Stochastic Network Calculus Research Articles

Federated Learning Empowered Computation Offloading and Resource Management in 6G-V2X

Humankind's urbanization and luxuriousneed increase the number of vehicles day by day. Alongside the advancement of technologies, autonomous vehicles have now come into a greater existence. Due to the resource constraints for obtaining a high processing rate in performing the intelligent tasks, Vehicular Edge Computing (VEC) has been introduced. However, a delay occurs in transferring the information due to the traits of automotive tasks. In this paper, a Federated Learning empowered computation Offloading and Resource management (FLOR) is proposed, which offloads and manages the resources with low latency. The FLOR framework operates over heterogeneous networks that include Vehicle-to-Everything (V2X) communication, Dedicated Short Range Communication (DSRC), 5G millimeter-wave (5G-mmWave), and 6G Vehicle-to-Vehicle (6G-V2V) communication. FLOR employs the stochastic network calculus mechanism for computing the upper bound delay of the heterogeneous communication and obtains the probability of offloading mechanism. Upon the detected probability value, the FLOR offloads the computation task to the optimal network. Further, to avoid the increase in resource cost and utilize the available resources efficiently, an effective Radio Resource Management (e-RRM) based on Federated Q-Learning is proposed in FLOR, for allocating and using the available resources optimally. The simulation result shows that the proposed FLOR framework effectively offloads the computation between the available heterogeneous networks with optimal resource allocation by 20.69% than the existing solutions.

Human-in-the-loop virtual reality offloading scheme in wireless 6G Terahertz networks

Upcoming sixth-generation networks have to be carefully designed to offer support to a wide range of human-centric applications, typically computationally intensive and heavily dependent on human perception. This paper proposes a human-in-the-loop-based resource allocation approach found on the joint use of matching theory and stochastic network calculus principles to efficiently address tasks offloading issues within a sixth-generation network environment. The considered matching game approach is characterized by externalities, whose stability is properly discussed here. In particular, by focusing on virtual reality services, the paper aims at maximizing the average service reliability in terms of ability in guaranteeing a proper quality of experience target, i.e., keeping the end-to-end delay below a suitable threshold, by the proper exploitation of the human cognitive limitations. Finally, the effectiveness of the proposed human-in-the-loop-based resource allocation approach is verified by proving comparisons with simulation results and conventional brain-agnostic strategies.

Comparison of Different Approaches for Solar PV and Storage Sizing

We study the problem of optimally and simultaneously sizing solar photovoltaic (PV) and storage capacity in order to partly or completely offset grid usage. While prior work offers some insights, researchers typically consider only a single sizing approach. In contrast, we use a firm theoretical foundation to compare and contrast sizing approaches based on robust simulation, robust optimization, and stochastic network calculus. We evaluate the robustness and computational complexity of these approaches in a realistic setting to provide practical, robust advice on system sizing.

Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus

The stochastic network calculus (SNC) holds promise as a versatile and uniform framework to calculate probabilistic performance bounds in networks of queues. A great challenge to accurate bounds and efficient calculations are stochastic dependencies between flows due to resource sharing inside the network. However, by carefully utilizing the basic SNC concepts in the network analysis the necessity of taking these dependencies into account can be minimized. To that end, we unleash the power of the pay multiplexing only once principle (PMOO, known from the deterministic network calculus) in the SNC analysis. We choose an analytic combinatorics presentation of the results in order to ease complex calculations. In tree-reducible networks, a subclass of general feedforward networks, we obtain an effective analysis in terms of avoiding the need to take internal flow dependencies into account. In a comprehensive numerical evaluation, we demonstrate how this unleashed PMOO analysis can reduce the known gap between simulations and SNC calculations significantly, and how it favourably compares to state-of-the art SNC calculations in terms of accuracy and computational effort. Motivated by these promising results, we also consider general feedforward networks, when some flow dependencies have to be taken into account. To that end, the unleashed PMOO analysis is extended to the partially dependent case and a case study of a canonical topology, known as the diamond network, is provided, again displaying favourable results over the state of the art.

Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus

The stochastic network calculus (SNC) holds promise as a versatile and uniform framework to calculate probabilistic performance bounds in networks of queues. A great challenge to accurate bounds and efficient calculations are stochastic dependencies between flows due to resource sharing inside the network. However, by carefully utilizing the basic SNC concepts in the network analysis the necessity of taking these dependencies into account can be minimized. To that end, we unleash the power of the pay multiplexing only once principle (PMOO, known from the deterministic network calculus) in the SNC analysis. We choose an analytic combinatorics presentation of the results in order to ease complex calculations. In tree-reducible networks, a subclass of general feedforward networks, we obtain an effective analysis in terms of avoiding the need to take internal flow dependencies into account. In a comprehensive numerical evaluation, we demonstrate how this unleashed PMOO analysis can reduce the known gap between simulations and SNC calculations significantly, and how it favourably compares to state-of-the art SNC calculations in terms of accuracy and computational effort. Motivated by these promising results, we also consider general feedforward networks, when some flow dependencies have to be taken into account. To that end, the unleashed PMOO analysis is extended to the partially dependent case and a case study of a canonical example topology, known as the diamond network, is provided, again displaying favourable results over the state of the art.

Applying stochastic network calculus to logical link control layer in underwater wireless communication: Underwater fish farming perspective

AbstractIn recent days, research in underwater fish farming has been seen as a sustainable method to land‐based fish farming due to the increasing global population and limitations in land‐based farming areas worldwide.The fish farming cage installations are usually operated in the depth area of seawater and are ideal circumstances for fish farming. Monitoring sensor‐based data collection from fish farming is highly efficient and more innovative. Due to the unique properties of seawater, the substantial challenge of an Underwater Wireless Sensor Network (UWSN) is guaranteeing reliable data communication between the underwater sensor nodes and surface nodes. To ensure reliable communication, the mathematical approach Stochastic Network Calculus (SNC) is considered for modeling the reliable data flow control in the logical link control layer to satisfy the requirements of the underwater fish farming application. This paper presents the SNC mathematical model construction of stochastic bounds for frame flow, window size, service element analysis, feedback loop analysis, delay, and backlog bounds derived in the proposed model. The proposed model produces the better frame delivery and throughput in an incremental order with respect to the simulation model. There is significant difference (4 to 12 percentage) between the stochastic and simulation model. Thus, the reliable frame flow can be adopted with the dynamic changes in the feedback loop to avoid the overflow by repeated requests.

QoS-aware resource allocation and fault tolerant operation in hybrid SDN using stochastic network calculus

Mobile edge computing (MEC) is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the users. With the advent of the next-generation mobile networks, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. So, the integration of software defined networking (SDN) and MEC techniques was seriously considered to facilitate the orchestration and management of Mobile Edge Hosts (MEH). Edge clouds can be installed as an interface between the local servers and the core to provide the required services based on the known concept of the SDN networks. Nonetheless, the problem of reliability and fault tolerance will be of great importance in such networks. The paper introduced a dynamic architecture that focuses on the end-to-end mobility support required to maintain service continuity and quality of service. This paper also presents an SDN control plane with stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Compared to other solutions investigated in the literature, the proposed approach exhibits a significant increase in the throughput distributed over the active links of mobile edge hosts. It also proved that the outage index and the system’s aggregate data rate can be effectively improved by up to 32%.

Stochastic Network Calculus-Aided Delay Analysis of Wireless-Power Line Mixed Networks

In this paper, we investigate the delay performance of a wireless-power line mixed network via a stochastic network calculus (SNC)-based approach. The data transmission in this mixed network is modeled by a two-stage tandem queue, wherein the data is first relayed through a wireless fading channel and then transmitted over a power line communication (PLC) system. The Rayleigh fading captures the wireless fading channel; whereas, the PLC channel gain is characterized by the log-normal distribution. The statistical characteristics of the service processes of both the wireless channel and PLC channel are derived. With any given traffic arrival and the service capability derived, the delay can be easily bounded via SNC.

