In time-sensitive networks, the frames of time-triggered (TT) flows need to be transmitted in scheduled slots. To avoid the interference of frames from other flows to the frames of the TT flows, guard bands are generally reserved prior to scheduled slots. The time-sensitive networking (TSN) standard, IEEE 802.1Qbu, specifies a frame preemption model that enables frames of TT flows (preemption frames) to preempt frames of other flows (preempted frames). According to this model, preempted frames are divided into fragments that are transmitted in the gaps of preemption frames. Consequently, the IEEE 802.1Qbu model reduces the guard band from the longest Ethernet frame (typically 1518 bytes) to 123 bytes, which improves the bandwidth utilization and delay performance of preempted frames without causing frame disorder. However, in scenarios in which the preempted frame load is heavy, and the length is short, numerous guard bands smaller than 123 bytes are generated. These guard bands prevent the IEEE 802.1Qbu model from transmitting preempted frame fragments, resulting in a considerable decrease in bandwidth utilization. To solve this problem, we propose a novel frame preemption model without guard bands, based on a padding and splicing mechanism. While ensuring that preemption frames are transmitted according to scheduled slots, this model can transmit preempted frames within arbitrary byte gaps based on the schedule without obtaining the length of the preempted frames in advance. We compared the transmission-delay performance of the proposed model with that of the IEEE 802.1Qbu model using a theoretical analysis. The evaluation results obtained from heavily loaded preemption frame scenarios revealed that the proposed model improved link utilization by 32.8% relative to the IEEE 802.1Qbu model and reduced the transmission delay by more than one order of magnitude. Moreover, when the IEEE 802.1Qbu model fails, the proposed model still transmits 60% of preempted frames.

The IEEE 802.1Qbv standard schedules time-triggered (TT) flows (i.e., period flows) in a fixed TT Window each period. However,period flows generated by non-deterministic end systems exhibit significant jitter, whichleads to a mismatch between the generation times and the scheduled TT Windows. In the worst case scenario, this mismatch can cause an additional send delay of approximately one period in the flow. In this study,we model the send delay issue due to the maximum send delay requirement for all frames in a period flow not being satisfied simultaneously, and measure the jitter of period flows in a typical non-deterministic end system. Moreover, we propose an access mechanism for jittered period flows to schedule multiple conflict-free TT Windows in each period of flows at the source end system and control the send delay within the required delay tolerance. This mechanism enables deterministic access to jittered period flows, providing a prerequisite for reliable end-to-end transmission in the network. Moreover, this mechanism adopts a multi-objective integer linear programming (MILP) solver to optimize the TT Windows schedule. Furthermore, we establish the constraints and objective functions for the MILP solver and evaluate the mechanism with actual sampled frames in a real non-deterministic end system. Compared with the conventional fixed single TT Window and worst-case delay analysis mechanisms, the proposed mechanism satisfies the send delay requirements and considerably reduces the buffer usage at the source end system.

Programmable switches and SmartNICs motivate the programmable network. ASIC is adopted in programmable switches to achieve high throughput, and FPGA-based SmartNIC is becoming increasingly popular. The programmable parser is a key element in programmable switches and SmartNICs, which can identify the protocol types and extract the relevant fields. The programmable parser for the next generation programmable switches and SmartNICs requires a significant improvement in PPAL (performance, power, area, and latency), which is quite challenging. According to the Ethernet roadmap, 800 Gbps and 1.6 Tbps are expected to be the future switch interface speeds after 2022, which leads to higher throughput of the parser. Meanwhile, the end of Dennard scaling and the slowdown of Moore’s Law result in limited power and area. Besides, the need for low-latency and low-jitter operations at the datacenter scale continues to grow.

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users' satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

Time-triggered Ethernet (TTE) technology introduces the concept of time-triggered on the basis of traditional Ethernet, so that it can achieve conflict-free and deterministic service forwarding without sacrificing compatibility. However, storage resources in industrial, aviation, aerospace and other equipment are limited. Therefore, it is important for TTEthernet to develop switching technologies with high storage efficiency and scalability. This paper proposes a dual plane switching (DPS) architecture for TTEthernet, which divides time-triggered services and event-triggered services into two planes for data forwarding. Experimental results show that using the TTE switch of this architecture has the advantages of high clock synchronization accuracy, high throughout, low transmission delay and small jitter of TTE service.

In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.

Content caching is a promising way to overcome backhaul limitations in small cell networks. However, in such type of networks, small base stations (SBSs) are always deployed with limited cache storages. Thus, it is necessary for SBSs to adjust their contents for better caching efficiency, so as to reduce backhaul traffic. In this paper, we study the content replacement problem to minimize the traffic flowing into the costly backhaul links. However, in small cell networks where SBSs make up backhaul mesh networks, the effectiveness of reducing backhaul traffic depends on the hop distance from the content location to the requesting user. On this basis, we formulate a hop minimization problem that is inherently combinatorial. Through log-sum-exp approximation, we can solve the problem and arrive at a close-form solution with guaranteed performance gap to the optimal solution. By exploiting the properties of continuous-time Markov chain (CTMC), the solution can be implemented by designing a CTMC that can instruct the content replacement process. As a consequence, a concise, efficient, and flexible content replacement strategy is proposed. Simulation results verify our analysis and show that our proposed strategy outperforms the conventional strategies.

