Delay Guarantees Research Articles

Enabling Multicast Slices in Edge Networks

Telecommunication networks are undergoing a disruptive transition toward distributed mobile edge networks with virtualized network functions (VNFs) [e.g., firewalls, intrusion detection systems (IDSs), and transcoders] within the proximity of users. This transition will enable network services, especially Internet-of-Things (IoT) applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this article, we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into integer linear programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a nontrivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on reinforcement learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real testbed. The experimental results demonstrate that the proposed algorithms are promising.

Stochastic Delay Guarantee of Wireless Dual-Hop Networks With Interference-Limited Relay

In the interference-limited communication scenarios, the interference impairs the data transmission and further degrades the delay performance. In this letter, the stochastic delay guarantee for a wireless dual-hop network with an interference-limited relay is investigated. Considering the randomness of wireless fading in both data links and interference links, the stochastic service process provided by the network is characterized in terms of the moment generation function (MGF). Due to the stochastic service process, the delay should be a random variable (RV). The delay-outage probability is defined as the probability that the actual transmission delay exceeds a threshold value. To evaluate the stochastic delay guarantee, we derive an upper bound on the minimum acceptable delay threshold and a lower bound on the maximum supportable transmitted data size with the constraint of the delay-outage probability, respectively. Simulation results show that the bounds obtained by the theoretical analysis are reasonably tight and well reflect the effect of the interference on the stochastic delay guarantee.

A Reliability-Aware, Delay Guaranteed, and Resource Efficient Placement of Service Function Chains in Softwarized 5G Networks

Network Functions Virtualization (NFV) allows flexibility, scalability, agility, and easy manageability of networks by leveraging the features of virtualization and cloud computing technologies. However, softwarization of network functions imposes many challenges. Reliability and latency are major challenges in NFV-enabled 5G networks that can lead to customer dissatisfaction and revenue loss. In general, redundancy is used to improve the reliability of communication services. However, redundancy requires the same amount of additional resources and thus increases cost. In this work, we address the reliability-aware, delay guaranteed, and resource efficient Service Function Chain (SFC) placement problem in softwarized 5G networks. First, we propose a novel SFC subchaining method to enhance the reliability of an SFC without backups. If reliability requirement is not met after subchaining method, we add backups to VNFs to meet the reliability requirement. Then, we formulate the reliable SFC placement problem as an Integer Linear Programming (ILP) problem in order to solve it optimally. Owing to high computational complexity of the ILP problem for solving large input instances, we propose a modified stable matching algorithm to provide near-optimal solution in polynomial time. By extensive simulations we show that our proposed solutions consume lesser physical resources compared to state-of-the-art solutions for provisioning reliable communication services.

Jellyfish dynamic routing protocol with mobile sink for location privacy and congestion avoidance in wireless sensor networks

Abstract Recently, Wireless Sensor Network (WSN) is often viewed with an oversized range of sensors that are structured and collaborate to gather and transmit information around the targets. As sensors may be positioned in harsh surroundings, it is critical for secure data transmission. Therefore, a dynamic routing path should be essential for WSN applications. In this paper, a Jellyfish Dynamic Routing Protocol (JDRP) for preserving location privacy and congestion avoidance with less delay guaranteed is proposed. With this routing technique, the complete sensor field is divided into different subdivisions and each subdivision elects a target area by computing its transmission distance. The backbone of the dynamic routing protocol consists of a virtual ring called bell nodes and a radial line called tentacle nodes employs more nodes to construct the network. The amount of radial line and radius of the virtual ring in a network are conjointly determined to ease the communication path from the node to sink. In this structure, the radial line paths are routed directionally and bell nodes are routed with angular directions probabilistically. From the routing path, the tentacle nodes collect the data to dynamic sink which will assure that the information is going to be collected with less delay and attacker cannot guess their positions. The experimental results show that the proposed JDRP method accomplishes enhanced performance in terms of energy consumption, packet delivery delay and lifetime.

Adaptive Forwarding With Probabilistic Delay Guarantee in Low-Duty-Cycle WSNs

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees ( i.e. , delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%~25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery.

