Group mood and engagement are invisible currents that shape how people collaborate at work, emerging through everyday interactions rather than isolated individual states. While positive socio-emotional dynamics support collaboration and productivity, they remain difficult to sense and interpret unobtrusively. This work investigates whether posture-based behavioral cues sensed through everyday objects can provide insight into group mood and engagement. We present a pressure-sensing chair prototype that captures changes in weight distribution during meetings. In a study with 14 groups (N = 46), we combine postural cues with participants’ retrospective video annotation to triangulate engagement and mood. Our results show that posture activity is associated with engagement and mood arousal, while moments of shared group mood co-occur with increased postural synchrony. We further identify synchronized behavioral patterns reflecting affective convergence and contagion. These findings demonstrate how situated sensing through everyday objects can reveal socio-emotional dynamics and inform the design of collaborative systems.

Named Data Networking (NDN) enables efficient content distribution through in-network caching. However, the additional states of network intermediary nodes make NDN forwarding more burdensome, and the unpredictability of cache hits during forwarding leads to uncertain content retrieval latency. To overcome performance bottlenecks at the router's data plane and enhance network determinism, we propose the decoupled content router with remote content store (D-Router). This novel architecture decouples the local content store (CS) from routers and introduces the remote CS device for pooling important content. When Interest packets arrive at a router whose CS is overloaded, we ensure determinism by forwarding them to the remote CS for processing if the requested content is cached there, preventing blocking before the local CS of routers and potential random cache hits along the forwarding path. The dual-path bypass forwarding is supported through the design of routers and a dual-path routing protocol. D-Router is compatible with traditional NDN. Experiments show notable enhancements in data plane performance, including a 30% reduction in round-trip time (RTT), a 25% increase in throughput, improved determinism, and reduced network jitter. Additionally, the decoupling of CS makes it easier for network administrators to deploy network upgrades.

Due to the high costs of low Earth orbit (LEO) satellite manufacturing and launch, as well as the complexity of in-orbit network protocol debugging, simulating and verifying satellite network protocols on the ground before satellite launch holds significant importance. Compared to the expensive emulation with one-to-one replication, simulation (e.g., using ns-3) can achieve discrete event processing at a relatively lower cost by extending the wall clock time. However, very few studies have used ns-3 for LEO satellite network simulation, facing challenges such as faithfully simulating the on/off state switching of inter-satellite links (ISLs) and achieving simulation performance scalability for high-density satellite constellations. In this work, we propose a system to accelerate mega-scale LEO satellite network simulation in ns-3 via MPI-based parallelization. Specifically, we simulate ISLs based on ns-3's P2P channels/P2P remote channels and achieve runtime link connection/disconnection by implementing stateful traffic dropping inside the network interface. Then, we conduct concurrent simulation with ns-3's parallel and distributed simulation capability and partition the satellite constellation into multiple simulation processes through a hierarchical clustering algorithm and automated scripts, considering satellite locality and inter-process workload balance. Our evaluation shows significant speed improvements via parallelization, e.g., a 373% speedup with 12 processes for LEO-192, and a 156% speedup with 3 processes for LEO-3072.

Considering the high costs of satellite manufacturing and launch, as well as the complexity of in-orbit debugging, pre-launch emulation on the ground will significantly reduce the development costs of low Earth orbit (LEO) satellite networks. LEO satellite network emulation faces challenges in emulating mega-scale constellations in a lightweight and scalable manner, as well as efficiently handling the frequent link on/off switching for both inter-satellite networks and terrestrial access networks. Existing simulation/emulation tools, such as NS-3, Mininet, QualNet, fall short in addressing these issues effectively. In this work, we propose a lightweight satellite emulation testbed based on Docker containers and the VLAN protocol. In the data plane, our testbed uses Docker containers to emulate satellites/terminals, and uses VETH-pairs and bridges to emulate inter-satellite networks and terrestrial access networks. Furthermore, these virtual network elements can horizontally scale across multiple servers for mega-scale constellation emulation. In the control plane, the real-time constellation topology changes are efficiently emulated through the configuration of VLAN segmentation according to satellite movement patterns. Evaluation shows the testbed's low resource occupancy and high efficiency, with 100 nodes consuming only 1000MB memory and 100 links switching in less than 2s.

For mega-scale low Earth orbit (LEO) satellite constellations, the relative position changes between satellites and ground terminals pose challenges for end-to-end TCP session maintenance, which serves as the substrate for many Internet services. Mobile IP resolves the session maintenance issue by in-troducing a binding mechanism between the care-of address and home address; however, this also leads to inefficient triangular routing. Our recently proposed LISP-LEO, through partition-satellite mapping, routes traffic to the service satellite above the destination terminal's partition, addressing the triangular routing problem. However, due to the corner case of partition-satellite mapping, LISP-LEO introduces the issue of the last-hop route selection, as well as the associated per-terminal registration states on the satellite, making the solution non-scalable. In this work, we propose Gaia, a ground station-centric mobility management scheme for LEO satellite networks. Gaia maintains a precise mapping of each ground terminal's IP and its geographical location. When receiving traffic from a source terminal, the access satellite can, based on the geographical location of the destination terminal carried by the traffic, directly locate the satellite above the destination terminal and tunnel the traffic to it. In addition, to reduce the satellite's burden, we add a DNS-like querying mechanism by offloading the mapping of terminal IPs and geographical locations to the ground station. The evaluation shows that Gaia outperforms Mobile IP and LISP-LEO in both end-to-end latency and on-board resource consumption.

With the emergence of time-sensitive IIoT applications, such as remote operation and industrial control, a long-distance deterministic forwarding service is highly desirable. However, most of the existing research is limited to local area networks, or requires costly replacement of core network devices. Enabling incremental deterministic networks based on off-the-shelf technologies is a significant challenge. This paper designs a core-agnostic and cost-effective solution named Hirail to achieve the smooth evolution of long-distance deterministic networks. Firstly, we investigate that a time-discrete shaper (TDS) can be deployed at the ingress node to enable millisecond-level bounded delay. TDS functions similarly to the concept of buying time-stamped tickets for each flow prior to getting on a high-speed rail, thus avoiding the expensive modification of core devices. Then, to alleviate the flow aggregation problem under long-distance links, we utilize the inband network telemetry to construct the delay-aware network map and conduct adaptive source routing based on the map. Finally, an adjustable buffer at the last hop is devised for jitter reduction. Evaluation results show that Hirail can meet the bounded delay and jitter demands, and outperforms other solutions in terms of performance and overhead.

In situ flow information telemetry (IFIT) is a state-of-the-art in-band telemetry framework for operator networks and can serve as the information foundation for network intelligence in the emerging sixth-generation regime. However, the performance advantage of IFIT comes at the cost of excessive telemetry data notification overhead, which makes it challenging to promote IFIT extensively. Therefore, we propose an approach named APN-integrated IFIT information notification (AIIN) to provide data notification overhead adaptability to IFIT. AIIN introduces a requirement-aware capability and reactive differentiated treatment into IFIT. In AIIN, we first enhance application-aware networking (APN) and integrate it into IFIT notification to support the explicit expression of data notification requirements. Then, oriented toward the different timeliness (telemetry data lag time) and accuracy (telemetry data retention rate) requirements expressed in APN, we design different behavioral treatment models to define reactive functions and procedures to make network devices explicitly process these requirements without decisions. The AIIN prototype is implemented on P4 switches. We also deploy the prototype on the China Environment for Network Innovation (CENI) network. Emulation results show that AIIN can achieve nanosecond line speed performance with differentiated and reactive data notification overhead reduction and, in the best case, can reduce bandwidth occupation by approximately 84%.

In-band Network Telemetry (INT) enables hop-by-hop device-internal state exposure for maintaining and troubleshooting data center networks. To achievenetwork-widetelemetry coverage, orchestration on top of the INT primitive is required. A straightforward solution would flood the network with INT probe packets for maximum measurement coverage, which leads to a huge bandwidth overhead. A refined solution leverages the SDN controller to collect the network topology information and carry out centralized probing path planning, which, however, is inefficient in reacting to topology changes. To tackle the above problems, we proposeINT-label, a lightweight In-band Network-Wide Telemetry architecture via the distributed labeling approach. INT-label periodically labels the sampled packets with device-internal states. It is cost-effective with a minor bandwidth overhead and able to seamlessly adapt to topology changes. In order to reduce the number of labeled packets, we introduce a times-based probabilistic labeling algorithm, which allows fewer packets to carry more INT information than the interval-based algorithm. In addition, to counteract the degradation of telemetry resolution due to loss of labeled packets, we design a feedback mechanism which can adaptively change the instant labeling frequency. We provide theoretical proof that INT-label can achieve network-wide telemetry. We analyze the impact of transmission delay on coverage rate and labeling times distribution under the INT-label architecture. Evaluation on software P4 switches suggests that INT-label can achieve 99.72% measurement coverage under the labeling frequency of 20 times per second. With the adaptive labeling enabled, even if 60% of the packets are lost, the coverage can still reach 92%.

