traffic, research of the network performance depending on traffic types is relevant. In this paper, we present the impact of a high-/low-priority traffic ratio on WLAN performance with different numbers of access categories. The simulation results show different impact of high-/low-priority traffic ratio on the performance of the MAC sublayer of wireless LANs depending on different network-sizes and on network conditions. Performance of the large network with two access categories and with the prevalent high-priority traffic is significantly higher than in the case of using four categories on the MAC sublayer. This allows us to conclude that the performance improvement of the large network with the prevalent high-priority traffic can be achieved by an adaptive adjustment of the access categories number on the MAC sublayer.

Recently, high port-count optical packet switches, based on the Arrayed Waveguide Grating (AWG) device, have been proposed for providing the very high bandwidth and connectivity degree that will be required in future datacentre interconnection networks. A practical, cost-effective realisation of such a switch requires that over-subscription of the AWG cross-connect output ports be allowed, with the resulting packet contention being resolved using a packet buffering facility in the switch. In this paper, we propose an efficient AWG switch architecture of this type that uses a recirculating optical packet buffering scheme and we develop analytic modelling methods to estimate the switch packet blocking probability and packet buffering delay. The analytic model resolves internal traffic flows in the switch as Markov-modulated Poisson Processes (MMPP) and also allows modelling of offered load to the switch as a MMPP process. Taking this model of the switch, we first show its accuracy compared to discrete event simulations and then use it to develop a heuristic optimisation algorithm to dimension the switch hardware components, with the objective of minimising the operating cost by optimally dimensioning the main power consuming components, subject to constraints on the maximum contention probability and average and maximum propagation delays. The resulting optimised switch design demonstrates the proposed architecture to be a promising candidate for future high-port count datacentre switches.

Accurate prediction of traffic conditions on orthogonal frequency division multiple access passive optical networks is important because of its vital role in network resource management and efficient bandwidth allocation. Given the dynamic and stochastic nature of network traffic, our proposed algorithm conducts a probabilistic approach by using the hidden Markov model (HMM). The HMM defines traffic states with two parameters: the mean and contrast of the bandwidth request observations. Simulation results demonstrate the performance comparison between with and without the prediction method in terms of throughput and end-to-end delay. As a result, the throughput improves 15% and the saturation offered load of the delay for the prediction and non-prediction is 0.8 and 0.7, respectively.

This study aims to put forward an extensive discussion about the increasing demand for available bandwidth to serve the multiple types of traffic in modern wavelength division multiplexing (WDM) metropolitan area networks (MANs). A traffic-depended multi-buffer node architecture in conjunction with an efficient asynchronous transmission WDM access (WDMA) protocol to serve the variable size Internet packets in ring MANs is proposed. The structure of the multi-buffer node architecture is determined by the probability distribution of each packet size category in the MAN traffic, providing storage and dropping events equity among the nodes. The adopted WDMA algorithm satisfies the requirement for high performance efficiency especially under high offered load, by taking care to optimally face the bandwidth fragmentation problem and to maximize the bandwidth exploitation, while it effectively avoids both the packets collisions over the wavelengths and the destination conflicts. Numerical results prove that the proposed network model achieves throughput improvement up to 334% as compared with the relative study of Pranggono and Elmirghani (2011). An analytical framework is developed for the protocol throughput predictions under both symmetric and asymmetric IP traffic scenarios. Also, the proposed protocol performance is thoroughly investigated through simulation results based on Poisson and self-similar traffic model statistics, for both traffic scenarios.