통합 플로우 기반 스케줄링과 Interleaved Regulator를 이용한 네트워크 지연시간 보장 프레임워크

통합 플로우 기반 스케줄링과 Interleaved Regulator를 이용한 네트워크 지연시간 보장 프레임워크

Framework for Delay Guarantee in Multi-Domain Networks Based on Interleaved Regulators

The key to the asynchronous traffic shaping (ATS) technology being standardized in IEEE 802.1 time sensitive network (TSN) task group (TG) is the theorem that a minimal interleaved regulator (IR), attached to a FIFO system does not increase delay upper bound while suppresses the burst accumulation. In this work it is observed that the FIFO system can be a network for flows that share same input/output ports and same queues of the network, and are treated with a scheduling scheme that guarantees the FIFO property within a queue. Based on this observation, a framework for delay bound guarantee is further proposed, in which the networks with flow aggregates (FAs) scheduling and minimal IRs per FA attached at the network edge are interconnected. The framework guarantees the end-to-end delay bound with reduced complexity, compared to the traditional flow-based approach. Numerical analysis shows that the framework yields smaller bound than both the flow-based frameworks such as the integrated services (IntServ) and the class-based ATS, at least in the networks with identical flows and symmetrical topology.

Dynamic Flow Migration for Embedded Services in SDN/NFV-Enabled 5G Core Networks

Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.

SDN replaced deployment and real-time QoS provisioning based on network models

From industrial communications to real-time internet applications, end-to-end delay guarantee is one of the most important aspects of quality of service (QoS). As a new networking paradigm, software-defined networking (SDN) exposes an opportunity to provide this guarantee. In exploiting SDN, We can replace the legacy network devices to SDN forwarding units (SDN-FUs gradually. This paper focuses on the SDN replaced deployment problem, striving for providing real-time QoS provisioning in a network meanwhile. Utilising previous works originated from network calculus, we first propose the candidate nodes selection methods for upgrading legacy nodes to SDN-FUs and then present a routing algorithm located on SDN controller to direct the running of SDN-FUs. Via a simulation about a small network, we show that the delay of a flow passing through a network can reduce significantly with the usage of SDN.

Efficient upstream bandwidth utilization with minimum bandwidth waste for time and wavelength division passive optical network

For an efficient bandwidth management among the optical network units in the upstream link of a passive optical network (PON), it is necessary to use a dynamic bandwidth assignment (DBA) scheme at the optical line terminal. This is really helpful for the PON service providers in meeting their delay guarantees and bandwidth commitments as per the service level agreements (SLAs). It also enables them to increase their revenue by accommodating more users in the available bandwidth. This study investigates the weaknesses of existing ITU compliant DBA schemes. The study reviews the earlier reported work on DBA with a focus on the ITU compliant schemes. The study argues that the existing reported schemes suffer from a few deficiencies such as inefficient bandwidth reporting in their polling process and borrow refund problem while utilizing the unused bandwidth (UBW). To overcome these shortcomings, an efficient bandwidth assignment (EBA) scheme is presented. Extensive simulations are performed to test the performance of the proposed scheme with both Poisson distributed and self-similar traffic patterns. The results show that the EBA scheme efficiently utilizes the UBW and RSB leading up to 80%, 90%, 60% and 99% lesser upstream delays with Poisson traffic pattern and up to 80%, 77% and 99% lesser delay with self-similar traffic compared to GREAL, IACG, and GIANT schemes. However, compared to EBU it shows up to 12% higher delay values due to reduction of its RSB share because of the utilization of UBW by T3 and T4. Overall, compared to other schemes, EBA exhibits least bandwidth waste per cycle resulting in least US delays. The bandwidth waste in EBA is also minimized due to least unallocated bandwidth ratio and frame loss rate. The maximum US delays of all traffic classes for EBA remains under 2 ms for T2 and T3 till the traffic load of 1 and till the load of 0.8 for T4 traffic class.

Providing delay-guaranteed multicast transmission services for an SDN-based publish/subscribe system

Providing end-to-end delay guarantees in traditional Internet is a complex task due to the distributed nature of TCP/IP protocols. Software Defined Networking (SDN) gives a new dimension to improve QoS(Quality of Service) as it can benefit from its flexibility, programmability and centralized view. In this paper, we provide delay-guaranteed data transmission service instead of “best efforts” service for a topic-based publish/subscribe system by means of exploring these specific features of SDN. We attribute this routing problem in such conditions to Delay-Constraint Lowest Cost Steiner Tree (DCLCST) problem. To solve it, we compute the shortest delay paths from source node to every subscribe node and the shortest cost paths from every subscribe node to any other node using dijkstra algorithm. Then we construct a delay-constraint least cost steiner tree for per-topic based on these paths as multicast tree. We also present experimental results to demonstrate the effectiveness of the algorithms and methods we proposed.