Joint resource allocation in integrated networking, computing, and caching frameworks has attracted plenty of attention. In software-defined service-centric networking (SDSCN), we investigate an energy cost economical functionality embedding (FE) problem. The FE problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is difficult to obtain its global optimal solutions. In this paper, we propose a low-complexity high-performance algorithm for energy-economical FE design in large-scale SDSCN systems by leveraging the alternating direction method of multipliers (ADMM) together with successive convex approximation (SCA). In specific, the FE problem is first approximated as a sequence of convex subproblems via SCA. Each convex subproblem is then reformulated as a novel ADMM form to enable parallel computations and closed-form solutions. Numerical results show that our fast algorithm reduces the complexity by orders of magnitude and obtains favorable performances compared with state-of-the-art algorithms.

In-band Network Telemetry (INT) empowers high-resolution network monitoring by collecting hop-by-hop device-internal states through the data plane without frequently disturbing the control plane. To achieve network-wide monitoring, a high-level orchestration is made to provision multiple monitoring paths to cover the entire network. The path number and path overlapping are kept minimum to maximally reduce the telemetry overhead. However, in production deployment, except for the telemetry overhead, the telemetry timeliness is equally important for fine-grained monitoring, which creates new requirements of balanced monitoring path planning. Given the INT probes from multiple paths are collected to the central controller for analysis, the late arrival of even one probe will delay the analysis process and affect the monitoring timeliness. To address the problem, we propose INT-balance, a novel path planning algorithm for balanced INT path generation. In INT-balance, we first break the original network graph into multiple path segments at the odd vertices. Then, we iteratively splice the two shortest path segments with the joint endpoints to form a longer path segment until the path segment number reaches half the number of the odd vertices. INT-balance generates the minimum number of INT paths with well-balanced path lengths, covering every edge of the network graph without any path overlapping. Evaluation on a network of 100 switches shows that the path length variance of INT-balance is 67% less than that of INT-path, while the algorithm execution time is increased only by 0.012s.

Off-the-shelf x86 servers are widely deployed as middleboxes in edge and public clouds, such as cloud gateways and load balancers. They follow the “run-to-completion” model and achieve parallel traffic processing by distributing packet flows across multiple CPU cores using the RSS (receive side scaling) capability of NICs. However, RSS can cause inter-core load imbalance as it conducts stateless hashing without considering the CPU core utilization. As a result, multiple heavy-hitter flows can potentially overload a single CPU core when they are hashed onto that core. In this research, we propose P4RSS, a load-aware intra-server load balancing solution that leverages the P4 data plane. Specifically, a P4 ASIC is placed in front of the CPU to perform stateful traffic load balancing among multiple CPU cores based on real-time monitoring of core utilization. In addition, flow affinity maintenance and heavy hitter throttling are also offloaded to the P4 ASIC to free up valuable CPU computing resources. P4RSS can be implemented in the form of either hyper-converged server switches or P4-based SmartNICs. Evaluation results demonstrate that P4RSS reduces the standard deviation of CPU core utilization by 22%~53% compared to RSS. This not only improves the stability of middleboxes but also allows for higher CPU utilization without overprovisioning.

The emerging time-critical applications pose intense demands for enabling large-scale deterministic networks. In this paper, we propose a new Programmable Cycle-Specified Queue (PCSQ) for wide-area deterministic packet scheduling. We implement the first end-to-end high-precision rotation dequeuing, which enables microsecond-level time slot resource reservation (noted as T) and especially jitter control of up to 2T. We prototype the PCSQ scheduler on an FPGA. The PCSQ-enabled switches can guarantee bounded delay and jitter transmission on a realistic testbed.

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from remote centers to the edge of the network. However, with its widespread deployment, new challenges arise in terms of training efficiency and service of quality (QoS). Massive repetitive model training is ubiquitous due to the inevitable needs of users for the same types of data and training results. Additionally, a smaller volume of data samples will cause the over-fitting of models. To address these issues, driven by the Internet of intelligence, this paper proposes a distributed edge intelligence sharing scheme, which allows distributed edge nodes to quickly and economically improve learning performance by sharing their learned intelligence. Considering the time-varying edge network states including data collection states, computing and communication states, and node reputation states, the distributed intelligence sharing is formulated as a multi-agent Markov decision process (MDP). Then, a novel collective deep reinforcement learning (CDRL) algorithm is designed to obtain the optimal intelligence sharing policy, which consists of local soft actor-critic (SAC) learning at each edge node and collective learning between different edge nodes. Simulation results indicate our proposal outperforms the benchmark schemes in terms of learning efficiency and intelligence sharing efficiency.

Time-Sensitive Networking (TSN) is emerging to support critical real-time applications in Industry 4.0. Recent proposals leverage Cyclic Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in TSN. However, the CQF is not flexible enough in two aspects. First, it cannot achieve zero jitter. The Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows require zero jitter. Second, it may require setting the maximum queue length to a fixed value in advance and scheduling the flows offline, which is challenging for dynamic traffic scheduling. In this paper, we firstly present a time-aware cyclic-queuing (TACQ) mechanism to enable zero jitter for CQF. TACQ consists of a novel no-wait shaper (NWS) and a cyclic-queuing shaper (CQS). The NWS handles isochronous flows by strictly limiting the transmission time of flows that do not overlap on each output port and each period. The CQS is extended from CQF to schedule cyclic flows. Then, we propose a variable time slot mechanism and a novel incremental routing and scheduling (IRAS) algorithm based on software-defined networking (SDN) to online schedule dynamic flows. Simulation results show that TACQ significantly reduces the delay of isochronous flows and achieves zero jitter compared with CQF. And the IRAS algorithm approaches 96.1% of the optimal solution in scheduling 2000 flows with a feasible per-flow computational time.

With the rapid development of the Industrial Internet of Things (IIoT), massive IIoT devices connect to industrial networks via wired and wireless. Furthermore, industrial networks pose new requirements on communications, such as strict latency boundaries, ultra-reliable transmission, and so on. To this end, time-sensitive networking (TSN) embedded fifth-generation (5G) wireless communication technology (i.e., TSN-5G networks), is considered the most promising solution to address these challenges. TSN can provide deterministic end-to-end latency and reliability for real-time applications in wired networks. 5G supports ultra-reliable and low-latency communications (uRLLC), providing increased flexibility and inherent mobility support in the wireless network. Thus, the integration of TSN and 5G provides numerous benefits, including increased flexibility, lower commissioning costs, and seamless interoperability of various devices, regardless of whether they use a wired or wireless interface. Nonetheless, the potential barriers between the TSN and 5G systems, such as clock synchronization and end-to-end traffic scheduling, are inevitable. Time synchronization has been studied in many works, so this paper focuses on the end-to-end traffic scheduling problem in TSN-5G networks. We propose a novel integrated TSN and 5G industrial network architecture, where the 5G system acts as a logical TSN-capable bridge. Based on this network architecture, we design a Double Q-learning based hierarchical particle swarm optimization algorithm (DQHPSO) to search for the optimal scheduling solution. The DQHPSO algorithm adopts a level-based population structure and introduces Double Q-learning to adjust the number of levels in the population, which evades the local optimum to further improve the search efficiency. Extensive simulations demonstrate that the DQHPSO algorithm can increase the scheduling success ratio of time-triggered flows compared to other algorithms.

The issue of high energy consumption and low energy utilization in data center networks (DCNs) has always been the focus of attention of both academia and industry. One general solution is to select a subset of network devices that can meet the traffic transmission requirements, thereby turning off the remaining redundant devices. However, modeling the problem as integer linear programming introduces significant time overhead, while heuristic approaches often suffer from poor generalizability. In this paper, we propose GreenDCN.ai, a closed-loop control system, which utilizes In-band Network Telemetry to collect the network-wide device-internal state, and leverages a Deep Reinforcement Learning-based energy-saving algorithm to make rapid decisions to turn on or off network device ports in response to the real-time network state. The trained GreenDCN.ai can adaptively adjust its energy-saving strategy without human intervention when the DCN topology changes. Besides, based on the regularity of the DCN topology, we design two training complexity reduction methods to address the non-convergence issue under large-scale DCN topologies. Specifically, we split the large-scale DCN topology into sub-topologies for parallel training on each sub-topology without breaking the DCN topology connectivity. Evaluation on software P4 switches suggests that GreenDCN.ai can achieve stable convergence within 590 episodes, generate effective action decisions within$\boldsymbol{79}\upmu\mathrm{s}$, and save about 34% to 39% of the network energy consumption.

