A tree‐based algorithm for virtual infrastructure allocation with joint virtual machine and network requirements

Cloud providers have introduced the on‐demand provisioning of virtual infrastructures (VIs) to deliver virtual networks of computing resources as a service. By combining network and computing virtualization, providers allow traffic isolation between hosted VIs. Taking advantage of this opportunity, tenants have deployed private VIs with application‐optimized network topologies to increase quality of experience of final users. One of the main open challenges in this scenario is the allocation of physical resources to host VIs in accordance with quality of service computing (eg, virtual CPUs and memory) and network requirements (guaranteed bandwidth and specific network topology). Moreover, a VI can be allocated anywhere atop a network datacenter, and because of its NP‐hard complexity, the search for optimal solutions has a limited applicability in cloud providers as requesting users seek an immediate response. The present work proposes an algorithm to accomplish the VI allocation by applying tree‐based heuristics to reduce the search space, performing a joint allocation of computing and network resources. So as to accomplish this goal, the mechanism includes a strategy to convert physical and virtual graphs to trees, which later are pruned by a grouped accounting algorithm. These innovations reduce the number of comparisons required to allocate a VI. Experimental results indicate that the proposed algorithm finds an allocation on feasible time for different cloud scenarios and VI topologies, while maintaining a high acceptance rate and a moderate physical infrastructure fragmentation.     

不确定信息下的评估指标权重配置方法

为了解决在数据不确定条件下极大熵权重配置模型找寻最优权重的问题,引入云模型将具有随机性和模糊性的数据转换为定性的数字特征。以此为基础在逼近理想解排序方法架构下定义了正负理想解,基于改进的欧氏距离设计了正负向距离,提出了多评估对象相对能力量化模型,并在极大熵准则下构建了新的权重配置模型,使得评估对象相对能力大小排序的专家知识得到充分运用。通过分析采用了模拟退火算法实现最优指标权重的搜寻,解决了原先算法模型不能应用于非凸优化情形的问题,进而给出科学合理的权重配置方案。仿真实例表明,在多次迭代后找寻到最优权重熵为1.377 7,满足要求,证明了所提算法的有效性。     

Joint optimization of power allocation and relay location for regenerative relaying with multi‐antenna reception at destination

We consider joint power allocation (PA) and relay positioning in a dual‐hop regenerative relay system with multiple antennas equipped at the destination. With a fixed relay location, an adaptive PA strategy at the source and relay under a sum power constraint that minimizes the system outage probability is presented. With a fixed PA strategy, the optimal relay location is derived. We then propose to jointly optimize PA and relay location. It is shown that employing more destination antennas and/or choosing an appropriate relay location can significantly save the power needed at the relay. Numerical results are presented to demonstrate the performance of the proposed PA and relay positioning algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

战术异构网络互联端到端QoS研究

战术异构通信网络的融合,需要更完善的端到端服务质量（Quality of Service,QoS）体系。在研究和分析战术异构通信网特点后,分析总结出战术异构通信网络端到端QoS研究内容,并参考下一代网络QoS框架结构,提出了基于策略的QoS架构模型。最后阐述了所涉及的关键技术,用于解决在战术异构通信网中不同业务在跨网络传输时端到端QoS保证,合理利用无线网络资源,有效提高数据传输性能等问题。     

论同步提升GSM数据业务流量与降低无线利用率

针对数据业务快速发展对网络质量造成的影响,从无线信道资源管理、分配和占用机制、数据业务承载性能与效率提升等方面研究,提出了＂三阶段四原则＂的优化思路,根据不同业务导向分配资源,在确保数据业务用户感知的前提下,降低了无线利用率,提升了GSM网络性能     

Joint power allocation and subcarrier pairing in OFDM-based cooperative relaying system

This paper proposes rate-maximized (MR) joint subcarrier pairing (SP) and power allocation (PA) (MR-SP&PA),a novel scheme for maximizing the weighted sum rate of the orthogonal-frequency-division multiplexing (OFDM) relaying system with a decode-and-forward (DF) relay.MR-SP&PA is based on the joint optimization of both SP and power allocation with total power constraint,and formulated as a mixed integer programming problem in the paper.The programming problem is then transformed to a convex optimization problem by using continuous relaxation,and solved in the Lagrangian dual domain.Simulation results show that MR-SP&PA can maximize the weighted sum rate under total power constraint and outperform equal power allocation (EPA) and proportion power allocation (PCG).     

