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In this paper, we address the issue of rule duplication during network updates in software-defined networking (SDN). In SDN, network update involves the controller in sending update packets to desired set of switches, where the update rules are installed. To ensure update consistency, old flow rules are stored until the total update procedure is complete. Higher consumption of ternary content addressable memories (TCAMs) during update increases the cost of network update and decreases the scalability of SDN. In this paper, we propose an approach for consistent update with redundancy reduction, named CURE, which reduces the TCAM usage during update. CURE prioritizes switches according to their usage pattern and schedules updates based on priority zones. The proposed approach guarantees that highly loaded switches are updated first. CURE also maintains packet-level consistency by implementing a multilevel queuing approach. In this framework, each switch in the current update region stores the incoming packets in individual device queues until the switch completes update. Therefore, after the initiation of an update, packets are processed according to new rules only. The results of performance evaluation depict that the average rule space utilization during update using CURE is 29.954% less than using the two-phase update proposed in the existing literature. [ABSTRACT FROM AUTHOR]

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%. [ABSTRACT FROM AUTHOR]

Although the logically-centralized perspective is offered in Software-Defined Networking (SDN), the data plane is still distributed. Update commands sent by the centralized controller are executed asynchronously and independently in each switch. The timed SDN enables synchronous and coordinated update operations as each update command can be triggered by a pre-defined timestamp. Prior work on timed update mainly focuses on producing a congestion-free update sequence, whereas finding a congestion-free timed update sequence may prolong the update time. Even worse, such an update order may not exist. In this paper, we propose Chronus+, a novel timed update system that utilizes the switch buffer to shorten the update time while minimizing the switch buffer during updates. First, we formulate the Minimum Switch Buffer Problem (MSBP) as an integer linear programming and show its hardness. Then, we propose a set of efficient algorithms to solve the problem in polynomial time. Extensive evaluations in Mininet and large-scale simulations show that Chronus+ can reduce the update time by at least 17% and the switch buffer size by at least 27% compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The scalability issue hinders the deployment of Software-Defined Networking (SDN) in the Wide Area Networks (WANs). Existing solutions have two issues: (1) network performance relies on complicated controller synchronization, which increases the complexity of network control; (2) fine-grained flow processing enables flexible flow control at the cost of high processing load on the controllers and high flow table occupancy on switches. In this paper, we propose a scalable routing solution named HybridFlow, which achieves a good load balancing performance using a single controller with low control overhead (i.e., flow routing and rerouting overhead). HybridFlow mainly employs two techniques: hybrid routing and crucial flow rerouting. Hybrid routing enabled by commercial SDN switches gives us opportunities to reduce the processing load of the controller by routing flows with the hybrid OpenFlow/OSPF mode. Thus, the majority of flows can be routed by OSPF without involving the controller. Crucial flow rerouting realizes load balancing by dynamically identifying crucial flows based on a new metric called Variation Slope and rerouting these flows with the hybrid OpenFlow/OSPF mode. The simulation based on the real traffic traces and network typologies shows that compared with the optimal solution, HybridFlow can achieve 87% of the optimal load balancing performance by rerouting 36% less flows on average. [ABSTRACT FROM AUTHOR]