The virtual cloud network has been the first choice for most enterprises to expand local networks due to its convenience, flexibility, and elasticity. Cloud gateways are the key to steering traffic among these virtual networks, which require high throughput and low delay, usually in Tbps and microseconds (us), to improve the performance of cloud regions. However, as cloud traffic growth far exceeds Moore's law, previous software cloud gateways are facing the performance bottleneck that they are vulnerable to the attack of heavy-hitter flows. In this paper, we propose a high-performance and low-cost software cloud gateway for accelerating virtual cloud networks. By revisiting the Vector Packet Processing (VPP) framework, we design a custom control plane to enable various network functions of the cloud gateway, which enhances the routing and forwarding actions in the data plane. And we also provide a common interface for users to flexibly configure and manage the gateway. This pure software architecture has a low deployment cost. Compared with other software gateways with roughly the same price, the processing performance is close to the NIC's line speed, which is suitable for high concurrency cloud scenarios.

In space-terrestrial integrated networks, the relative motion between LEO satellites and ground terminals is inevitable, which will trigger the reassignment of the terminal IP addresses and disrupt the ongoing TCP connections. Traditional Mobile IP protocol can solve the problem by using the home agent and the tunneling mechanism. However, for space-terrestrial integrated networks, Mobile IP is inefficient as it introduces (1) increased latency when registering with the remote home agent, (2) high packet loss due to large registration latency, (3) triangular routing to the remote home agent. To address the above issues, we propose LISP-LEO, a location/identity separation-based mobility management protocol for LEO satellite networks. Specifically, (1) we divide the Earth's surface into partitions and maintain a partition-satellite mapping table in real-time according to the regularity of satellite motion, (2) we always route traffic to the satellite above the destined terminal by querying the partition-satellite mapping table, which eliminates triangular routing and the related performance overheads, (3) we handle the corner case that multiple satellites occur above the destined terminal by proposing last-hop relay. The evaluation convinces that, for the LEO-48 constellation, LISP-LEO produces a 55.0% reduction in the RTT and a 45.8% reduction in the number of forwarding hops in the worst routing case compared with Mobile IP.

Recently, there are significant advances in networking, computing, and caching (NCC). Nevertheless, few attempts have been made to explore the potential of the promising server-less computing paradigm in NCC integrated frameworks. In this paper, we consider a software-defined serverless service-centric networking (SD-SSCN) framework that not only dynamically orchestrates NCC by combining software-defined networking and service-centric networking technologies but also strongly focuses on the context of serverless computing. To achieve a cost-efficient green SD-SSCN system, we first formulate the joint resource allocation problem to minimize an overall average cost model. We derive this problem as a nonlinear integer programming problem, then we relax it as a quadratic programming problem and develop a primal-dual interior-point algorithm to find joint resource allocation solutions. Simulation results show that our proposed SD-SSCN framework significantly outperforms the traditional networks in terms of the average cost in the context of serverless computing.

With the low latency and high coverage, the low earth orbit (LEO) satellite systems are attracting more and more venture capitals as well as research attentions. Due to their highly dynamic constellation topologies, routing protocols on the ground have to be tailored to efficiently adapt to the regular topology changes. However, for irregular topology changes caused by exceptional link failure/recovery, network-wide route flooding is still necessary for route convergence. But, this will cause significant traffic flooding redundancy due to the high density of constellation topologies. For larger-scale constellations, the redundancy issue will be exacerbated. To lessen the redundancy, this work proposes a lightweight route flooding mechanism by generating a sparse flooding topology that prunes the original full-mesh topology, and only flooding the route information on the sparse topology. By considering the maximum flooding hop as well as the robustness of the flooding topology, we design an algorithm to calculate the optimal topology instead of just applying the minimum spanning tree. The evaluation shows that, for the LEO-96 constellation, the new flooding topology has a 28.1% reduction in the inter-satellite links (ISLs) compared with the original topology, and the new flooding mechanism has a 37.52% reduction in the traffic flooded and a 10.03% reduction in the route convergence time compared with OSPF. Such improvements will be amplified on larger-scale constellations.

Deterministic IP (DIP) networking is a promising technique that can provide delay-bounded transmission in large-scale networks. Nevertheless, DIP faces several challenges in the mixed traffic scenarios, including (i) the capability of ultralow latency communications, (ii) the simultaneous satisfaction of diverse QoS requirements, and (iii) the network efficiency. The problems are more formidable in the dynamic surroundings without prior knowledge of traffic demands. To address the above-mentioned issues, this paper designs a flexible DIP (FDIP) network. In the proposed network, we classify the queues at the output port into multiple groups. Each group operates with different cycle lengths. FDIP can assign the time-sensitive flows with different groups, hence delivering diverse QoS requirements, simultaneously. The ultra-low latency communication can be achieved by specific groups with short cycle lengths. Moreover, the flexible scheduling with diverse cycle lengths improves resource utilization, hence increasing the throughput (i.e., the number of acceptable time-sensitive flows). We formulate a throughput maximization problem that jointly considers the admission control, transmission path selection, and cycle length assignment A branch and bound (BnB)-based heuristic is developed. Simulation results show that the proposed FDIP significantly outperforms the standard DIP in terms of both the throughput and the latency guarantees.

User authentication on the blockchain has been considered a promising solution to secure communications in LEO satellite communication systems. Due to resource-limited LEO satellites, the blockchain needs to be deployed in the terrestrial network component of LEO satellite communication systems, consequently resulting in high authentication delays. To fill the gap, we propose to cache the blockchain at LEO satellites and update the blockchain periodically and design a delay-aware cooperative caching scheme for on-chain authentication by considering the query delay and the synchronization delay. Specifically, we first propose to divide LEO satellites into multiple clusters which have the same copy of all the blocks belonging to the blockchain. Then, we model the clustering problem as a coalition formation game. Afterward, we design a distributed delay-aware coalition formation algorithm, which is called DAC, to find an optimal coalition partition. Extensive simulation results show the efficacy of the proposed scheme.

Emerging latency-sensitive applications (e.g., industrial control, in-vehicle networks) require that the networks guaranteed data delivery with low, bounded latency. To meet this requirement, the IEEE 802.1 Working Group developed the time-sensitive networks (TSN) standard to enable deterministic communication on standard Ethernet. TSN technology is developed to enable deterministic communication using traffic scheduling and shaping technology. However, while the TSN standards define the mechanisms to handle scheduled traffic, it does not specify algorithms to compute fine-grained traffic scheduling policy. Current TSN flow scheduling schemes largely rely on a manual process, requiring knowledge of the traffic pattern and network topology features. Inspired by recent successes in applying reinforcement learning in online control, we propose a deep reinforcement learning aided no-waiting flow scheduling algorithm in TSN. Extensive simulations are performed to verify that our algorithm can find the optimal solution in an acceptable time.

In this article, we propose a multilayer Ka/Q-band satellite–terrestrial integrated network for the Internet of Remote Things (IoRT) to achieve a high transmission rate with communication robustness in dynamic network environments. Under this architecture, we investigate how to jointly manage the offloading path selection and resource allocation to offload computation-intensive and delay-sensitive tasks in the IoRT. Considering continuous low earth orbit (LEO) satellite movements and Markovian rainfall changes, the computation offloading problem is described as a Markov decision process (MDP) formulation with the objective of maximizing the number of offloaded tasks with satisfied delay requirements and minimizing the power consumption of the LEO satellites. A deep reinforcement learning (DRL) approach is leveraged to make optimal decisions by taking account of dynamic queues of IoRT devices, channel conditions that vary with rainfall intensities and satellite positions, and computing capabilities of ground stations. Extensive simulations are conducted to validate the effectiveness and superiority of our proposed scheme.

With the rapid development of wireless communication technologies, the emerging multimedia applications make mobile Internet traffic grow explosively while putting forward higher service requirements for the next-generation wireless networks. Therefore, how to achieve low-latency content transmission by effectively allocating heterogeneous network resources to improve the network quality of service and end-user quality of experience is a key issue to be solved urgently in the current Internet. In this article, we propose a deep reinforcement learning (DRL)-based resource allocation scheme to improve content distribution in a layered fog radio access network (FRAN). We formulate the optimal resource allocation problem as a minimal delay model, where in-network caching is deployed and the same content requests from mobile users can be aggregated in the queue of each base station. To cope with the increasing user requests and overcome capacity constraints of the FRAN, moreover, a cloud–edge cooperation offloading scheme is utilized in our model, where the integrated allocation of caching, computing, and communication resources and joint optimization between in-network caching and routing are considered to promote resource utilization and content delivery. In our solution, a new DRL policy is designed to make cross-layer cooperative caching and routing decisions for the arriving content requests according to request history information and available network resources in the system. Simulation results demonstrate that our proposed model can performs much better than the existing cloud–edge cooperation schemes in the FRAN.

