Segment Routing (SR) provides an effective approach to path control with minimal control-plane signaling. This makes SR a strong candidate for routing in the Satellite-Terrestrial Integrated Network (STIN), the core infrastructure enabling ubiquitous Internet of Things (IoT) connectivity. However, directly applying existing SR solutions to satellite networks presents significant challenges, which include limited bandwidth and constrained onboard processing capabilities, hindering efficient IoT data transmission. To enable reliable and efficient IoT services, we propose Satellite-Tailored Segment Routing (STSR), a novel framework designed specifically for satellite networks in STIN. STSR is built as a lightweight and Segment Routing over IPv6 (SRv6)-compatible extension. It deploys a customized data plane that enables efficient source routing through bit-based encoding and streamlined processing, which is vital for resource-constrained satellites. Furthermore, we develop a quality of service (QoS)-aware route compression scheme designed to meet diverse IoT service demands. This scheme leverages the computational resources of terrestrial controllers to generate compact, STSR-encoded paths. By accounting for QoS requirements and satellite-specific dynamics, the embedded algorithm enhances routing performance for heterogeneous IoT flows within the satellite network. Simulation and evaluation results demonstrate that STSR outperforms existing SRv6-based approaches in path encoding efficiency, payload transmission efficiency, processing overhead, and traffic engineering performance.

Space-Terrestrial Integrated Networks (STIN) have an important influence on Service Function Chains (SFCs), broadening their service scope and enhancing application performance. However, STIN features a dynamic terrestrial access layer and an inter-satellite layer; it's complex to orchestrate the SFC in such a dynamic network. In this paper, we provide the Virtual Multi-Resource Storage Time Aggregated Graph (VMR-STAG) model and SFC orchestration algorithms for SFC orchestration in STIN. Inspired by the Virtual Topology (VT) method, VMR-STAG aggregates the dual-layer dynamic topology and time-varying resources into a single virtual graph, thus reducing the complexity of the STIN model. Based on VMR-STAG, we propose the Offline Request Orchestration (ORO) and Online Request Orchestration (OLRO) algorithms, designed to minimize SFC migration frequency, service delay, service jitter, and enhance load balancing. Leveraging resource distribution across multiple time slots, these algorithms schedule the long-lasting, high-performance SFC within STIN. Evaluation and simulation results demonstrate that our proposed model and algorithms significantly outperform conventional solutions, achieving significant improvements in model complexity, SFC migration frequency, workload balancing, service delay, and jitter.

Low Earth Orbit satellite networks play a significant role in providing global ubiquitous services. The application of Inter-Satellite Links (ISLs) has accelerated development in Non-Terrestrial Networks with satellite backhaul. However, ISL-based backhaul does not match the performance of terrestrial fiber links, which makes its impact on end-to-end performance non-negligible. Existing radio resource allocation solutions usually neglect the backhaul performance, potentially failing to meet end-to-end Quality of Service requirements. In this work, we propose ARA-IBA, an access resource allocation scheme with ISL-based backhaul awareness. The satellite network’s backhaul path states, including delay and packet loss rate, are exposed to on-board base stations to enable dynamic resource scheduling. In ARA-IBA, resources are jointly scheduled for Guaranteed Bit Rate (GBR), Delay-critical GBR, and Non-GBR users. For GBR and Delay-critical GBR users, backhaul delay is utilized to optimize end-to-end delay satisfaction. A clustering game is employed to perform fine-grained allocation adjustments. Overloaded Non-GBR users are then scheduled using a pointer network. ARA-IBA optimizes backhaul packet loss rate while maintaining scalability to accommodate varying user numbers. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in terms of users’ delay satisfaction and backhaul packet loss rates.

The excessively long Segment Identifier (SID) list in SRv6 challenges the promotion of SRv6 network slicing in cloudedge collaborative network. In this paper, we studied the problem of packet overhead during SRv6 SID processing in SRv6 network slicing services. We first design a new mechanism called Behavior Expression Slicing (BES) mechanism to establish the feasibility of dynamically virtual address allocation as well as SID compression based on adjacency information. Then we propose an algorithm to optimize compression efficiency in proposed BES mechanism. The simulation results indicate that the compression rate of the proposed mechanism and algorithm can reach over 75%.