Delay Analysis of Mobile Edge Computing Using Poisson Cluster Process Modeling: A Stochastic Network Calculus Perspective

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

Deterministic Latency Bounded Network Slice Deployment in IP-Over-WDM Based Metro-Aggregation Networks

As a critical enabler of the next generation networks, network slicing can dynamically and flexibly create virtual networks leveraging necessary network resources for services with different quality of service (QoS) requirements. To satisfy the strict QoS requirements of some vertical industries, e.g., industry automation, the Deterministic Networking (DetNet) concept has been recently proposed to investigate deterministic service provisioning with bounds on service latency, loss, and jitter. A critical issue for providing deterministic services is how to allocate the right amount of resources that ensures the QoS requirements within a network slice. In this paper, we first obtain the amount of network resources needed for a network slice, using the stochastic network calculus (SNC) method, to ensure the end-to-end latency requirement under specific traffic demands. We then study the network slice deployment problem in an IP-over-WDM (wavelength division multiplexing) metro aggregation network and propose a heuristic with three different objectives: minimizing traffic hops, minimizing lightpaths and minimizing wavelengths, which can help network service provider to optimize network deployment under different considerations. The simulation results show the comparison of the resource utilization under different strategies.

Medium Access Control layer protocol design based on stochastic network calculus for underwater wireless communication in open‐ocean fish farming

SummaryThe usage of the underwater communication channel and medium access control (MAC) layer protocol architecture is the most problematic aspect of a seawater wireless communication network. The underwater acoustic wireless communication is a highly appealing choice for information transfer in the communication medium. However, calculating the deferral rate for transmitting real‐time traffic is a challenging task in the underwater environment. Therefore, it introduces the research challenges related to underwater communication, especially throughput and transmission deferral. This research work investigates an MAC protocol U‐TDMA with multi‐hop worst‐case delay analysis for different flow and relays. This research intended to compare the execution of the U‐TDMA protocol with stochastic network calculus (SNC) and without SNC. The U‐TDMA‐wireless protocol establishes the connection and forwards the fish‐mandated information to the mobile sink with the help of upper layers. Also, it improves interface efficiency by taking account of the lengthy delay time of the underwater acoustic channel and addressing difficulties connected to the terminal.

Network layer communication protocol design for underwater agriculture farming

SummaryIn recent times, underwater agriculture farming is seen as a viable alternative to land‐based farming by agricultural scientists due to increasing limitations in land‐based farming areas around the world. But farming underwater is not as easy as it is said. Continuous monitoring of different parameters of the water inside the biosphere where the herbs, crops, and plants are cultivated is one of the highest priorities for the effective outcome in underwater farming. To achieve such precise monitoring, sensor‐based data collection from the underwater biosphere is the only way. Employing human divers for monitoring of water parameters has many limitations such as diving time, accidents due to harsh underwater environment. Alternatively, adopting sensor‐based monitoring provides spatial and temporal flexibility. But one of the significant challenges of such an underwater wireless sensor network (UWSN) is ensuring reliable communication between the underwater sensors and surface station. To ensure reliable delivery of sensed information network layer operation is vital. The underwater environment has unique challenges such as high interference, harsh environment, limited lifetime, and short network lifetime the network layer protocols of terrestrial networks can't be adopted into the underwater environment. So this research focuses on the development of a mathematical model for network layer communication using stochastic network calculus (SNC) taking into consideration the requirements of underwater agriculture farming application. In this research, the delay and backlog bounds of the proposed model are derived. The proposed mathematical model is also evaluated for its correctness using the discrete event simulation.