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent$Q$-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

Relying on offloading computation tasks to the network edge, ultra dense networks (UDNs) are capable of providing delay-aware service to nearby users. Meanwhile, software defined networking (SDN) is deemed as an effective technology to ease the management of infrastructure plane and control plane in UDNs, which is termed as SDN-based ultra dense networks. Specifically, the centralized SDN controller is capable of managing the whole network globally. With the increasing demands for various applications as well as the limitation of computation, storage and communication resource, how to allocate spectrum resource appropriately is imperative. In this article, we mainly show solicitude for spectrum sharing and edge computation offloading problems in SDN-based ultra dense networks, constituted of various macro base stations (MBSs), small-cell base stations (SBSs) and user equipments (UEs). To address this issue, we propose a second-price auction scheme for ensuring the fair bidding for spectrum rent, which enables the MBS edge cloud and SBS edge cloud to occupy the channel in cooperative and competitive modes. Moreover, the MBS edge cloud is termed as the buyer, and the SBS edge clouds are the sellers who sell the offloading resource to the MBS edge cloud. To be specific, the spectrum sharing and computation offloading scheme is executed in the SDN controller, and the controller is responsible for distributing spectrum allocation instructions to the infrastructure plane. Finally, experimental results validate the effectiveness of our proposed scheme in SDN-based ultra dense networks.

Software-based virtual switches are indispensable in multi-tenant cloud networks. They either work as bridges between virtual machines and the underlying networks, or act as the virtual network function carriers for flexible service chaining and orchestration. Therefore, high-accuracy monitoring of virtual switches is significant for ease of data center network management. The recently proposed In-band Network Telemetry, which relies on the protocol-independent switch architecture (PISA), can achieve the monitoring requirements. However, not all the virtual switches with production quality are P4-based or built under the PISA. In this work, we provide the design and implementation of label-based INT and probe-based INT on top of OVS and VPP, the two mainstream software-based virtual switches with non-PISA architecture. Extensive evaluation shows that our implementation has low performance overhead in terms of forwarding latency, packet loss ratio and CPU consumption. Under 100Mbps traffic pressure, the CPU overhead of INT on OVS and VPP are less than 0.1% and 0.5%, and the switch latency are added by less than$4 \mu\mathrm{s}$and$3\mu\mathrm{s}$, respectively.

Recent proposals leverage Cyclic-Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in Time-Sensitive Networking (TSN). However, the Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows such as synchronized frames and motor control loops require zero jitter.We present TACQ, a first time-aware cyclic-queuing mechanism to support the co-transmission of cyclic flows and isochronous flows. Our key insight is that isochronous flows should enable zero-jitter while minimally impacting the bounded-delay of cyclic flows. To achieve this goal, we design a novel no-wait shaper (NWS) that handles isochronous flows with as little time-slot as possible. For cyclic flows, we extend the CQF to schedule flows with a double-closed state on the Tx-gate and compute the open time on the Rx-gate. Simulation results show that TACQ significantly reduces the delay of isochronous flows by 81.3% and achieves zero jitter compared with CQF. And the NWS effectively schedules 87.2% flows at 500 flows level in feasible execution time.

Recent proposals leverage Time-Aware Shaper (TAS) to achieve precise transmission in Time-Sensitive Networking (TSN). However, most of the proposals require the information of all time-triggered flows to be known in advance and synthesize the gate control list of each switch offline, making the mechanisms they designed inapplicable to industrial automation scenarios where the devices are changed dynamically and the flows should be scheduled online. In this paper, we propose an online routing and scheduling mechanism of TAS for time-sensitive networks. In order to maximize the number of schedulable flows and reduce bandwidth waste, we devise the variable time slot mechanism and minimize the sending start time of each flow. Based on these mechanisms, a novel incremental routing and scheduling (IRAS) algorithm is designed to achieve per-flow deployment, with a pre-routing algorithm to reduce synthesis time. The evaluations show that the IRAS algorithm approaches 96.5 % of the optimal solution in scheduling 2000 flows, and has a feasible per-flow computational time from sub-seconds to less than ten seconds.

SpaceX plans ambitiously to launch approximately 12,000 satellites from 2019 to 2024, expected to be a complement or even competitor to ground networks. However, the mega-scale satellite network is topology-varying and the frequency of inter-satellite link (ISL) handovers increases rapidly as the topology expands, which will further arouse a massive number of route updates with considerable packet travel delay or even packet loss during the route convergence. The classic Dijkstra's algorithm is adopted for space route calculation, however, it always selects the default shortest path from multiple equal-cost shortest paths between two satellite nodes. To reduce the route change as much as possible during the periodical topology change, in this work, we refined the original Dijkstra and propose StableRoute to select the most appropriate route from the equal-cost candidates with the least route updates compared with the routing table last round. In this way, the end-to-end paths can be maintained as far as possible without time-to-time oscillation. Evaluation shows that it reduces 41% of the route updates in a 36 × 36 topology compared with Dijkstra, and the reduction rate will rise persistently with the growth of the satellite constellation.

The In-band Network Telemetry (INT) enables hop-by-hop device-internal state exposure for reliably maintaining and troubleshooting data center networks. For achieving network-wide telemetry, orchestration on top of the INT primitive is further required. One straightforward solution is to flood the INT probe packets into the network topology for maximum measurement coverage, which, however, leads to huge bandwidth overhead. A refined solution is to leverage the SDN controller to collect the topology and carry out centralized probing path planning, which, however, cannot seamlessly adapt to occasional topology changes. To tackle the above problems, in this work, we propose INT-label, a lightweight In-band Network-Wide Telemetry architecture via interval-based distributed labelling. INT-label periodically labels device-internal states onto sampled packets, which is cost-effective with minor bandwidth overhead and able to seamlessly adapt to topology changes. Furthermore, to avoid telemetry resolution degradation due to loss of labelled packets, we also design a feedback mechanism to adaptively change the instant label frequency. Evaluation on software P4 switches suggests that INT-label can achieve 99.72% measurement coverage under a label frequency of 20 times per second. With adaptive labelling enabled, the coverage can still reach 92% even if 60% of the packets are lost in the data plane.

In-band Network Telemetry (INT) enables fine-grained network monitoring to ease the management of large-scale networks, which, however, relies on the real-time collection of a huge amount of telemetry data through the southbound interface. For example, the INT telemetry data upload rate of a 28-pod FatTree topology reaches 3Tbps under a probe frequency of 100 times/s, which is rather unacceptable since the controller-switch link bandwidth is limited. To mitigate the telemetry data collection overhead, in this work, we propose INT-filter, a novel measurement architecture that deploys the same prediction algorithm on both the data plane and the control plane to predict the traffic state in the near future instead of uploading all the telemetry data. Such prediction-based approach leverages the observation that there is considerable redundancy in the telemetry data sequence. In addition, we design an integration mechanism that conducts predictions using multiple methods simultaneously and uploads the predicted result from the least-error method to further decrease the upload volume. Extensive evaluation suggests that INT-filter can achieve at least 33.6% data collection decrease under a 10ms probe interval. With prediction integration, the upload reduction can further reach 58.5%.

On-path caching is a building block in Named Data Networking that helps eliminate redundant traffic. The performance of redundancy elimination depends on both Content Store (CS) and Pending Interest Table (PIT), i.e., CS caches content for future reuse, and PIT aggregates repetitive requests in a short period. However, contemporary proactive caching strategies only take account of CS while neglecting PIT. In this work, we integrate both PIT and CS into the proactive caching model, derive how to calculate aggregated request rate, and propose an algorithm to calculate the aggregated request rate across the tree topology. Then we formulate caching placement into optimization problems and solve them with a decomposition-based evolutionary algorithm. The simulation results show that the proposed scheme outperforms conventional solutions.

Network slicing (NS) is a promising technology of 5G that provides customized end-to-end network service to multi-tenant. How to improve resource utilization efficiency with guarantee of performance isolation in a shared infrastructure is one of the main challenges in resource allocation problem of NS. To address this challenge, we characterize the degree of performance isolation based on the relationship between the requested resource amount, the allocated resource amount, and the time-varying network loads. We propose a dynamic resource allocation problem with probabilistic isolation guarantee (DRAIG), which is formulated as a chance constrained program. As the true probability distribution of network loads is usually unknown, we use the Conditional Valuate-at-Risk (CVaR) measure to provide a distributionally robust formulation that approximate the basic chance constraints of DRA-IG in a data-driven manner. We estimate the second-order moment of network loads by the periodic history information. Then, we further reformulate the distributionally robust optimization problem as a tractable semidefinite programming (SDP). Finally, numerical evaluation verifies the effectiveness of the proposed method.

To meet the growing demand for high bandwidth of Multimedia network, IP Network Providers spend millions of dollars overprovisioning bandwidth of their network. However, due to the lack of reasonable traffic scheduling, the over-provisioning network still has a severe issue of utilization imbalance. Traffic Engineering (TE) is proposed to solve this problem. Network measurement and routing optimization strategies are two key components of TE. Effective real-time network measurement provides the basis for the generation of route optimization strategies, which makes the network congestion-aware. Existing out-band network telemetry that transmits extra probes to measure network status has the problem of inaccurate measurement information in the network. Besides, the relationship between complex network status and routing optimization strategy is difficult to describe with an exact mathematical model. Therefore, we propose a novel TE approach, which is called DPRO. It combines In-band Network Telemetry based on programmable language P4 with Reinforcement Learning to minimize network max-link-utilization. Extensive experiments show that our approach significantly outperforms several widely-used baseline methods in terms of max-link-utilization.