In this paper, we propose a scheme to address the problem of load management in the control plane of Software-Defined Internet of Things (SDIoT) networks. In SDIoT, multiple controllers are deployed to enhance network scalability. With the growth of IoT, the number of devices is increasing rapidly. The management of control plane load is an essential issue for IoT networks because of the dynamic traffic characteristics. IoT traffic is highly dynamic due to the heterogeneity of IoT devices in terms of mobility, activation model, Quality of Service (QoS) demand, and flow generation rate. The challenge is to prevent controller overload and distribute traffic optimally under the consideration of heterogeneous IoT devices. The proposed scheme estimates control plane load based on the mobility and activation model of IoT devices. For mobility prediction, we use Order- $m$ fallback Markov Predictor as it consumes less space and performs efficiently even for small values of $m$. Based on the prediction results, we implement a traffic-aware rule-caching mechanism and a master controller assignment scheme to reduce the control plane load. Simulation results show that the proposed scheme reduces the peak intensity of the control traffic by 23.08% and 16.67%, as compared to the considered benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we present a traffic-aware flow migration approach, which reduces data plane load in Software-Defined Networking (SDN) during a network update. SDN update involves rerouting of multiple traffic flows to accommodate new flows. An unplanned flow migration schedule overloads the data plane by burdening the data links and flooding the rule-space of capacity-constrained SDN switches. The overload of data links and switches blocks the update process, and the network fails to address the Quality of Service (QoS) demands of the traffic flows, especially latency-sensitive flows. Prior approaches migrate flows without considering load reduction of the data plane along with QoS demands of the flows. In this work, we propose a load reduction strategy that prioritizes traffic flows based on QoS demands and aims to avoid link congestion and rule-space overflow during flow migration. The proposed scheme significantly reduces the maximum data link bandwidth usage. In particular, the maximum data link bandwidth usage is 13.22% less than the two-phase update approach. [ABSTRACT FROM AUTHOR]

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP’s revenue and reducing the peak-to-average traffic ratio (PATR). [ABSTRACT FROM AUTHOR]

In this paper, we consider a mission-critical control system, where a dynamic plant is monitored by a mobile device (MD), and the monitored signal is transmitted to a remote controller via heterogeneous cellular and Wi-Fi networks. We propose an optimal control-aware machine-to-machine (M2M) transmission strategy for mission-critical control applications, in which control performance is measured by remote estimation error and system stability while limited by bandwidth cost. Specifically, the problem of minimizing estimation error, subject to the constraints of cellular usage costs and system stability, is formulated as an infinite-horizon constrained Markov decision process (CMDP), where the MD has options to transmit through Wi-Fi or cellular, or to stay idle. We solve the problem by utilizing the Lagrange multiplier approach, and prove that the optimal strategy is a randomized mixture of two threshold structure strategies. Furthermore, to estimate the structured optimal strategy, we present an algorithm called simultaneous perturbation stochastic approximation (SPSA), in which the complexity is $O(|\mathcal {A}|)$ lower than a non-structured one with $|\mathcal {A}|$ being the number of the actions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dynamic controller assignment scheme while considering flow-specific requirements, with an aim to minimize controller response time in software-defined networks (SDN). Using the FlowVisor model, we develop a virtualized platform that acts as a manager between the control- and data-planes of SDN architecture. The proposed scheme consists of two phases – adaptive window selection and controller assignment. In the window selection phase, the virtualized manager determines time to wait before incoming flows can be assigned to controllers in adaptive manner. Based on the adaptive window size, the flows are assigned to the controllers in the second phase. We use dynamic stable-matching game to assign flows to controllers, while defining their preference lists to minimize flow-setup delay and associated control overhead. Simulation studies show that the proposed scheme is capable of minimizing controller response time by atleast 31% compared to the existing state-of-the-art. Further, the proposed scheme also reduces the percentage of QoS violated flows in the network. [ABSTRACT FROM AUTHOR]

Autonomous vehicular platoons will play an important role in improving on-road safety in tomorrow’s smart cities. Vehicles in an autonomous platoon can exploit vehicle-to-vehicle (V2V) communications to collect environmental information so as to maintain the target velocity and inter-vehicle distance. However, due to the uncertainty of the wireless channel, V2V communications within a platoon will experience a wireless system delay. Such system delay can impair the vehicles’ ability to stabilize their velocity and distances within their platoon. In this paper, the problem of integrated communication and control system is studied for wireless connected autonomous vehicular platoons. In particular, a novel framework is proposed for optimizing a platoon’s operation while jointly taking into account the delay of the wireless V2V network and the stability of the vehicle’s control system. First, stability analysis for the control system is performed and the maximum wireless system delay requirements which can prevent the instability of the control system are derived. Then, delay analysis is conducted to determine the end-to-end delay, including queuing, processing, and transmission delay for the V2V link in the wireless network. Subsequently, using the derived wireless delay, a lower bound and an approximated expression of the reliability for the wireless system, defined as the probability that the wireless system meets the control system’s delay needs, are derived. Then, the parameters of the control system are optimized in a way to maximize the derived wireless system reliability. Simulation results corroborate the analytical derivations and study the impact of parameters, such as the packet size and the platoon size, on the reliability performance of the vehicular platoon. More importantly, the simulation results shed light on the benefits of integrating control system and wireless network design while providing guidelines for designing an autonomous platoon so as to realize the required wireless network reliability and control system stability. [ABSTRACT FROM AUTHOR]

