Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.
Interference alignment (IA) is a promising interference management technique to achieve the theoretical optimal degree of freedom (DoF) performance in multi-user cooperation scenarios. However, the effective achievable sum-rate performance of IA is largely affected by the feedback overhead and accuracy of channel state information (CSI) and decoding information (DI). Therefore, it is critical to establish the exact relationship between feedback overhead and the achievable sum-rate of IA to obtain the optimal effective performance. Most existing IA performance analysis approaches focus on the vector quantization (VQ)-based feedback strategy, but the implementation complexity of VQ will be excessive when more quantization bits are required to achieve the expected quantization accuracy for larger-sized matrices or higher signal-to-noise ratio (SNR) regimes. Moreover, the obtained achievable sum-rate formulas are too complicated for quick performance evaluation. In this paper, a new sum-rate performance analysis method for IA under different quantization and feedback strategies is proposed to achieve a trade-off between accuracy and complexity, and the closed-form achievable sum-rate expressions are derived. First, in the IA case with random vector quantization (RVQ)-based CSI feedback, the quantization error of RVQ is transformed into the equivalent VQ error of the Gaussian channel error, based on which the achievable sum-rate formula is obtained. Second, in the IA case with scalar quantization (SQ)-based CSI feedback, the relationship between the effective sum-rate and SQ bits is established. Third, in the IA case with SQ-based CSI feedback and RVQ-based DI feedback, the achievable sum-rate formula is derived by combining these two kinds of quantization errors. Finally, the simulation results confirm that the theoretical results are accurate enough, which can help to determine the optimal CSI feedback overhead in practical channel conditions. Moreover, the theoretical and simulation results demonstrate that RVQ may be more applicable to IA scenarios with fewer receiving antennas and low SNR regimes.
In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output interfering broadcast channels. The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has $N$ receive antennas and the $j$ th base station transmits $d_{j}$ data streams, where $\sum _{j=1}^{I}d_{j}={N}+1$ and ${N}\ge 2$ , the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell $i$ scales as $\rho ^{\alpha }$ , where $\rho $ is the normalized transmit power and $\alpha \in [{0,1}]$ , then cell $i$ can achieve $\alpha d_{i}$ degrees of freedom. The simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA.
Data center networks (DCNs) are the essential infrastructure for cloud data centers, while the contradiction between the fixed topology and nonuniform distribution of traffic are causing the coexisting of network congestions and low network utilization in traditional wired DCNs. Thus the hybrid DCNs emerge as an alternative solution to construct flexible wireless topologies according to the dynamic traffic demands. By using wireless links, the DCN's resource oversubscription problem can be alleviated and the network energy efficiency can be greatly improved. However, due to the dense deployment of wireless access points (APs) in hybrid DCNs, the interference among APs is an obstacle to the wireless network capacity. In this paper a novel clustering scheme for interference alignment (IA) is proposed to guarantee the max-min fairness of APs in the hybrid DCN. Each AP pair is allowed to join in multiple parallel IA clusters, if those clusters are allocated with orthogonal segmented resource, such as orthogonal frequency bands or time slots. The allocated resource proportion for each parallel cluster is determined by solving a linear max-min optimization problem. For the fully connected J user M × M MIMO IC, the max-min normalized degree of freedom (DoF) by parallel clustering is proven to be (2M-1)/J, while for the partially connected IC, we focused on the 2 × 2 case and proposed a heuristic algorithm to find all potential clusters for resource allocation optimization. Theoretical analysis and simulation results have validated that the proposed parallel clustering scheme can achieve better maxmin normalized DoF performance than the single user MIMO and traditional singular IA clustering schemes.
Wireless network virtualization is a promising technique for future wireless networks. In this paper, different from traditional virtualization approaches by means of resource isolation at the subchannel or time-slot level, we propose a novel framework of heterogeneous cellular network virtualization combined with interference alignment (IA) technology, which utilizes IA to cancel the mutual interference, by aligning the interference from other transmitters into a lower dimensional subspace at each receiver. In this framework, we formulate the virtual resource allocation as a joint virtualization and IA problem, considering the gain not only from interference mitigation introduced by IA but from the sum-rate improvement brought by virtualization as well. In addition, to reduce the computational complexity, with the recent advances in discrete stochastic approximation (DSA), we propose a two-step algorithm to solve the formulated problem. The basic principle is to design IA schemes for each feasible association combination and then to traverse the association space to search for the optimal association combination with the maximum sum rate. Extensive simulations are conducted with different system parameters to show the effectiveness of the proposed scheme.
This paper presents a circular symmetrical focusing reflectarray design method that supports multi-channel wireless transmission. When the wireless nodes are distributed symmetrically from the reflectarray, the reflector array provides electromagnetic wave focusing ability for its diagonal node communication. By constructing an equivalent environment, converting the multi-channel electromagnetic wave focusing problem into a vertical focusing problem, and adjusting the parameters in the equivalent environment based on the actual environment. Realize the design that supports multi-channel circular symmetrical reflectarray.
Nowadays satellite networks are playing an increasing role in earth observation, global communication, etc. Many space missions require to deliver large amounts of data to the ground system for different purposes, and analyzing the maximum throughput of the given satellite network is a prerequisite for efficient data transmission. However, satellite networks possess the time-varying topologies, dynamic bandwidth and limited on-board energy, which restricts the end-to-end capacity and poses challenges to the analysis. In this paper, we utilize temporal graphs for better solving the end-to-end max-flow problem over energy-limited satellite networks. An energy time-expanded graph (eTEG) is constructed to accurately represent the restriction of on-board limited energy on data transmission capability. Furthermore, to maximize flow delivery and energy utilization, we proposed an eTEG-based max-flow routing algorithm with time-dependent residual network update rules. Simulation results are also presented to verify the efficacy of our algorithm.
The exponential growth of services demands further increase of spectral efficiency which drives the next generation wireless access networks towards deploying femtocells with frequency reuse. However, the interference will be severe especially in dense deployment scenarios. The fair resource allocation problem by joint consideration of sub‐channel assignment and interference alignment (IA) is more complicated than the traditional problem. First, not all the users are appropriate for IA since the number of participant users is limited by the feasibility constraint and the interference power levels are different for the path loss. Second, IA can increase the degrees‐of‐freedoms while occupy additional signal dimensions of participant users, hence more sub‐channels are needed by IA compared with the non‐participants when each user transmits the same number of streams. This study models the fair resource allocation problem as an optimisation problem, which is a non‐deterministic polynomial‐time (NP)‐hard. To solve it with low complexity, the authors propose a graph‐based scheme to give the approximate solution, where the selection criteria of IA group are based on the influence of IA on the interference graph. Simulation results show that the authors scheme can approximate the optimal solution in a small network and improve the fairness in dense deployment scenarios.
Interference alignment (IA) is an emerging interference management approach which fully exploits the potential spatial bandwidth in wireless networks, achieving optimal DOF. However, perfect IA is almost unaccessible for 3-user complex constant interference channel without symbol extension. For this, an efficient phase based imperfect interference alignment scheme is proposed in this paper, which uses a deviation compensation factor (DCA) to measure how much the practical channel coefficients deviate from perfect IA feasibility condition. The DCA is allocated to receivers based on a Minimum Interference Leakage (MIL) criterion. Simulation results shows that the MIL based DCA, Max-SINR and TDMA algorithms perform better in different SNR regions, and the proposed scheme can be improved with proper switch between different strategies.
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