User association has redrawn much attention lately, due to the introduction of content caching in small base stations (SBSs). To reduce traffic burden on backhaul links, users are associated with different SBSs when requesting different contents. However, user-perceived delay increases if the serving SBSs that have the desired contents are overloaded. Moreover, the user association problem becomes complex due to the vast number of contents. In this paper, to reduce user-perceived delay as well as backhaul loads, we propose a cluster-level user association scheme where content clustering is leveraged to simplify user association and reduce its complexity. Particularly, similar contents are clustered together according to the content preferences of users and cached contents in SBSs. Thus, the dimensionality of the user problem becomes smaller. On this basis, we propose a distributed cluster-level user association scheme, where each user selects SBSs based on their traffic loads and cached contents. Simulation results show that our scheme based on clustered contents outperforms the traditional schemes.

Supporting cooperative radio among remote radio heads (RRHs) and elastic cloud service in the baseband unit (BBU) pool, cloud radio access network (C-RAN) is perceived as a promising solution for the next mobile network. In C-RAN, cooperative radio can enhance power saving at RRHs, along with impact on computation effort and power saving in the BBU pool. In turn, power saving at RRHs, benefited from cooperative radio is restricted by the constrained computation capacity provisioned by processors in the BBU pool. Besides, limited fronthauls, which support baseband signal transfer between the BBU pool and RRHs, affect the cooperative radio design and power consumption at RRH as well. By jointly optimizing transmit beamforming among RRHs and processor sleeping in the BBU pool, we exploit such interplay between communication and computation for system power minimization in C-RAN with limited fronthaul and computation capacity. Specifically, we formulate this problem as a mixed-integer non-linear programming problem (MINLP), and then leverage the special structure of the MINLP to make a near optimal decision on the set of active processors and transmit beamforming vectors with high efficiency. Finally, extensive numerical results demonstrate that our proposed algorithms can enforce processor sleeping and reduce system power consumption significantly.

Cloud radio access network (C-RAN) is perceived as an energy-efficient solution for the next mobile network. The cloud-based baseband unit (BBU) pool is capable of dynamically provisioning computational resources for mobile users to improve hardware utilization such that unused processors can be switched off for power saving in the BBU pool. Besides, cooperative radio among remote radio heads (RRHs) can optimize transmit beamforming to reduce power consumption at RRHs. Thus, to achieve more judicious system power saving, this is need to consider power consumption in the BBU pool and that at RRHs together. In this paper, we aim to minimize system power consumption by jointly exploiting transmit beamforming and processor sleeping. Specifically, we formulate a mixed integer non-linear system power minimization problem, which is hard to solve. For tractability purpose, we transform this problem to an equivalent clustering problem embedded with a series of transmit beamforming problems and processor sleeping problems. On this basis, we first focus on solving the embedded problems with the given clustering and then propose a low-complexity clustering algorithm to search out the optimal clustering with minimum system power consumption. Finally, simulation results show that our proposed algorithms can save system power significantly.

In order to improve the utilization of the fading channel of the high-rate IEEE 802.11n wireless networks, an adaptive rate control and frame length adjustment scheme (i.e., ARCLA) is proposed in this paper. In ARCLA, the subframe error rate (SFER) of the aggregate MAC protocol data unit (A-MPDU) is used for the estimation of the link quality, and the channel air time is fairly allocated over the data transmissions based on the channel coherent time to ensure the uniformity of the link quality during one frame transmission. According to that, the data rate and the data frame length are adaptively adjusted in two-dimensions to fit the fluctuant channel condition. Furthermore, an analytical framework based on the semi-Markov process is formulated to evaluate the performance of our scheme. Simulations results verify the theoretical analysis and show the superiority of ARCLA in respect to throughput performance.

Cloud radio access network (C-RAN), regarded as a promising green network architecture, facilitates cooperative transmission among remote radio heads (RRHs) while enabling flexible computation provisioning in the virtualized baseband unit pool. By jointly optimizing cooperative transmission, i.e., transmit power allocation with zero-forcing precoding adopted, and computation provisioning, i.e., virtual machine assignment, this paper minimizes the system power consumption comprised of transmit power and processing power in downlink C-RAN. Specifically, subject to per-RRH power constraint (PRPC) and per-MU quality of service constraint, the system power consumption minimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem. To solve the challenging MINLP, we reformulate the MINLP as a minimum weight perfect matching problem to get the initial solution without considering the PRPC. On this basis, a power-aware greedy algorithm is further devised to modify the solution such that the PRPC is satisfied. Finally, extensive simulations show the superiority of the proposed scheme on system power saving and the tradeoff between transmit power and processing power.