This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control systems are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control $2\times$ more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is $18\times$ larger than a round-robin scheduling baseline. [ABSTRACT FROM AUTHOR]

We abstract the core logical functions from applications that require ultra-low-latency wireless communications to provide a novel definition for reliability. Real-time applications — such as intelligent transportation, remote surgery, and industrial automation — involve a significant element of control and decision making. Such systems involve three logical components: observers (e.g. sensors) measuring the state of an environment or dynamical system, a centralized executive (e.g. controller) deciding on the state, and agents (e.g. actuators) that implement the executive’s decisions. The executive harvests the observers’ measurements and decides on the short-term trajectory of the system by instructing its agents to take appropriate actions. All observation packets (typically uplink) and action packets (typically downlink) must be delivered by hard deadlines to ensure the proper functioning of the controlled system. In-full on-time delivery cannot be guaranteed in wireless systems due to inherent uncertainties in the channel such as fading and unpredictable interference; accordingly, the executive will have to drop some packets. We develop a novel framework to formulate the Observer Selection Problem (OSP) through which the executive schedules a sequence of observations that maximize its knowledge about the current state of the system. To solve this problem efficiently yet optimally, we devise a branch-and-bound algorithm that systematically prunes the search space. Our work is different from existing work on real-time communications in that communication reliability is not conveyed by packet loss or error rate, but rather by the extent of the executive’s knowledge about the state of the system it controls. [ABSTRACT FROM AUTHOR]

As networking attempts to cleanly separate the control plane and forwarding plane abstractions, it also defines a clear interface between these two layers. An underlying network state is represented as a view to act upon in the control plane. We are interested in studying some fundamental properties of this interface, both in a general framework, and in the specific case of content routing. We try to evaluate the traffic between the two planes based on allowing a minimum level of acceptable distortion in the network state representation in the control plane. We apply our framework to content distribution, and see how we can compute the overhead of maintaining the location of content in the control plane. This is of importance to evaluate resolution-based content discovery in content-oriented network architectures: we identify scenarios where the cost of updating the control plane for content routing overwhelms the benefit of fetching the nearest copy. We also show how to minimize the cost of this overhead when associating costs to peering traffic and to internal traffic for network of caches. [ABSTRACT FROM PUBLISHER]

In a software defined network, the controller needs to obtain/collect traffic measurement information (i.e., flow statistics) from switches for different applications, such as traffic engineering. Existing solutions seldom consider the per-switch cost, which may lead to heavy statistics collection cost (e.g., high CPU overhead) on some switches. Due to limited computing power on most commodity switches, heavy statistics collection cost on those switches may seriously interfere with the basic rule operations, especially when some switches need to deal with many new-arrival flows or update routes of existing flows. To address this challenge, we design and implement efficient flow statistics collection (FSC) with limited interference on the basic rule operations. We formally propose a cooperative flow statistics collection with per-switch cost constraint (CP-FSC) problem. We prove that the CP-FSC problem is NP-hard and present an efficient algorithm with approximation ratio 1/2, based on dynamic programming. To reduce the time complexity, a greedy-based algorithm with approximation ratio 1/3 is also presented. We implement the proposed FSC algorithms on our SDN platform. The experimental results and the extensive simulation results show 36%-59% performance improvement compared with the existing solutions. [ABSTRACT FROM AUTHOR]