Martingale-Based Bandwidth Abstraction and Slice Instantiation Under the End-to-End Latency-Bounded Reliability Constraint

End-to-end (E2E) reliability provisioning is crucial in network slice customization. In this paper, leveraging martingale theory and stochastic network calculus, we construct an analysis framework of the reliability regard to E2E latency for the multi-hop system, where nodes provide heterogeneous services. A martingale parameter, called as tandem service descriptor, is proposed to measure the service ability of nodes, which embodies the features of tandem service mode supplied for the specific E2E flow. The bandwidth abstraction algorithm is designed, which decouples the E2E latency-bounded reliability requirement as the demanded bandwidth of each node. Subject to the abstracted bandwidth, slice instantiation strategies are performed for the access network and core network respectively. The transmission power is allocated dynamically according to the access states and channel characteristics. In the core network nodes, the service rates that the arrival traffic requires are determined. Simulation results verify the effectiveness of the slice customization scheme.

Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing

Mobile edge computing is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Due to the high frame loss rate during multi-hop transmissions in wireless edge computing networks, the problem of reliability and system safety will be of great importance. Data caching is a necessity to provide reliable edge computing and, at the same time a major consumer of energy. So, we propose a reliable mobile edge computing system using active caching and robust resource allocation approaches. This paper presents an energy-efficient stochastic network calculus framework to control edge-computing data flows for active data-caching systems. In accordance with the entrance processes of different QoS-class data streams, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a reliable user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to improving system safety. In this energy-cooperative approach, the power can be shared among the cells using a grid network. The simulation results show that the proposed approach can effectively increase the network reliability and power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot user distribution models. The numerical results also exhibit considerable power saving for different traffic models in addition to increasing the total network throughput by up to 25%.

End-to-End Delay Bound for Wireless uVR Services Over 6G Terahertz Communications

The forthcoming sixth-generation (6G) network technology will trigger tremendous changes across several application scenarios and even in the way we work, communicate, and, more in general, organize our everyday life. In particular, in such a new digital era, virtual reality (VR) technology will play a fundamental role, carrying with it numerous challenges as regards the increasing demand of high rate, highly reliable, and low-latency communications, especially in reference to Internet-of-Things environments. In this sense, an effective end-to-end (e2e) delay analysis becomes imperative to pursue an efficient design of the foreseen VR services. Toward this goal, this article proposes the e2e delay investigation through the martingale theoretical bound and considering the stochastic network calculus principles for its formulation. The performance discussion focuses on the achieved communications reliability, comparing the analytical predictions obtained via the martingale bound with the simulation results, and the Markov queuing theory-based alternative. The presented results confirm the validity of the proposed approach, exhibiting noticeable closeness between the theoretical bound and the simulation outcomes.

Traffic Allocation for Heterogeneous Links in Satellite Data Relay Networks

Efficient traffic allocation for heterogeneous links in satellite data relay networks (SDRNs) has been an attractive topic nowadays. However, different traffic types and link heterogeneity bring plenty of difficulties to the link delay evaluation which directly impacts the traffic allocation efficiency. Owing to the unique determination of the probability distribution of arrival processes, we succeed in characterizing any type of traffic with the help of the moment generating function (MGF). According to the alternation and discontinuity of satellite ground links (SGLs), we propose a Markov chain Monte Carlo embedded service matrix construction method to model the SGL service. Being apt at combining service processes, the stochastic network calculus based min-plus convolution is employed to depict the relay service. By deriving the tight upper bounds of the delay in heterogeneous links based on the MGF, min-plus convolution, and Markov chain, respectively, taking advantage of the Chernoff bound and Union bound, we calculate the optimal traffic allocation ratio in terms of minimum system delay. Finally, numerous simulations are conducted to show the relationships among the optimal traffic allocation ratio, traffic type, and link heterogeneity.