Generative Artificial Intelligenge (GAI) is revolutionizing the world with its unprecedented content creation ability. Large Language Model (LLM) is one of its most embraced branches. However, due to LLM’s substantial size and resource-intensive nature, it is cloud-hosted, raising concerns about privacy, usage limitations, and latency. In this paper, we propose to utilize ubiquitous distributed wireless edge computing resources for real-time LLM inference. Specifically, we introduce a novel LLM edge inference framework, incorporating batching and model quantization to ensure high throughput inference on resource-limited edge devices. Then, based on the architecture of transformer decoder-based LLMs, we formulate an edge inference optimization problem which is NP-hard, considering batch scheduling and joint allocation of communication and computation resources. The solution is the optimal throughput under edge resource constraints and heterogeneous user requirements on latency and accuracy. To solve this NP-hard problem, we develop an OT-GAH (Optimal Tree-search with Generalized Assignment Heuristics) algorithm with reasonable complexity and$\frac {1}{2}$-approximation ratio. We first design the OT algorithm with online tree-pruning for single-edge-node multi-user case, which navigates the inference request selection within the tree structure to miximize throughput. We then consider the multi-edge-node case and propose the GAH algorithm, which recrusively invokes the OT in each node’s inference scheduling iteration. Simulation results demonstrate the superiority of OT-GAH batching over other benchmarks, revealing an over 45% time complexity reduction compared to brute-force searching.

Low earth orbit (LEO) satellite networks can seamlessly supplement terrestrial networks by providing a high capacity, wide coverage, and cost-effective solution. Positioned to play a significant role in the upcoming 5G/6G era thanks to reduced launch expenses, LEO satellite networks offer benefits such as multi-path transmission, aggregated link bandwidth, redundant paths, and enhanced mobility support. These advantages necessitate further exploration in integrated satellite-terrestrial networks. In this work, we leverage network conditions, underlying link status, and real-time service characteristics to achieve effective synergy, aiming to fulfill application requirements. We formulate the service-oriented multi-path scheduling (SOMPS) problem as a bounded multi-knapsack problem and employ dynamic programming methods for its solution. Simulation results demonstrate that our proposed scheme provides high transmission rate, low latency, and customized information delivery for services, in comparison with baseline schemes.

Driven by technological advances and new business models, we have seen a renewed interest in LEO satellite constellations. The deployment of large-scale LEO satellite networks is becoming a reality. The network topology changes periodically, as satellites orbit the Earth. This imposes a great challenge to network monitoring. Meanwhile, as a new network monitoring method, In-band Network Telemetry (INT) can provide per-hop granular telemetry metadata, needed to tackle the mobile nature of LEO satellite constellations. Given this, we apply INT to LEO satellite networks for real-time fine-grained monitoring. We propose a path planning solution to identify the paths for network-wide telemetry and the paths for disseminating the telemetry data to the ground facilities. By taking advantage of the predictable satellite trajectories and topology variations, the path planning solution is designed to achieve network-wide coverage and minimize telemetry overhead. We take the LEO48 constellation as an example to visually show the detailed paths of the monitoring scheme. We conduct experiments on different sizes of networks to evaluate the original path planning algorithm and the improved balanced algorithm in this paper, demonstrating the timeliness and balance of the telemetry solution.

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.