Network reliability becomes increasingly important in modern data center networks (DCNs). The DCNs are expected to work sustainably under internal failures and assist network operators in troubleshooting them rapidly. However, some network failures will happen silently with packets discarded without producing any explicit notification before causing tremendous damage to the network. To troubleshoot these "gray failures", in this work, we present a rapid gray failure detection and localization mechanism based on the recently proposed In-band Network Telemetry (INT). Specifically, we leverage simplified INT probe packets to conduct network-wide telemetry to help the servers under ToR switches obtain all the feasible paths between sources and destinations. Once a network failure occurs, the affected thus unavailable paths will immediately be detected and flushed out of the path information table at each server by a timeout mechanism. Hence, servers can proactively perform source routing-based fast traffic reroute to avoid massive packet loss and retain uninterrupted quality of experience. At the meantime, all the aged path entries will be uploaded to a remote controller for centralized failure localization by identifying common path elements. To verify the feasibility of our design, we build a virtual network testbed with software P4 switches and a Redis database. Evaluation shows that our system can successfully detect network gray failures and reroute the affected traffic in no time while complete failure localization within only a few seconds.

Recently, fog computing has emerged as a prospective technique to provide pervasive and agile computation services for Internet-of-Things (IoT) devices and support advanced applications. Introducing the energy harvesting (EH) technique into the fog computing system can extend the battery lifetime and provide a higher quality of experiences (QoE) for IoT devices. In the EH-enabled IoT fog system, computation offloading is an important issue and has attracted much attention. In most existing works, it is assumed that the IoT device is fully aware of the system state. However, in practical offloading problems, the IoT device may not be able to obtain accurate system state information, and only have a partial observation of the environment. Therefore, in this article, we investigate the decentralized partially observable offloading problem in the EH-enabled IoT fog system, in which multiple IoT devices cooperate to maximize the network performance while meeting their QoE requirements. We formulate the optimization problem as a decentralized partially observable Markov decision process (Dec-POMDP) in which each IoT device makes the task offloading decisions according to its local observation of the environment. The Lagrangian approach and the policy gradient method are adopted to find the optimal solution for the proposed problem. Due to the high complexity of solving the Dec-POMDP, a learning-based decentralized offloading algorithm with low complexity is presented to find the approximate optimal solution. Finally, extensive experimental evaluation and comparison are carried out to show the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

Large-scale services are generally hosted on multiple application servers to scale out in today's data centers. Load balancers distribute users' requests across these servers. Software load balancer and switch-based load balancer are two typical classes of load balancers. However, most of the existing mechanisms either exhibit high processing latency at load balancers or likely lead to unbalanced requests distribution without considering the disparity of the application servers. In this paper, we study how the disparity of application servers significantly impacts the response time of requests. A fast switch-based Load Balancer considering Application Server states (LBAS) then is proposed to minimize the processing latency at both load balancers and application servers. The data plane of LBAS is well designed to store millions of connections in limited storage capacity without violating per-connection consistency. Besides, a partial dynamic weighting algorithm based on the Ridge Regression theory is designed and implemented to decrease the processing latency at application servers. We implement LBAS using the P4 programming language and conduct a series of extensive experiments to evaluate the performance. The results demonstrate that the proposed LBAS mechanism significantly reduces the response time of requests compared with Uniform random, Static weight, and Spotlight in various scenarios.

Device-to-Device (D2D) caching assists Mobile Edge Computing (MEC) based caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this paper, a Blockchain-based Cache and Delivery Market (CDM) is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and MEC caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on transactions and prevent frauds, a consensus protocol among the smart contract execution nodes (SCENE) is necessary. To minimize the latency of reaching consensus while guaranteeing its confidence level, we propose partial Practical Byzantine Fault Tolerance (pPBFT) protocol. Further, the model of cache sharing and transaction execution consensus is proposed, and we further formulate caching placement and SCENE selection as Markov Decision Process problems. Due to the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problem. The simulation results show that the proposed schemes outperform conventional solutions in terms of traffic offloading, content retrieval latency, and consensus latency.

With global coverage as well as ultra-low latency, the Low-Earth-Orbit (LEO) satellite constellation is regarded as an ideal complement to the terrestrial network infrastructure. One technical issue in LEO satellite networks is efficient and resilient routing. Considering the periodic topology changes, straightforwardly leveraging terrestrial routing protocols, such as OSPF, will incur endless route convergence, consuming expensive inter-satellite link bandwidth. Prior work proposes several snapshot-based routing approaches, which either require to store a sequence of routing table snapshots in limited satellite memory, or have to maintain frequent interaction with the ground stations. In this work, we propose OPSPF, a novel routing protocol dedicated to LEO satellite networks. OPSPF takes advantage of the regularity of the constellation and conducts periodic route calculation for instantaneous routing table generation, which well handles the regular topology changes. Moreover, OPSPF proposes an on-demand dynamic routing mechanism, dedicated to the irregular topology changes caused by link failure/recovery. Evaluation shows, compared with OSPF, OPSPF has zero route convergence overhead during regular topology changes and 57% reduction of the communication overhead and 82% reduction of the route convergence time during irregular topology changes.

With the ever-increasing complexity of networks, fine-grained network monitoring enables better network reliability and timely feedback control. The In-band Network Telemetry (INT) allows cost-effective network monitoring by encapsulating device-internal states into probe packets. However, INT only specifies an underlying device-level primitive while how to achieve network-wide traffic monitoring remains undefined. In this work, we propose INT-path, a network-wide telemetry framework, by decoupling the system into a routing mechanism and a routing path generation policy. Specifically, we embed source routing into INT probes to allow specifying the route the probe packet takes through the network. Above the mechanism, we develop an Euler trail-based path planning policy to generate non-overlapped INT paths that cover the entire network with a minimum path number. Besides, an exhaustive analysis of algorithm's run-time complexity is also provided. INT-path can “encode” the network-wide traffic status into a series of “bitmap images”, transforming network troubleshooting into pattern recognition problems. INT-path is very suitable for deployment in data center networks thanks to their symmetric network topologies.

Network Function Virtualization (NFV) turns a sequence of network functions on hardwares into a service chain of virtual network functions (VNFs) provisioned on virtual machines or containers. However, the chain of VNFs may suffer from interruption as long as one VNF fails due to software faults or hardware malfunctions. A common approach to ensuring high availability is to provide backup nodes for primary VNFs. However, existing work on allocating backup nodes have not considered the heterogeneous resource demands of different VNFs. In this paper, we formalize the resource-aware backup allocation problem, which aims to minimize the backup resource consumption while meeting the overall availability demand. To this end, we prove the NP-hardness of this problem and propose the RABA-CDDE algorithm based on differential evolution to solve it. Besides, to reduce the computation overhead of RABA-CDDE, a greedy algorithm is proposed. Our extensive evaluation shows that the proposed algorithms can reduce the resource consumption by about 15% and 35% respectively compared to the state-of-art solutions in dedicated and shared protection scenarios.

Computation offloading has been considered as one of the key research issues in edge computing fields. In order to reduce the energy consumption of the mobile terminal, the energy efficiency issue of computation offloading has attracted a lot of attention from academia and industry. In this study, the authors propose an energy‐efficient computation offloading scheme in 5G cellular networks with edge computing and device‐to‐device (D2D) communications. They consider the computation offloading to fog computing devices via D2D communications and mobile edge computing (MEC) servers via cellular networks. And thus the computation task execution model can be composed of local execution, fog computing device execution and MEC server execution. Then, they formulate the computation offloading issue as stochastic optimisation problem, and use the Lyapunov optimisation technology framework to solve this problem. Finally, extensive simulation results are presented to illustrate the effectiveness of the proposed scheme.

Intrusion detection technology has received increasing attention in recent years. Many researchers have proposed various intrusion detection systems using machine learning (ML) methods. However, there are two noteworthy factors affecting the robustness of the model. One is the severe imbalance of network traffic in different categories and the other is the nonidentical distribution between training set and test set in feature space. This paper presents a multilevel intrusion detection model framework named multilevel semi-supervised ML (MSML) to address these issues. The MSML framework includes four modules: 1) pure cluster extraction; 2) pattern discovery; 3) fine-grained classification (FC); and 4) model updating. In the pure cluster module, we introduce an concept of “pure cluster” and propose a hierarchical semi-supervised k-means algorithm with an aim to find out all the pure clusters. In the pattern discovery module, we define the “unknown pattern” and apply cluster-based method aiming to find those unknown patterns. Then a test sample is sentenced to labeled known pattern or unlabeled unknown pattern. The FC module can achieves FC for those unknown pattern samples. The model updating module provides a mechanism for retraining. KDDCUP99 dataset is applied to evaluate MSML. Experimental results show that MSML is superior to other existing intrusion detection models in terms of overall accuracy, F1-score, and unknown pattern recognition capability.