Decentralized gene regulatory networks: An approach to energy efficient node coordination with delay constraints for wireless sensor systems

Wireless sensor networks (WSNs) systems have been used for several applications from target tracking to environment monitoring. Recently, there has been increased interest in the design of WSN protocols for delay-sensitive applications, such as military surveillance, health monitoring, and infrastructure security. However, due to the strict resource constraints of the sensor nodes, WSNs pose critical challenges for network protocol design for reliable communications with delay constraints. Biologically inspired modeling techniques have received considerable attention for their robustness, scalability, and adaptability with simple local interactions and limited information. In this paper, we apply gene regulatory networks (GRNs) principles to the WSN system and propose a new GRN model for decentralized node coordination to achieve energy saving while meeting delay requirements. Using control theory, we provide conditions of system parameters to ensure system convergence and stability. Simulation results indicate that the proposed scheme achieves superior performance with energy savings as well as desirable delay guarantees compared with other wellknown schemes.

Joint Optimization of Delay Guarantees and Resource Allocation for Service Function Chaining

A Service Function Chain (SFC) is an ordered sequence of virtual network functions (VNFs) to provide network service. Most existing SFC orchestration schemes, however, cannot optimize the resources allocation while guaranteeing the service delay constraint. To fulfill this goal, we propose a Layered Graph based SFC Orchestration Scheme (LGOS). LGOS converts both the cost of resource and the related delay into the link weights in the layered graph, which helps abstract the SFC orchestration problem as a shortest path problem. Then a simulated annealing based batch processing algorithm is designed for SFC requests set. Through extensive evaluations, we demonstrated that our scheme can reduce the end-to-end delay and the operational expenditure by 21.6% and 13.7% at least, and the acceptance ratio of requests set can be improved by 22.3%, compared with other algorithms.

Throughput Optimization With Delay Guarantee for Massive Random Access of M2M Communications in Industrial IoT

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

Energy-Efficient Resource Allocation for Secure Cognitive Radio Network With Delay QoS Guarantee

A joint resource allocation policy for an underlay multi-user secure cognitive radio network (CRN) adopting the time-division multiple-access scheme with the guarantee of the statistical delay quality-of-service is proposed in this paper. Through the problem formulation of the resource allocation for the multi-user secure CRN with perfect channel state information (CSI), the energy-efficient joint power and time optimization scheme is proposed, which aims at minimizing the β-fair cost functions subject to fairness, delay, and security constraints. Furthermore, considering the multi-user secure CRN with imperfect CSI, we also propose a robust resource allocation scheme and the tradeoff between the robustness and the achievable sum-power is analyzed. Numerical results show that the optimal resource allocation scheme with perfect CSI can effectively balance the tradeoff between the energy efficiency and the fairness and is superior to the other conventional resource allocation strategies. Moreover, the robust resource allocation policy with imperfect CSI makes the theoretical research much closer to the practical applications.

네트워크 지연시간 보장을 위한 개선된 레귤레이팅 스케줄러

네트워크 지연시간 보장을 위한 개선된 레귤레이팅 스케줄러

Delay Guarantee and Effective Capacity of Downlink NOMA Fading Channels

Nonorthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami- $m$ and Rician NOMA channels, by exploiting stochastic network calculus (SNC). The key challenge addressed is to derive the Mellin transforms of the service processes in the NOMA fading channels. The transforms are proved to be stable, and incorporated into the SNC to provide the closed-form upper bounds of the delay violation probability. The paper also applies the Mellin transforms to develop the closed-form expressions for the effective capacity of the NOMA fading channels, which measures the channel capacity under statistical delay guarantees. By further applying the min–max and max–min rules, two new power allocation algorithms are proposed to optimize the closed-form expressions, which can provide the NOMA users fairness in terms of delay violation probability and effective capacity. Simulation results substantiate the derived upper bounds of the delay violation probabilities, and the effective capacity. The proposed power allocation algorithms are also numerically validated.