Generative Artificial Intelligence (GAI) is taking the world by storm with its unparalleled content creation ability. Large Language Models (LLMs) are at the forefront of this movement. However, the significant resource demands of LLMs often require cloud hosting, which raises issues regarding privacy, latency, and usage limitations. Although edge intelligence has long been utilized to solve these challenges by enabling real-time AI computation on ubiquitous edge resources close to data sources, most research has focused on traditional AI models and has left a gap in addressing the unique characteristics of LLM inference, such as considerable model size, auto-regressive processes, and self-attention mechanisms. In this paper, we present an edge intelligence optimization problem tailored for LLM inference. Specifically, with the deployment of the batching technique and model quantization on resource-limited edge devices, we formulate an inference model for transformer decoder-based LLMs. Furthermore, our approach aims to maximize the inference throughput via batch scheduling and joint allocation of communication and computation resources, while also considering edge resource constraints and varying user requirements of latency and accuracy. To address this NP-hard problem, we develop an optimal Depth-First Tree-Searching algorithm with online tree-Pruning (DFTSP) that operates within a feasible time complexity. Simulation results indicate that DFTSP surpasses other batching benchmarks in throughput across diverse user settings and quantization techniques, and it reduces time complexity by over 45% compared to the brute-force searching method.

In the large-scale dynamic Low Earth Orbit (LEO) satellite networks, the conventional TCP-based single-path transmission encounters challenges such as prolonged propagation delay, frequent connection failures, and suboptimal resource utilization. In this paper, we propose an Integrated Multi-Path Network Coding (IMPNC) transmission scheme. This scheme leverages multiple paths for end-to-end transmission to achieve bandwidth aggregation and redundant backup. The multi-path transmission is facilitated by Multi-Path Quick UDP Internet Connection (MPQUIC) protocol to adapt to the limited satellite bandwidth and caching resources. The proposed approach involves encoding packets at nodes along the paths, addressing the significant out-of-order problem arising from variable delays on different paths. Additionally, we present a Software Defined Networking (SDN)-based domain clustering architecture, which offers a more streamlined control approach, reducing overall complexity. Furthermore, we formulate the domain clustering problems as mixed-integer nonlinear programming and the coding-based routing problem as a Steiner tree problem. Evaluation results demonstrate that the proposed scheme effectively reduces the latency over 25.1%, enhances bandwidth utilization by 19.6%, and ensures reliable data transmission by reducing retransmission probability by 4.1%.

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%.

The Internet of Things (IoT) ecosystem is undergoing a significant evolution through its integration with satellite networks, empowering remote and computation-intensive IoT tasks to leverage computing services via satellite links. Current research in this field predominantly focuses on minimizing latency and energy consumption in computation offloading, yet overlooks the substantial costs incurred by satellite resource utilization. To address this oversight, we introduce a cost-effective hybrid computation offloading (CE-HCO) paradigm in satellite-terrestrial integrated networks (STINs) in this article. First, we propose the 5G-based system framework facilitates gNB and user plane function functionalities on satellites and fosters collaboration between public cloud providers and satellite operators. The framework is in line with the latest 3GPP activities and business models in satellite computing. Then, we formulate the CE-HCO problem, aiming to minimize total computation offloading costs while satisfying diverse user latency requirements and adhering to satellite energy constraints. To tackle this NP-hard problem, we develop an algorithm employing the penalty method and successive convex approximation to simplify the complex mixed-integer nonlinear programming into tractable convex iterations. Simulation results show that our approach outperforms existing baselines in balancing performance and cost, and offer guidance on pricing policies for satellite computing services to promote future commercial growth.

In this paper, a clustered parallel training algorithm is designed for personalized federated learning (Per-FL), called CPPer-FL. CPPer-FL improves the communication and training efficiency of Per-FL from two perspectives, namely, less burden for the central server and lower interaction idling delay. CPPer-FL adopts a client-edge-center learning architecture, which offloads the central server's model aggregation and communication burden to distributed edge servers. Also, CPPer-FL redesigns the cascading model synchronization and updating procedure in conventional Per-FL and changes it to a parallel manner, thus improving the interaction efficiency in the training process. Further, for the proposed hierarchical architecture, two approaches are proposed to cater to Per-FL: similarity-based clustering for client-edge association and personalized model aggregation for parallel model updating, such that clients' personal features can be preserved in the training process. The convergence of CPPer-FL has been formally analyzed and proved. Evaluation results validate the communication efficiency, model convergence, and model accuracy improvement.