Traditional networks face many challenges due to the diversity of applications, such as cloud computing, Internet of Things, and the industrial Internet. Future Internet needs to address these challenges to improve network scalability, security, mobility, and quality of service. In this work, we survey the recently proposed architectures and the emerging technologies that meet these new demands. Some cases for these architectures and technologies are also presented. We propose an integrated framework called the service customized network which combines the strength of current architectures, and discuss some of the open challenges and opportunities for future Internet. We hope that this work can help readers quickly understand the problems and challenges in the current research and serves as a guide and motivation for future network research.

With the development of network function virtualization (NFV), service function chains (SFCs) are deployed via virtual network functions (VNFs). In general, the SFCs are served via composition and then deployed into data center infrastructures. However, most of the existing works neglect SFC composition. Furthermore, they consider that VNF instances are independently deployed for each SFC, which may underutilize the computational power of servers. We consider, for each required VNF in the chain, the operator can either place it on a new instance or assign it to an established instance if the residual resource of that instance is sufficient. Such a deployment scheme can leverage resources more efficiently and we define it as SFC placement and assignment. In this paper, we first combine SFC composition, placement and assignment together to enhance resource allocation. We present the system model and formulate the problem as 0-1 integer programming. We aim to improve the VNF instance utilization as well as reduce the link consumption. A heuristic approach called Jcap is developed to solve the problem in two stages. The simulations show that Jcap achieves competitive performance with the optimal results obtained from mathematical model.

Caching in mobile networks can reduce the redundant data transmission and cope with the challenge of the explosive growth of mobile data traffic. It has been considered as a promising technology in 5G networks and has been attracting a lot of attention in recent years. Although many existing works have addressed the content placement problem or the cache optimisation problem, most of them do not consider the issue of hierarchical collaborative caching. Collaborative caching can further alleviate the traffic pressure and reduce the user‐perceived latency by reducing duplicate content transmission. Therefore, in this study, the authors consider a hierarchical collaborative caching framework with the cache deployment at the distributed gateway and mobile edge computing servers, and then design a novel caching strategy based on this framework. They formulate the hierarchical collaborative content placement problem as an optimisation problem to maximise the latency saving under the constraint of limited cache capacity. Since finding the optimal solution is an NP‐hard problem, they propose a genetic placement algorithm to find the near‐optimal solution to reduce the computation complexity. Numerical experiment results show that the proposed algorithms can significantly improve the performance compared with the reference algorithms.

The past few years have witnessed a wide deployment of software defined networks facilitating a separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in software defined networking networks. NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies, such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision. Simulation results demonstrated the effectiveness of NetworkAI.

Network virtualization can offer more flexibility and better maintainability for the current Internet through allowing multiple heterogeneous virtual networks (VNs) to share the network resource of a common infrastructure provider. The main challenge in this respect is the efficient embedding the virtual nodes and virtual links from the VN requests onto the limited substrate network resources. The notion of storage resource can exchange bandwidth resource to some extent gives us a hint that the efficient utilization of storage resource can relieve the bandwidth resource consumption. The existing VN embedding model does not consider the storage resource constraints on substrate nodes and virtual nodes, and does not keep up with the need of actual situation. In this paper, we propose a novel VN embedding model based on 3-D resource constraints including computing, network and storage, and devise two heuristic algorithms as the baseline algorithms to deal with the VN embedding problem. To our best of our knowledge, this is the first time to propose VN embedding problem based on 3-D resources including computing, network, and storage.

Although some excellent works have been done on networking, caching, and computing, these three important areas have traditionally been addressed separately in the literature. In this paper, we describe the recent advances in jointing networking, caching, and computing and present a novel integrated framework: software-defined networking, caching, and computing (SD-NCC). SD-NCC enables dynamic orchestration of networking, caching, and computing resources to efficiently meet the requirements of different applications and improve the end-to-end system performance. Energy consumption is considered as an important factor when performing resource placement in this paper. Specifically, we study the joint caching, computing, and bandwidth resource allocation for SD-NCC and formulate it as an optimization problem. In addition, to reduce computational complexity and signaling overhead, we propose a distributed algorithm to solve the formulated problem, based on recent advances in alternating direction method of multipliers (ADMM), in which different network nodes only need to solve their own problems without exchange of caching/computing decisions with fast convergence rate. Simulation results show the effectiveness of our proposed framework and ADMM-based algorithm with different system parameters.

In this work, we propose OpenSched, a layered architecture that glues the QoS apps, the controller and the switches together to maximally unleash the power of centralized QoS control. Specifically, our design consists of (1) a flexible northbound interface via the ``builder pattern'', (2) one-to-many controller-switch interactions via device abstraction, thread pooling and Java NIO's selector mechanism, and (3) efficient southbound protocol handling as well as QoS policy execution via a producer-consumer model at the switch side. We build a prototype based on ONOS and OVS with 2340 lines of Java code and 1097 lines of c code. OpenSched is expected to facilitate flexible network resource provisioning.

We propose a novel framework called Software Defined Networking, Caching and Computing (SD-NCC) which integrates networking, caching and computing in a systematic way to improve the end-to-end system performance. In SDNCC, the more in-network resources it utilizes, the less network usage it costs under the same service demands. However only minimizing the total network usage leads to bottlenecks in the network, making the network fragile to traffic bursts. In this paper, we study the joint networking, caching and computing resource allocation issue and formulate it as an optimization problem to make a trade off between minimizing network usage and balancing servers' load. In addition, taking into consideration the inaccurate measurement of network state information (NSI), we reformulate this problem under imperfect NSI. Because the joint allocation problems with imperfect NSI are large-scale combinational optimization problems, we propose a discrete stochastic approximation(DSA) algorithm to deal with it. Finally, simulations are conducted to demonstrate the effectiveness of proposed framework and algorithms. Simulation results show that SD-NCC can significantly improve the end-to-end performance by sharing the physical infrastructure and information resources. Besides, DSA algorithms can achieve near-optimal performance.

Named Data Networking (NDN) is a prominent fully- fledged Information-Centric Networking (ICN) architecture. NDN can help users to take advantage of multiple access networks in Heterogeneous Wireless Networks (HWNs) more efficiently than IP. In HWNs with NDN, which we call information-centric HWNs, jointly designing forwarding strategy and resource allocation has great potential to improve network performance, which is ignored in the literatures. To fill in this blank, we propose a jointly designed forwarding strategy and resource allocation algorithm called Dynamic Forwarding and Resource Allocation (DFRA) that can adapt variable wireless environment. We also establish the fundamental throughput limitations of information-centric HWNs and prove that DFRA is throughput-optimal. By the cooperation between forwarding strategy and resource allocation, DFRA enables users to utilize wireless communication resource in information-centric HWNs more efficiently. From simulation results, DFRA can provide larger network throughput, faster download speed and better fairness than forwarding strategy that doesn't explicitly cooperate with resource allocation.

With the ever-increasing demand for high quality video, mobile video transmission optimization over a limited wireless network capacity has attracted extensive attention. Scalable Video Coding (SVC) is a main solution to provide better Quality of Experience (QoE) by encoding each video into one mandatory base layer and several optional enhancement layers. Deployment of caching in wireless networks has been considered as another effective method to mitigate redundant data transmission over backhaul links and to reduce the end-to-end video transmission delay. Although some works have been done for layered video content over cellular networks with caching, most of them focus on video quality selection or video caching to optimize the users' QoE. The problem of energy- efficient content placement is largely ignored. To fill this gap, we focus on the problem of energy- efficient content placement for layered video content delivery over cellular networks in this paper. Our design objective is to maximize the energy cost savings. We formulate the energy- efficient content placement problem as a convex optimization problem. Then, by solving the optimization problem, we can obtain the optimal set of content placement parameters for the Mobile Network Operator (MNO) to design an optimal caching policy for layered video contents. Finally, simulation results are presented to show the performance of the proposed content placement scheme.

Recently, designing information-agnostic coflow scheduling mechanisms attracts much attention since by leveraging priority queues, they could reduce coflow completion time in data-parallel clusters without a priori knowledge, such as flow size, coflow size. However, existing information-agnostic mechanisms generally schedule coflows only according to the sent data size of different coflows and ignore other useful coflow-level attributes like width, length and communication patterns. In this paper, we investigate that the coflow completion time could be further decreased by jointly leveraging multiple coflow-level attributes. Based on this investigation, we present a Multiple-attributes-based Coflow Scheduling (MCS) mechanism to reduce the coflow completion time. In MCS, a Shortest and Narrowest Coflow First (SNCF) algorithm is designed to separate coflows based on their widths and estimated lengths at the start of a coflow. During the transmission of coflows, one type of demotion thresholds employed in previous coflow scheduling mechanisms is too crude for various coflows. Therefore, we proposed a double-threshold scheme to adjust the priorities of narrow (small coflow width) and wide (large coflow width) coflows according to different thresholds. Trace-driven simulations with production workloads show that MCS outperforms the previous information-agnostic scheduler Aalo, and reduces the coflow completion time of small coflows.