By migrating baseband processing functionalities into a centralized cloud-based baseband unit (BBU) pool, cloud radio access network (C-RAN) facilitates cooperative transmission among remote radio heads (RRHs) and enables flexible computation provisioning in the BBU pool. In C-RAN, due to the high amount of data transfer from the BBU pool to RRHs through fronthauls, limited fronthaul capacity becomes a key factor when designing cooperative transmission schemes among RRHs. Meanwhile, as computational resources are provisioned to mobile users (MUs) for baseband processing in the form of virtual machines (VMs) in the BBU pool, an effective VM assignment strategy is also with great significance. In this paper, we propose a holistic framework for green C-RAN under the constraint of limited fronthaul capacity, where we jointly optimize hybrid clustering and computation provisioning to appropriately provide a cluster of RRHs and a VM to each MU for cooperative transmission and baseband processing, aiming at minimizing the system power consumption. The system power minimization problem is formulated as an integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem to an equivalent hybrid clustering problem embedded with a series of VM assignment problems. On this basis, we first achieve the optimal solution for system power minimization with high computational complexity, and then, a greedy algorithm is proposed to solve the hybrid clustering problem for practical implementation. Finally, the simulation results demonstrate that the proposed joint optimization of hybrid clustering and computation provisioning can significantly reduce the system power consumption.

The use of buffers in the central stage of memory–memory–memory (MMM) switch can potentially cause the forwarding of packets to the outputs in out‐of‐sequence order. In this study, the authors present a novel scheduling algorithm, frame‐based in‐sequence scheduling in MMM switch (FIM 3 ), which can provide 100% throughput and in‐sequence service as well. FIM 3 adopts a predetermined cyclic shift configuration at the first stage and the oldest‐cell‐first selection for arbitrations at the second and third stages. Frame‐based scheduling algorithm is adopted, and fake packets will be added for an incomplete frame to obtain full. FIM 3 is decentralised and requires no speedup. The simulation results show that FIM 3 has a better delay performance than padded frame does under uniform traffic, and achieves 100% throughput under non‐uniform traffic.

Clos‐network switches have attracted a lot of attention because of their modularity and scalability. However, out‐of‐sequence problems weigh heavily against the application of buffered Clos‐network switches. A space‐memory‐memory (SMM) Clos‐network switch is proposed in this study which is able to provide in‐sequence service. There are two features in the proposed switch which are different from the previously proposed schemes. First, two‐stage load‐balanced Birkhoff‐von Neumann (LB‐BvN) switches are adopted for each second‐stage module. Second, no inter‐stage matching is needed. The key idea to provide in‐order cell delivery in the proposed SMM Clos‐network switch is that cells belonging to the same flow will experience the identical delay when passing through the LB‐BvN switches at the second stage, and arrive at their destined third‐stage module in order. Each switching module operates independently, which makes the proposed SMM Clos‐network switch practical to implement in hardware. Simulations show that the proposed switch can achieve high performance under both Bernoulli and Bursty arrival traffic.

This letter presents a novel adaptive alternating direction method of multipliers with support/signal value detection for compressed sensing. The support/signal value detection in our algorithm can achieve an efficient reconstruction by leveraging more information that goes beyond simple sparsity. Especially for time-correlated signals in large-scale problems, our proposal performs better than conventional methods, since more accurate signal information could be estimated from prior knowledge during initialization. Simulation results show that our method can improve the average PSNR by 1.02-2.05 dB for undersampled video sequences.

Satisfying critical QoS requirements in next generation networks poses major challenges, due to its intrinsic complexity of network resource allocation. This paper considers the problem of load balance based bandwidth allocation for delay sensitive services. For satisfying deterministic end-to-end delay requirement, a simple and efficient algorithm for path level optimal bandwidth allocation, path level equal ratio allocation algorithm (P-ERA), is developed first. Based on P-ERA, another algorithm for network level optimized bandwidth allocation, network level equal ratio allocation algorithm (N-ERA), is also presented for much more complicated network circumstances. N-ERA algorithm features appropriate route selection and balanced bandwidth allocation, and can adaptively avoid, or at least delay, the emergence of network bottleneck when a network is heavily loaded. Extensive simulations indicate that N-ERA algorithm can make full use of network bandwidth and admit more services, even if they are delay critical, than other ones dealing with the same problem. And the less computation complexity of N-ERA algorithm makes it of great application value.

Cyclic Cubes, proposed as a new family of interconnection networks, is proved not new and is equivalent to the wrapped butterfly networks. There is a similar fact that the Cyclic Cubes is also equivalent to the Bidi Shufflenet, which is investigated recently. But this fact is almost neglected. In this paper, we discuss the equivalence between the Cyclic Cubes and the Bidi Shufflenet.

We propose a deadlock-free wormhole routing scheme called Zigzag routing for the pan-Mesh, which generalizes the mesh topology. Accordingly, we prove that only two virtual channels are needed for deadlock-freeness on every physical channel by using Zigzag routing.