Recently, MEC has been integrated with satellite networks to process remote terrestrial computation tasks with superior coverage and delay. Since single satellite computation is hard to tackle spatially uneven computation workloads, computation peer offloading among multiple satellites is urgently needed to further improve service quality and resource utilization. However, considering limited resources, deficient energy, and costly overheads of communication and computation, how to enable efficient offloading cooperation in the time-varying satellite networks is a significant challenge. In this paper, we first design a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities. Second, we formulate the Multi-Hop Satellite Peer offloading (MHSPO) problem, aiming to jointly minimize the delay and energy consumption under system resources and backlog constraints. Then, to adapt to the network dynamics, the decision-making process with uncertain future workloads is optimized by leveraging the delayed online learning method under the Lyapunov framework. Finally, we develop a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance. Extensive simulations show that multi-hop peer offloading among satellites improves edge computing performance efficiently.

Earth observation applications, such as emergency surveillance and disaster relief, are thriving due to the availability of earth observation satellites that provide timely and objective observation data at different spatial and temporal scales. Such observation data is processed at the satellite edge with orbital edge computing, leading to potentially reduced bandwidth cost and transmission delay. However, most existing studies primarily focus on optimizing computation offloading but ignore the consideration of object observation and observation data transmission. To fill this gap, this paper proposes a novel scheduling approach that jointly considers observation satellites, relay satellites, and computing satellites in LEO satellite systems, aiming to maximize the number of completed observation tasks while taking into account various requirements of observation, transmission, and computation resources. Specifically, we first formulate the problem of jointly scheduling observation, relay, and computing satellites to maximize the number of accomplished observation tasks. Then, we decompose the formulated problem into two sub-problems and design a resource-aware algorithm called ORCA to determine the optimal scheduling of observation, relay, and computing satellites. Simulation results demonstrate that ORCA outperforms existing algorithms in terms of completing a number of observation tasks.

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.

The natural mesh network topology of satellite networks makes multi-path transmission prevalent due to its ability to provide bandwidth aggregation and backup using redundant paths. While as one of the significant benefits of multi-path transmission, high reliability requires extensive signaling to achieve excellent performance. So designing an analysis and treatment method to tackle the network overhead and reliability balance remains a major challenge. In addition, massive and burst services in satellite networks will have different demands, which require the network to execute fine-grained path scheduling and management of the multi-path transmission. In this paper, we carefully model the multi-path reliability and network overhead factors under the SDN-based integrated satellite-terrestrial network architecture to characterize the network state. Second, an adaptive multi-path selection scheme is designed to handle the number of active paths, which considers different Quality of Service (QoS) requirements under the limited resources of satellite networks. Then, we formulate the problem as Non-Linear Binary Programming (NLBP) and develop a practical Particle Swarm Optimization (PSO)-based algorithm to solve it. Simulation results show that the proposed scheme can improve satellite networks’ throughput and resource utilization.

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.

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.

Low earth orbit (LEO) small satellites have attracted great interests in civilian and military applications due to their low cost and high service performance. However, the enormous scale and high dynamism of small satellites pose challenges to network flexibility and scalability. Therefore, the hierarchical satellite network structure is introduced as an effective approach to enhance the satellite network capabilities further. In this regard, small satellites’ clustering is of fundamental importance for designing such a hierarchical structure. Satellite clusters are always prone to instability due to unpredictable link failures and frequent topology changes. In this article, we study the small satellite clustering problem of jointly optimizing the cluster reliability and the network management overhead. A coalition game-theoretic framework is introduced to obtain low computational complexity by adopting the clustering-decision-making process in an automated and fully distributed fashion. A distributed coalition formation algorithm based on the optimization of reliability and management overhead is developed for the clustering problem. Finally, extensive simulations have been conducted, and the results show that our proposed clustering scheme is able to produce better results than the baseline schemes.

