Optimal VM placement in data centres with architectural and resource constraints

Recent advance in virtualisation technology enables service provisioning in a flexible way by consolidating several virtual machines (VMs) into a single physical machine (PM). The inter-VM communications are inevitable when a group of VMs in a data centre provide services in a collaborative manner. With the increasing demands of such intra-data-centre traffics, it becomes essential to study the VM-to-PM placement such that the aggregated communication cost within a data centre is minimised. Such optimisation problem is proved NP-hard and formulated as an integer programming with quadratic constraints in this paper. Different from existing work, our formulation takes into consideration of data-centre architecture, inter-VM traffic pattern, and resource capacity of PMs. Furthermore, a heuristic algorithm is proposed and its high efficiency is extensively validated

Information-sharing outage-probability analysis of vehicular networks

In vehicular networks, information dissemination/sharing among vehicles is of salient importance. Although diverse mechanisms have been proposed in the existing literature, the related information credibility issues have not been investigated. Against this background, in this paper, we propose a credible information-sharing mechanism capable of ensuring that the vehicles do share genuine road traffic information (RTI). We commence with the outage-probability analysis of information sharing in vehicular networks under both a general scenario and a specific highway scenario. Closed-form expressions are derived for both scenarios, given the specific channel settings. Based on the outage-probability expressions, we formulate the utility of RTI sharing and design an algorithm for promoting the sharing of genuine RTI. To verify our theoretical analysis and the proposed mechanism, we invoke a real-world dataset containing the locations of Beijing taxis to conduct our simulations. Explicitly, our simulation results show that the spatial distribution of the vehicles obeys a Poisson point process (PPP), and our proposed credible RTI sharing mechanism is capable of ensuring that all vehicles indeed do share genuine RTI with each other

PAPR reduction using iterative clipping/filtering and ADMM approaches for OFDM-based mixed-numerology systems

Mixed-numerology transmission is proposed to support a variety of communication scenarios with diverse requirements. However, as the orthogonal frequency division multiplexing (OFDM) remains as the basic waveform, the peak-to average power ratio (PAPR) problem is still cumbersome. In this paper, based on the iterative clipping and filtering (ICF) and optimization methods, we investigate the PAPR reduction in the mixed-numerology systems. We first illustrate that the direct extension of classical ICF brings about the accumulation of inter-numerology interference (INI) due to the repeated execution. By exploiting the clipping noise rather than the clipped signal, the noise-shaped ICF (NS-ICF) method is then proposed without increasing the INI. Next, we address the in-band distortion minimization problem subject to the PAPR constraint. By reformulation, the resulting model is separable in both the objective function and the constraints, and well suited for the alternating direction method of multipliers (ADMM) approach. The ADMM-based algorithms are then developed to split the original problem into several subproblems which can be easily solved with closed-form solutions. Furthermore, the applications of the proposed PAPR reduction methods combined with filtering and windowing techniques are also shown to be effective

UAV assisted integrated sensing and communications for Internet of Things : 3D trajectory optimization and resource allocation

High-mobility unmanned aerial vehicles (UAVs) can serve as dual-function aerial service platforms for the Internet of Things (IoT), providing both sensing and communication services for IoT nodes without a base station (BS), particularly in emergency situations. In this paper, a UAV-assisted integrated sensing and communications (ISAC) system is proposed for IoT, which simultaneously senses the status information around the IoT and sends the sensing information to both the IoT nodes and a data collection center. In order to assess the sensing performance of ISAC, the radar estimation rate is introduced as a significant metric from the perspective of information theory. Considering the mutual interference between sensing and communications, the radar estimation rate is maximized through the coordinated optimization of UAV task scheduling, transmit power allocation, and 3D flight parameters under the constraint of communication rate. The formulated non-convex mixed-integer programming problem is divided into three subproblems, including UAV task scheduling optimization, UAV sensing and communication power optimization, and UAV 3D flight parameters optimization. The optimal solutions can be achieved by proposing a three-layer iterative optimization algorithm to optimize the three subproblems iteratively. The simulation results show that the radar estimation rate can well measure the sensing performance of the ISAC, which can be effectively improved by optimizing the 3D UAV flight parameters