With the ever-increasing demand for high quality video, mobile video transmission optimization over limited wireless network capacity has been attracted extensive attention. HTTP Adaptive Bit Rate (ABR) streaming is a main solution to provide better Quality of Experience (QoE) by adapting multimedia content over wireless channels real-timely. Deployment of caching in wireless network has been considered as another effective method to mitigate redundant data transmission over backhaul links and to reduce the end-to-end video transmission delay. Although some works have been done for HTTP ABR streaming caching, they only consider the users' QoE. The problem of energy-efficient cache resource allocation is largely ignored. In this paper, we focus on the problem of optimal cache resource allocation for HTTP ABR streaming in cellular networks. Our design objective is to maximize both the users' QoE and energy cost saving. We formulate the content cache management problem as two sub-optimization problems. Then, by solving the two sub-optimization problems, we can obtain the optimal set of playback rates selected by users and the MNO's caching policy for each individual content. Finally, simulation results are presented to show the performance of the proposed cache resource allocation scheme.

ECN thresholds have limited operational range and very strict scope. Lower thresholds exacerbate the queue underflow while higher thresholds increase the queueing delays. In this paper, an Adaptive ECN (A-ECN) marking scheme is proposed to enhance the performance of ECN. A-ECN can adaptively adjust ECN marking thresholds in different scenarios to achieve good generality. Therefore, network operators can directly deploy A-ECN in various environments regardless of underlying queue types and bandwidth.

Network Function Virtualization has attracted attention from both academia and industry as it can help the service provider to obtain agility and flexibility in network service deployment. In general, the enterprises require their flows to pass through a specific sequence of virtual network function (VNF) that varies from service to service. In addition, for each VNF required in the coming service demands, the operator can either launch a new instance for it or assign it to an established instance. This makes the network service deployment tasks even more complicated. In this paper, we first propose a method based on min-K-cut to cluster the VNFs. With clustering results as guidance, we determine whether to launch or reuse the instance to improve utilization rate of the VNF instance. Furthermore, for purpose of decreasing link bandwidth occupation, we aggregate the instances that are deployed with VNFs from the same cluster into the same server or rack. We evaluate our approach considering the average link bandwidth occupied by every accepted demand, the instance utilization rate and the total number of served demands. The simulation shows that our approach reduces link occupation effectively, and, meanwhile, guarantees the VNF instance utilization rate advantageously.

Data transfer duration accounts for a great proportion of job completion time in big-data systems. To reduce the time spent on data transfer, some traffic scheduling mechanisms at coflow-level are proposed recently. Most of them abstract datacenter networks as an ideal non-blocking big-switch, and the bottleneck is located at egress or ingress ports of end-hosts instead of in networks. Thus, they mainly focus on how to allocate port capacities of end-hosts to jobs without considering innetwork congestion. However, link congestion frequently occurs in datacenter networks due to network oversubscription and load imbalance. When link congestion occurs, bottleneck locations will move from the ports of end-hosts to network links. In this paper, we design and implement SkipL, a congestionaware coflow scheduler which could detect congestion and schedules coflows at end-hosts to effectively reduce coflow completion time. In addition, to be easily deployed in cloud environments, SkipL does not require to control flow routes. SkipL prototype system is implemented in Linux. The results of experiments conducted in a real small testbed and simulations conducted in the flow-level simulator show that SkipL reduces the average Coflow Completion Time(CCT) compared to the per-flow fair sharing scheduling method and Varys.

With the continuing penetration of sensor devices and the development of wireless communication techniques, increasing number of applications involving context awareness in ubiquitous computing have been used in daily life. How to collect data from mobile devices at a low energy cost and to mine contextual habits of users remains a key challenge for ubiquitous computing. We proposed an efficient context- awareness computing middleware system, Senz. By leveraging high-efficiency mobile data transmission method, using high-performance context recognition algorithms, and combining mobile data with online third-party data, this middleware system can recognize various user behavior patterns reliably, accurately and efficiently. Experiments show that the Senz recognition accuracy of context activity is above 83% on average and the energy cost is relatively low. By integrating Senz SDK and cloud computing engine, developers can build rich user experience apps with better understanding of users' behavior data and providing various personalized contextual services.

Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocation in cloud computing is an NP‐hard problem. In this study, the authors conduct a reliability analysis of cloud services by applying a Markov‐based method. They formulate the cloud scheduling problem as a multi‐objective optimisation problem with constraints in terms of reliability, makespan, and flowtime. Furthermore, they propose a genetic algorithm‐based chaotic ant swarm (GA‐CAS) algorithm, in which four operators and natural selection are applied, to solve this constrained multi‐objective optimisation problem. Simulation results have demonstrated that GA‐CAS generally speeds up convergence and outperforms other meta‐heuristic approaches.

Network slicing has been considered as one of the key technologies in the next generation mobile network (fifth generation – 5G), which can create virtual network and provide customised services on demand. Most of the current work on network slicing mainly focuses on virtualisation technology, especially in virtual resource allocation. However, caching as a significant approach to improve the content delivery and quality of experience for end‐users has not been well considered in network slicing. In this study, the authors consider in‐network caching combining with network slicing, and propose an efficient caching resource allocation scheme for network slicing in 5G core network. They first formulate the caching resource allocation issue as an integer linear programming model, and then propose a caching resource allocation scheme based on chemical reaction optimisation (CRO) algorithm, which can significantly improve the caching resource utilisation. The CRO algorithm is a population‐based optimisation metaheuristic, which has advantages in searching optimal solution and computation complexity. Finally, extensive simulation results are presented to illustrate the performance of the proposed scheme.

As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-dataMWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.

In today’s networks, load balancing and priority queues in switches are used to support various quality-of-service (QoS) features and provide preferential treatment to certain types of traffic. Traditionally, network operators use ‘traceroute’ and ‘ping’ to troubleshoot load balancing and QoS problems. However, these tools are not supported by the common OpenFlow-based switches in software-defined networking (SDN). In addition, traceroute and ping have potential problems. Because load balancing mechanisms balance flows to different paths, it is impossible for these tools to send a single type of probe packet to find the forwarding paths of flows and measure latencies. Therefore, tracing flows’ real forwarding paths is needed before measuring their latencies, and path tracing and latency measurement should be jointly considered. To this end, FlowTrace is proposed to find arbitrary flow paths and measure flow latencies in OpenFlow networks. FlowTrace collects all flow entries and calculates flow paths according to the collected flow entries. However, polling flow entries from switches will induce high overhead in the control plane of SDN. Therefore, a passive flow table collecting method with zero control plane overhead is proposed to address this problem. After finding flows’ real forwarding paths, FlowTrace uses a new measurement method to measure the latencies of different flows. Results of experiments conducted in Mininet indicate that FlowTrace can correctly find flow paths and accurately measure the latencies of flows in different priority classes.

Recently, there are significant advances in the areas of networking, caching and computing. Nevertheless, these three important areas have traditionally been addressed separately in the existing research. In this paper, we present a novel framework that integrates networking, caching and computing in a systematic way and enables dynamic orchestration of these three resources to improve the end-to-end system performance and meet the requirements of different applications. Then, we consider the bandwidth, caching and computing resource allocation issue and formulate it as a joint caching/computing strategy and servers selection problem to minimize the combination cost of network usage and energy consumption in the framework. To minimize the combination cost of network usage and energy consumption in the framework, we formulate it as a joint caching/computing strategy and servers selection problem. In addition, we solve the joint caching/computing strategy and servers selection problem using an exhaustive-search algorithm. Simulation results show that our proposed framework significantly outperforms the traditional network without in-network caching/computing in terms of network usage and energy consumption.

Hypertext transfer protocol based (HTTP) adaptive streaming (HAS) of video over wireless networks has brings huge challenge for the mobile networks. Although some works have been done for video streaming delivery in heterogeneous cellular networks, most of them are focus on the video streaming scheduling or the caching strategy design. The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored. In this paper, the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied, we model the optimization problem as a mixed integer programming problem. To reduce the computational complexity, an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved. Then we use the many-to-one matching model to analyze the user association problem, and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

We present FDALB, a flow distribution aware load balancing mechanism aimed at reducing flow collisions and achieving high scalability. FDALB, like the most of centralized methods, uses a centralized controller to get the view of networks and congestion information. However, FDALB classifies flows into short flows and long flows. The paths of short flows and long flows are controlled by distributed switches and the centralized controller respectively. Thus, the controller handles only a small part of flows to achieve high scalability. To further reduce the controller's overhead, FDALB leverages end-hosts to tag long flows, thus switches can easily determine long flows by inspecting the tag. Besides, FDALB can adaptively adjust the threshold at each end-host to keep up with the flow distribution dynamics.