Viewport prediction and prefetch have an important influence on VR video streaming performance. This work proposes a novel federated learning-based viewport prediction model training algorithm, ComPer-FedAvg. The proposed algorithm leverages a VR video’s common viewing pattern and users’ personal viewing patterns to train the prediction model in a distributed and privacy-preserving manner. Further, considering the VR video viewport prediction accuracy, a stochastic game is formulated to solve the VR streaming network’s communication resource allocation problem, where limited communication resource blocks are auctioned to users to achieve the optimal overall VR viewing experience. For each user, the auction is decomposed into two disjoint subproblems, namely, the optimal number of data rate requesting and true value claiming (bidding). The optimal true value claiming has been analytically proved to be equal to the VR viewing reward with given data rate. Due to the lack of global information when users request data rate, we reformulate users’ data rate requesting problem as a POMDP problem. A novel deep reinforcement learning algorithm is adopted to solve the problem. Evaluation and simulation results show the proposed viewport prediction and VR streaming schemes outperform conventional solutions in terms of prediction accuracy and VR viewing experience.

Because of the challenges brought by the high dynamic topology of satellite networks to the transport layer, this paper is mainly devoted to a multipath transmission control protocol (MPTCP) path selection scheme based on segment control technology and software-defined networking (SDN). We describe the signaling interaction mode of MPTCP and the process of segment control technology applied in the satellite network. According to the requirements of real-time and accuracy of data transmission, we consider the link delay, stability, and packet loss rate, and construct the scheme as a maximum-flow minimum-cost problem. The experimental results show that the proposed scheme can meet the low delay requirements of delay-sensitive traffic flow, improve bandwidth utilization, and ensure more efficient and reliable data transmission.

Satellite network constellation is promising in providing efficient global Internet access. While the constellation scale, the user population, and service variety in satellite networks are too large, requiring efficient resource allocation and network management. Network virtualization is an efficient solution to achieve preceding objectives, but conventional schemes on terrestrial networks are not well adapted to satellite networks. Therefore, in this work, we establish a network virtualization model considering topology dynamics, quality of service requirement, and resource constraint. Then we formulate Virtual Network Embedding (VNE) into optimization problems, and we propose a multi-constraint virtual network embedding algorithm to solve the problem. Finally, we evaluate the proposed scheme and prove its adaptability to satellite networks.

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.

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.

Built-in caching in Named Data Networking (NDN) promises to provide efficient content delivery, where the dedicated on-path caching scheme is deployed to serve users' requests on the forwarding path. In this work, to utilize limited caching resources to achieve optimal performance, the caching placement decision is made by jointly considering the content popularity, underlying network topology, forwarding strategy and caching service mechanism in NDN. More specifically, we propose a service-aware caching model. In the model, we first define the Cache Service Matrix (CSM), which describes the position where each user's request is served for each piece of content. In order to make CSM comply with the caching placement, underlying topology, forwarding strategy, and on-path caching service mechanism, we propose an algorithm to calculate CSM under the preceding constraints. With CSM, the utility of caching placement could be derived correctly, and we formulate the optimal caching placement into optimization problems. Moreover, the differential grouping co-evolutionary (DG2-E) algorithm is adopted to decompose and solve the NP-hard optimization problems. Simulation results show the proposed scheme outperforms state of the art solutions in terms of inter-domain traffic reducing and request-response accelerating under arbitrary topologies.

Reproducing network emulation experiments on diverse physical platforms with varying computation and communication resources is non-trivial. Many state-of-the-art network emulation testbeds do not guarantee timing fidelity. Consequently, results obtained from these testbeds can be misleading, especially when insufficient physical resources are provided to run the experiments. Reproducibility is far from being the norm. In this paper, we present a novel approach that can guarantee reproducible results for network emulation. Our system, called the Virtual Time Machine (VTM), takes advantage of both time dilation and carefully controlled scheduling of the virtual machines. Time dilation allows sufficiently scaled resources to run the experiments in virtual time, and controlled VM scheduling prescribes the precise timing of message passing for distributed applications---independent of the resource provisioning of the underlying physical testbed. Preliminary experiments show that VTM can guarantee reproducible results with varying time dilation, resource subscription, and VM scheduling 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.

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.

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.

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.

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.

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.

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.

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 irregular deployment of small base stations (SBSs), the interference in cognitive heterogeneous networks (CHNs) becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains (DTMCs), the spectrum mobility with respect to the arrival and departure of macro base station (MBS) users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry (SG). Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information (CSI). Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.

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.

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.