Tiny Codes for Guaranteeable Delay

Future 5G systems will need to support ultra-reliable low-latency communications scenarios. From a latency-reliability viewpoint, it is inefficient to rely on average utility-based system design. Therefore, we introduce the notion of guaranteeable delay which is the average delay plus three standard deviations of the mean. We investigate the trade-off between guaranteeable delay and throughput for the point-to-point wireless erasure links with unreliable and delayed feedback, by bringing together signal flow techniques to the area of coding. We use tiny codes, i.e., sliding window by coding with just 2 packets, and design three variations of selective-repeat ARQ protocols, by building on the baseline scheme, i.e., uncoded ARQ, developed by Ausavapattanakun and Nosratinia: (i) Hybrid ARQ with soft combining at the receiver; (ii) cumulative feedback-based ARQ without rate adaptation; and (iii) coded ARQ with rate adaptation based on the cumulative feedback. Contrasting the performance of these protocols with uncoded ARQ, we demonstrate that the HARQ performs only slightly better, the cumulative feedback-based ARQ does not provide significant throughput while it has a better average delay, and the Coded ARQ can provide gains up to about 40% in terms of throughput. The Coded ARQ also provides delay guarantees, and is robust to various challenges such as imperfect and delayed feedback, burst erasures, and round-trip time fluctuations. This feature may be preferable for meeting the strict end-to-end latency and reliability requirements of the future use cases of ultra-reliable low-latency communications in 5G, such as mission-critical communications and industrial control for critical control messaging.

Energy-Efficient and Delay-Guaranteed Workload Allocation in IoT-Edge-Cloud Computing Systems

Edge computing has recently emerged as an extension to cloud computing for quality of service (QoS) provisioning particularly delay guarantee for delay-sensitive applications. By offloading the computationally intensive workloads to edge servers, the quality of computation experience, e.g., network transmission delay and transmission energy consumption, could be improved greatly. However, the computation resource of an edge server is so scarce that it cannot respond quickly to the bursting computation requirements. Accordingly, queuing delay is un-negligible in a computationally intensive environment, e.g., a computing environment consists of the Internet of Things (IoT) applications. In addition, the computation energy consumption in edge servers may be higher than that in clouds when the workload is heavy. To provide QoS for end users while achieving green computing for computing systems, the cooperation between edge servers and the cloud is significantly important. In this paper, the energy-efficient and delay-guaranteed workload allocation problem in an IoT-edge-cloud computing system are investigated. We formulate a delay-based workload allocation problem which suggests the optimal workload allocations among local edge server, neighbor edge servers, and cloud toward the minimal energy consumption as well as the delay guarantee. The problem is then tackled using a delay-base workload allocation (DBWA) algorithm based on Lyapunov drift-plus-penalty theory. The theoretical analysis and simulation results have been conducted to demonstrate the efficiency of the proposal for energy efficiency and delay guarantee in an IoT-edge-cloud system.

Toward Delay-Based Utility Maximization: Modeling and Implementation in an SDWN Platform

Packet (link) scheduling algorithms in wireless networks should be able to maximize the networks' transmission capability while maintaining an acceptable delay and fairness performance. Maximum-weight (MW) policies based on the delay index are throughput-optimal in a wireless network. However, sampling the real-time head-of-line (HOL) packet statistics is still challenging. We model the delay as a function of the arrival rate and service rate, and this model enables the scheduling algorithms to obtain the packet delay without high-frequency sampling. Then, we model the delay-related performance problems in everyday life as a utility-maximization problem. By utilizing the Lyapunov optimization, we transform the initial problem into a queue stability problem and design an MW algorithm to solve it. The solution can ensure deterministic long-term average delay guarantees. Moreover, its throughput utility differs from the optimally fair value by an amount that is inversely proportional to the delay guarantee. We evaluate the theoretical performance via simulations. We also build a real-world software-defined wireless network platform and implement our algorithm on it to evaluate the algorithm's actual performance. The experimental results show that our method outperforms the state-of-the-art algorithm in terms of throughput by up to 13.83%, and it outperforms the default first-in, first-out (FIFO) method by up to 40.41%.