Stochastic Network Calculus Model for Delay Distribution of Time Division Multiple Access and Carrier Sense Multiple Access in Underwater Acoustic Wireless Communication

Underwater Acoustic wireless communication becomes a popular research area for transmitting and receiving data between the communicators in the ocean environment. High accuracy in data communication can be succeeded by proficient modeling of carrier sense multiple access and time division access. For such innovations, the system has to proceed with an appropriate framework to make constant data traffic and limitations on start to finish data traffic delays. There are two major wireless communication multiple access methods that can be utilized in real-time underwater networks. One of the multiple access schemes is TDMA and another one is CSMA/CA. This paper concentrated on the modeling of TDMA and CSMA/CA also proposes the comparison of delay (end-end) of both multiple access protocols. The results using SNC to obtain delay bounds and are associated with simulation. The results show TDMA has a lesser efficiency than CSMA/CA in the acoustic environment.

End-to-End Delay Bound for VR Services in 6G Terahertz Networks with Heterogeneous Traffic and Different Scheduling Policies

The emerging sixth-generation networks have to provide effective support to a wide plethora of novel disruptive heterogeneous applications. This paper models the probabilistic end-to-end delay bound for the virtual reality services in the presence of heterogeneous traffic flows by resorting to the stochastic network calculus principles and exploiting the martingale envelopes. The paper presents the network performance analysis under the assumption of different scheduling policies, considering both the earliest deadline first and the first-in-first-out queue discipline. Furthermore, differently from previous literature, the probabilistic per-flow bounds have been formulated taking into account a number of traffic flows greater than two, which results in a theoretical analysis that is remarkably more complex than the case in which only two concurrent flows are considered. Finally, the validity of the theoretical bounds have been confirmed by the evident closeness between the analytical predictions and the actual simulation results considering, for the sake of argument, four concurrent traffic flows with heterogeneous quality-of-service constraints. That closeness exhibits the ability of the proposed analysis in fitting the actual behavior of the system, representing a suitable theoretical tool to support resource allocation strategies, without violating service constraints.

Analysis of backlog and delay in downlink power-domain non-orthogonal multiple access wireless networks

As important quality-of-service (QoS) performance indicators, backlog and delay in wireless networks must be in compliance with the requirement of low-latency and high-reliability scenarios. Non-orthogonal multiple access (NOMA) is a novel and key wireless technology for the next generation of mobile communications. Its potential impacts on backlog and delay deserve to be specially studied. Meanwhile, the stochastic network calculus is an effective tool for analyzing network performances such as backlog and delay. In this paper, for both orthogonal multiple access (OMA) scheme and NOMA scheme, we utilize the stochastic network calculus to characterize respectively cumulative service processes of channels in a downlink network. Based on moment generating functions, the closed-form expressions of the upper bounds on backlog and delay are derived through the definite integral and special function. Simulation results validate reasonableness and effectiveness of the proposed approach. We show that in terms of backlog and delay, performances cannot be always improved by substituting NOMA scheme for OMA scheme, except when the channel gain gap between users is significantly big. Moreover, the sensitivity to the same factor such as arrival rate, transmission power and power allocation, differs from user to user in NOMA. By simulations, it is further demonstrated that the performance loss due to a finite buffer in practical scenarios is acceptable and sometimes negligible, as long as the buffer size is set to be just moderate.

NETWORK CALCULUS. PART 2. PRACTICAL USE

The frst part of the article provided an overview of the basic principles of the promising direction of modeling and analysing infocommunication networks and systems - Network Calculus theory. The second part of the article considers examples of practical application of Network Calculus theory to the analysis of infocommunication networks and systems. A qualitative comparison was given of trafc delays estimates in the queuing system obtained using the methods of queuing theory, network calculus theory and simulation, with delays in a real system. The results of using network calculus to assess the boundary characteristics of the service trafc quality in the most promising infocommunication networks are presented. The features of the use of deterministic network calculus for estimating end-to-end delays in sensor networks and the upper queues boundaries in the bufers of the controller and switches of software-defined networking are described. Application of the theory of stochastic network calculus to obtain boundary estimates of end-to-end delays in LTE mobile networks for trafc fows of real and unreal time is shown.