Secrecy sum rate optimization in MISO nonorthogonal multiple access systems with orthogonal space‐time block coding

In this paper, we investigate the secrecy sum rate optimization problem for a multiple‐input single‐output (MISO) nonorthogonal multiple access (NOMA) system with orthogonal space‐time block codes (OSTBC). This system consists of a transmitter, two users, and a potential eavesdropper. The transmitter sends information by orthogonal space‐time block codes. The transmitter's precoder and the power allocation scheme are designed to maximize achievable secrecy sum rate subject to the power constraint at the transmitter and the minimum transmission rate requirement of the weak user. We consider two cases of the eavesdropper's channel condition to obtain positive secrecy sum rate. The first case is the eavesdropper's equivalent channel is the weakest, and the other is the eavesdropper's equivalent channel between the strong user and weak user. For the former case, we employ the constrained concave convex procedure (CCCP)‐based iterative algorithm with one‐dimensional search. While for the latter, we adopt the method of alternating optimization (AO) between precoder and power allocation. We solve a semidefinite programming to optimize the precoder and drive a closed‐form expression of power allocation. The simulation results obtained by our method demonstrate the superiority of our proposed scheme.     

J‐divergence‐based Power Allocation in Wireless Sensor Networks with Distributed Detection under Correlated Noises

In this paper, the power allocation problem in a wireless sensor network (WSN) with binary distributed detection is considered. It is assumed that the sensors independently transmit their local decisions to a fusion center (FC) through a slow fading orthogonal multiple access channel (OMAC), where, in every channel, the interferences from other devices are considered as correlated noises. In this channel, the associated power allocation optimization problem with equal power constraint is established between statistical distributions under different hypotheses by using the Jeffrey divergence (J‐divergence) as a performance criterion. It is shown that this criterion for the power allocation problem is more efficient compared to other criteria such as mean square error (MSE). Moreover, several numerical simulations and examples are presented to illustrate the effectiveness of the proposed approach.     

Exploiting IP‐layer traffic prediction analytics to allocate spectrum resources using swarm intelligence

Elastic optical networks emerge as a reliable backbone platform covering the next‐generation connectivity requirements. It consists of advanced enabling components that provide the ability for extensive configuration leading to performance improvement in many areas of interest. Higher layer analytics like data from IP traffic prediction can assist in the process of allocating resources at the optical layer. This way, light connections are established more efficiently while targeting specific performance goals. For that purpose, an algorithm is designed and evaluated that exploits traffic prediction of data transfers between nodes of an optical metro or backbone network. Next, it utilizes adaptive functionality based on particle swarm optimization to find paths with available spectrum resources. These resources can facilitate more efficiently the future traffic demand, since traffic prediction data are considered when finding the related paths. The innovative resource allocation method is evaluated using small and very large real topologies. It scales (in execution time and resource usage) according to node increase, executes in feasible time frames, and reduces transponder utilization resulting to increased energy efficiency.     

基于无线网络性能数据的用户业务行为分类与感知评估技术

提供了一种基于无线网络性能数据的用户业务分类方法,同时提供完备的感知分析技术.通过研究各类应用的具体资源占用特征,研究人员设计出有效的业务类型区分方法;同时考察不同应用对各种KPI的敏感程度以制订准确的感知评估规则.经过大量测试验证,该方法具有分类评估准确,效率高,处理开销小等优点.