Traffic Allocation Strategy Research Articles

Bandwidth partition and allocation for efficient spectrum utilization in cognitive communications

Conventional cognitive communications rely heavily on the smartness of secondary (unlicensed) users (SUs) to achieve high spectrum utilization, which involves the optimization of the SUs' policies and behaviors for dynamic spectrum access, power allocation among multiple channels, etc. Due to the inherent randomness of the primary users' (PUs') transmission, those efforts inevitably increase the implementation complexity and sensing overheads of the SUs, and in turn lower the spectrum utilization efficiency. In this paper, we try to change the focus from SU to PU. A cooperative traffic allocation strategy for PU, together with the non-uniform bandwidth partition, is employed to regularize the PU's resource occupancy pattern without compromising its performance, and to maximize the spare bandwidth for the SU at the same time. We first study the capacity based optimization problem (COP) together with the fully polynomial time approximation scheme (FPTAS) for an approximation guarantee of the global optimum. Then we analyze the subcarrier based optimization problem as the surrogate problem of COP, which can be solved by a greedy algorithm exactly. Both the theoretical analysis and the numerical simulations demonstrate the effectiveness of those methods to achieve the performance that almost identical to that of the global optimum solution.

A Distributed ADMM Approach With Decomposition-Coordination for Mobile Data Offloading

Mobile data offloading allows alternative network systems such as Wi-Fi hotspot access points (APs) to offload the traffic that is originally targeted for the cellular network operators (CNOs). It can alleviate the cellular network congestions and enhance users' quality-of-service. One of the main challenges of the mobile data offloading is to develop simple and distributed traffic allocation strategies that can optimally allocate the cellular data traffic from multiple CNOs to the APs. In this work, we propose a distributed optimization framework with decomposition-coordination to solve this problem. In our framework, the utility maximization problem of the cellular network system with mobile data offloading is formulated as a nonsmooth convex optimization problem. We then divide this problem into a set of subproblems, and each of them is solved by a CNO or an AP using its local information. All the subproblems are coordinated with each other by a virtual data offloading coordinator (VDOC), which can collect the intermediate calculation values from CNOs and APs. The VDOC will then feedback the coordination results calculated from the collected values to the corresponding CNOs and APs. We propose two distributed algorithms that can achieve the global optimization solution under different scenarios. The first one is the multiblock proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM). In this algorithm, the communication between the VDOC and CNOs or APs is assumed to be fully synchronized, and the VDOC will only feedback the coordination result after successfully receiving all the values from APs and CNOs. We relax this assumption by introducing the second algorithm referred to as the distributed asynchronized ADMM (Async-ADMM) algorithm. In this algorithm, the coordination between the VDOC and APs or CNOs does not need to be perfectly synchronized and the VDOC will feedback a coordination result whenever it receives a value from at least one AP or CNO. We prove that both algorithm can achieve the global optimal solution. We present the numerical results to verify the performance of our proposed approaches under various network settings and conditions.

Energy Efficiency of Cloud Virtual Machines: From Traffic Pattern and CPU Affinity Perspectives

Networking and machine virtualization play critical roles in the success of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past, including the impact from network traffic. When it comes to virtual machines (VMs) in cloud data centers, the interplay between energy consumption and network traffic, however, becomes much more complicated. Through real-world measurement on both Xen- and KVM-based platforms, we show that these state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling network transactions, generating excessive interrupt requests with ceaseless context switching, which in turn, increases energy consumption. Even when a physical machine is in an idle state, its VM's network transactions will incur nontrivial energy consumption. More interestingly, the energy consumption significantly varies with traffic allocation strategies and virtual CPU affinity conditions , which was not seen in conventional physical machines. Looking closely into the virtualization architectures, we then pinpoint the root causes and examine that our measurement results can be extended for various network configurations. Moreover, we also provide initial solutions toward optimizing energy consumption in virtualized environments.

Traffic Allocation Strategies in WSS-Based Dynamic Optical Networks

Elastic optical networking (EON) is a viable solution to meet future dynamic capacity requirements of Internet service provider and inter-datacenter networks. At the core of EON, wavelength selective switches (WSSs) are applied to individually route optical circuits, while assigning an arbitrary bandwidth to each circuit. Critically, the WSS control scheme and configuration time may delay the creation time of each circuit in the network. In this paper, we first detail the WSS-based optical data-plane implementation of a metropolitan network test-bed. Then, we review a software-defined networking (SDN) application designed to enable dynamic and fast circuit setup. Subsequently, we introduce a WSS logical model that captures the WSS time-sequence and is used to estimate the circuit-setup response time. Then, we present two batch service policies that aim to reduce the circuit-setup response time by bundling multiple WSS reconfiguration steps into a single SDN command. Resulting performance gains are estimated through simulation.