Computer networks have to support an everincreasing array of applications, ranging from cloud computing in datacenters to Internet access for users.In order to meet the various demands, a large number of network devices running different protocols are designed and deployed in networks.As a result, network management and the deployment of new protocols and applications are quite challenging.On one hand, network operators have to manage so many different network devices and manually configure these devices using different tools.On the other hand, vendors use different physical infrastructures as well as software interfaces to manufacture devices, which makes it difficult for researchers to implement new functions in devices.Therefore, network infrastructure and architecture design face great challenges.Software-defined networking (SDN) has been proposed as a new way to facilitate network evolution.SDN decouples the data and control planes, and removes the control plane from network hardware.In SDN, all the devices are controlled by a centralized controller through open protocols, such as OpenFlow, BGP, and NETCONF.Then, control functions are implemented in the centralized controller to realize operational efficiency and reduce costs.Thus, it dramatically simplifies the network, and brings many potential benefits in terms of network management, network virtualization, trouble shooting, and other

Deployment of caching in wireless networks has been considered an effective method to cope with the challenge brought on by the explosive wireless traffic. Although some research has been conducted on caching in cellular networks, most of the previous works have focused on performance optimization for content caching. To the best of our knowledge, the problem of caching resource sharing for multiple service provider servers (SPSs) has been largely ignored. In this paper, by assuming that the caching capability is deployed in the base station of a radio access network, we consider the problem of caching resource sharing for multiple SPSs competing for the caching space. We formulate this problem as an oligopoly market model and use a dynamic non-cooperative game to obtain the optimal amount of caching space needed by the SPSs. In the dynamic game, the SPSs gradually and iteratively adjust their strategies based on their previous strategies and the information given by the base station. Then through rigorous mathematical analysis, the Nash equilibrium and stability condition of the dynamic game are proven. Finally, simulation results are presented to show the performance of the proposed dynamic caching resource allocation scheme.

The proliferation of cloud services makes virtualization technology more important. One important feature of virtualization is live Virtual Machine (VM) migration. Two main metrics of evaluating a live VM migration mechanism are total migration time and downtime. Most existing literature on live VM migration focus on designing migration mechanisms to shorten the two metrics or making a tradeoff between them. Few of them can be applied to applications with delay requirements, such as a VM backup process that needs to be done in a specific time. This will negatively impact the user experiences and reduce the profit of cloud service providers. Besides, the frequently varied bandwidth required by the widely used pre-copy mechanism is difficult to be provided by current network technologies. In this work, we theoretically analyze how much bandwidth is required to guarantee the total migration time and downtime of a live VM migration, and then propose a novel transport control mechanism to guarantee the computed bandwidth. The experimental results demonstrate that the bandwidth obtained from the proposed reciprocal-based model guarantees the expected total migration time and downtime, and the proposed transport control mechanism ensures that the live VM migration flow obtains the expected bandwidth even if there are background flows.

In Content-Centric Networking (CCN), most existing works do not consider energy savings by turning off network devices in CCN. In this paper, we systematically analyze the energy efficiency problem in CCN by turning off the content routers and network links. We formulate the energy consumption issue as a Mixed Integer Linear Programming (MILP) model, and propose a centralized solution via spanning tree heuristic and a fully distributed consensus optimization algorithm via the alternating direction method of multipliers (ADMM) to solve the problem for CCN. By duplicating flow variables, the energy consumption problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Simulation results reveal that the proposed distributed algorithm is amenable to energy-efficient implementation, due to smaller amount of local information exchange at each iteration. Moreover, the proposed algorithm can converge to final status in a significantly smaller number of iterations compared to the method based on dual decomposition. In addition, our algorithm scales better to large networks and it does not require intensive finetuning of the step size.

As a great challenge of network virtualization, virtual network embedding/mapping is increasingly important. It aims to successfully and efficiently assign the nodes and links of a virtual network (VN) onto a shared substrate network. The problem has been proved to be NP-hard and some heuristic algorithms have been proposed. However, most of the algorithms use only the local information of a node, such as CPU capacity and bandwidth, to determine how to map a VN, without considering the topological attributes which may pose significant impact on the performance of the embedding. In this paper, a new embedding algorithm is proposed based on real-time topological attributes. The concept of betweenness centrality in graph theory is borrowed to sort the nodes of VNs, and the nodes of the substrate network are sorted according to the correlation properties between the former selected and unselected nodes. In this way, node mapping and link mapping can be well coupled. A simulator is built to evaluate the performance of the proposed virtual network embedding (VNE) algorithm. The results show that the new algorithm significantly increases the revenue/cost (R/C) ratio and acceptance ratio as well as reduces the runtime.

Due to the in-network caching capability, Content-Centric Networking (CCN) has emerged as one of the most promising architectures for the diffusion of contents over the Internet. Most existing works on CCN focus on network resource utilization, and the energy efficiency aspect is largely ignored. In this paper, we formulate the energy consumption issue as a Mixed Integer Linear Programming (MILP) problem, and propose a centralized solution via spanning tree heuristic and a fully distributed energy consumption optimization algorithm via dual decomposition (DD) to solve the problem for CCN. The dual decomposition method transforms the centralized energy consumption optimization problem into the router status, link status, and link flow subproblems. Simulation results reveal that the proposed scheme exhibits a fast convergence speed, and achieves superior energy efficiency compared to other widely used schemes in CCN.

Protocol feature words are byte subsequences within traffic payload that can distinguish application protocols, and they form the building blocks of many constructions of deep packet analysis rules in network management, measurement, and security systems. However, how to systematically and efficiently extract protocol feature words from network traffic remains a challenging issue. Existing n-gram approaches simply break pay-load into equal-length pieces and are ineffective in capturing the hidden statistical structure of the payload content. In this paper, we propose ProWord, an unsupervised approach that extracts protocol feature words from traffic traces. ProWord builds on two nontrivial algorithms. First, we propose an unsupervised segmentation algorithm based on the modified Voting Experts algorithm, such that we break payload into candidate words according to entropy information and provide more accurate segmentation than existing n-gram approaches. Second, we propose a ranking algorithm that incorporates different types of well-known feature word retrieval heuristics, such that we can build an ordered structure on the candidate words and select the highest ranked ones as protocol feature words. We compare ProWord and existing n-gram approaches via evaluation on real-world traffic traces. We show that ProWord captures true protocol feature words more accurately and performs significantly faster.

Everyday a large number of records of surfing internet are generated. In various situations when the authors are analysing internet data they do not know the cluster structure of the author's database of traffic features, such as when the border of cluster members is vague, and the clusters’ partitions have different shapes, how to establish an algorithm to solve the clustering problem? Adaptive clustering algorithms can meet this challenge. Moreover, how to determinate the number of clusters when not only fuzzy cluster but also hard cluster are used? To address those problems, a new cluster validity index is proposed in this study. The proposed index focuses on the information of the geometrical structure of dataset by analysing the neighbourhood of data objects, which makes the index independent of the traditional fuzzy membership matrix. The new index consists of two parts, namely the ‘compactness’ and ‘separation measure’. The compactness indicates the degree of the similarity among the data objects in the same cluster. The separation measure indicates the degree of dissimilarity among the data objects in different clusters. The performance of their proposed index is excellent underpinned by the outcomes from the experiments based on both artificial datasets and real world datasets.

The virtual network (VN) embedding/mapping problem is recognized as an essential question of network virtualization. The VN embedding problem is a major challenge in this field. Its target is to efficiently map the virtual nodes and virtual links onto the substrate network resources. Previous research focused on designing heuristic-based algorithms or attempting two-stage solutions by solving node mapping in the first stage and link mapping in the second stage. In this study, we propose a new VN embedding algorithm based on integer programming. We build a model of an augmented substrate graph, and formulate the VN embedding problem as an integer program with an objective function and some constraints. A factor of topology-awareness is added to the objective function. The VN embedding problem is solved in one stage. Simulation results show that our algorithm greatly enhances the acceptance ratio, and increases the revenue/cost ( R/C ) ratio and the revenue while decreasing the cost of the VN embedding problem.

Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value.

The virtual network embedding/mapping problem is a core issue of network virtualization. It is concerned mainly with how to map virtual network requests to the substrate network efficiently. There are two steps in this problem: node mapping and link mapping. Current studies mainly focus on developing heuristic algorithms, since both steps are computationally intractable. In this paper, we propose a new algorithm based on the proximity principle, which considers the distance factor besides the capacity factor in the node mapping step. Thus, the two steps of the embedding problem can be better integrated and the substrate network resource can be used more efficiently. Simulation results show that the new algorithm greatly enhances the performance of the revenue/cost ( R / C ) ratio, acceptance ratio, and runtime of the embedding problem.