QoS Based and Energy Aware Multi-Path Hierarchical Routing Algorithm in WSNs

In hierarchical networks, nodes are separated to play different roles such as CHs and cluster members. Each CH collects data from the cluster members within its cluster, aggregates the data and then transmits the data to the sink. Each algorithm that is used for packet routing in quality of service (QoS) based applications should be able to establish a tradeoffs between end to end delay parameter and energy consumption. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. We propose a QoS based and Energy aware Multi-path Hierarchical Routing Algorithm in wireless sensor networks namely QEMH. In this protocol, we try to satisfy the QoS requirements with the minimum energy via hierarchical methods. Our routing protocol includes two phase. In first phase, performs cluster heads election based on two parameters: node residual energy and node distance to sink. In second phase, accomplishes routes discovery using multiple criteria such as residual energy, remaining buffer size, signal-to-noise ratio and distance to sink. When each node detect an event can send data to the CH as single hop and CH to the sink along the paths. We use a weighted traffic allocation strategy to distribute the traffic amongst the available paths to improve the end to end delay and throughput. In this strategy, the CH distributes the traffic between the paths according to the end to end delay of each path. The end to end delay of each path is obtained during the paths discovery phase. QEMH maximizes the network lifetime as load balancing that causes energy consume uniformly throughout the network. Furthermore employs a queuing model to handle both real-time and non-real-time traffic. By means of simulations, we evaluate and compare the performance of our routing protocol with the MCMP and EAP protocols. Simulation results show that our proposed protocol is more efficient than those protocols in providing QoS requirements and minimizing energy consumption.

Delay minimisation in multipath routing using intelligent traffic distribution policies

A key component of an efficient multipath routing is the optimal traffic allocation strategy that deals with how the traffic should be distributed among the multiple paths. In this study, two intelligent traffic distribution policies that minimise the experienced delay by the end user are proposed. For such delay minimisation, the authors investigate and compare average delay minimisation and maximum delay minimisation (MDM) over all the paths. It will be shown that MDM is a more efficient strategy for end-to-end delay minimisation. Based on this fact, an intelligent algorithm that can minimise the maximum delay of all paths is proposed using an adaptive traffic sharing scheme.

An input traffic allocation strategy and an efficient transmission technique for collisions-free WDM ring MANs

This paper presents a wavelength division multiplexing multi-ring architecture with a separate control wavelength for metropolitan area networks. We study the critical problem of low fiber bandwidth utilization that many access ring protocols introduce, especially at high load conditions. In our study this problem is effectively faced by: (1) applying an efficient slotted access algorithm to avoid both data wavelengths and receiver collisions, (2) introducing a number of buffers at each node to effectively allocate the incoming traffic into them, and (3) applying an efficient algorithm for buffer selection for transmission that combines the priority criteria of packet age and receiver collisions avoidance. In this way, we obtain maximum fiber bandwidth utilization. Especially, our aim is to achieve not only significant throughput enhancement, but also essential dropping probability and total delay reduction. Also, the number of transmission buffers per node is investigated in order to manage maximum throughput improvement, while considering the financial cost. Finally, a discrete-event simulation model based on Poisson statistics is developed for the performance measures evaluation.

QoS and data relaying for wireless sensor networks

In this paper we study the effects of data relaying in wireless sensor networks (WSNets) under QoS constraints with two different strategies. In the first, data packets originating from the same source are sent to the base station possibly along several different paths, while in the second, exactly one path is used for this purpose. The two strategies correspond to splitting and not splitting relaying traffic, respectively. We model a sensor network architecture based on a three-tier hierarchy of nodes which generalizes to a two-tier WSNet with multiple sinks. Our results apply therefore to both types of networks. Based on the assumptions in our model, we describe several methods for computing relaying paths that are optimal with respect to energy consumption and satisfy QoS requirements expressed by the delay with which data are delivered to the base station(s). We then use our algorithms to perform an empirical analysis that quantifies the performance gains and losses of the splittable and unsplittable traffic allocation strategies for WSNets with delay-constrained traffic. Our experiments show that splitting traffic does not provide a significant advantage in energy consumption, but can afford strategies for relaying data with a lower delay penalty when using a model based on soft-delay constraints.

Class Dependent Traffic Allocation in a LEO Satellite Network

Traffic allocation strategy becomes a significant factor in optimization of bandwidth usage of telecommunication resources, especially with increasing use of broadband applications. Allocation strategy in dynamic LEO (Low Earth Orbital) satellite communication network is studied, to improve their Quality of Service (QoS). Traffic allocation control is performed to provide a near optimal utilization of their Inter Satellite Links (ISLs). A combination of two algorithms is used to allocate traffic in ISLs. Empirical analysis is performed to examine performance of the proposed algorithm, GALPEDA. Result shows that the proposed algorithm is useful for traffic allocation of multiclass traffic in LEO satellite communication.