数据中心网络中相关感知流量整合算法

随着大数据、云计算不断融入人们的日常生活,作为支撑其发展的基础设施--数据中心网络的能耗也在急剧增长。为了解决这个问题,学术界提出了能量感知路由（Energy-Aware Routing,EAR）。EAR的主要思想是通过将流量需求聚集在网络链路的子集,并睡眠未使用的网络设备以节省能量。但是在流量低谷时期频繁地切换网络设备模式可能会导致网络振荡甚至网络性能下降。因此提出了一种相关感知流量整合（Correlation-Aware Traffic Consolidation,CATC）算法。提出了基于软件定义网络（Software Defined Network,SDN）的CATC模型,即在流量整合时考虑了流之间的相关性,并结合链路速率来实现更高的节能。在流量约束和链路容量约束下将CATC模型转换为一个最优流量分配问题,并提出CATC算法来求解。仿真结果显示,与现有的节能算法相比,CATC算法在仅仅增加极少网络延迟的同时可以为数据中心网络节省大约45%的能量。     

数据中心网络路由研究进展

随着云计算的迅速发展,运营商对数据中心的需求与日俱增.作为数据中心网络的关键技术,路由负责在数据中心内部以及数据中心之间为流量选路,为不同服务质量要求的流量提供差异化的路由转发服务.当数据中心规模比较大时,由于应用不可预估的通信流量以及数据中心网络的拓扑特点,传统因特网路由方法不能提供令人满意的高吞吐率和资源利用率,网...     

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.     

Optimal online control for sleep mode in green base stations

In this paper, we investigate network sleep mode schemes for reducing energy consumption of radio access networks. We first propose, using Markov Decision Processes (MDPs), an optimal controller that associates to each traffic an activation/deactivation policy that maximizes a multiple objective function of the Quality of Service (QoS) and the energy consumption. We focus on a practical implementation issue, namely the ping pong effect resulting in unnecessary ON/OFF oscillations, that may affect the stability of the system. We illustrate our results numerically using theoretical models of the radio access network, and apply the developed mechanisms on a large-scale network simulator. Knowing that an offline optimization is not suitable for a large-scale network nor does it fit all traffic configurations, we propose, using an online controller that derives dynamically the optimal policy based on the dynamics of users in the cell. The design of our online controller is based on a simple ∊-greedy algorithm and learns the optimal threshold policy for activation/deactivation of network resources.     

Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks

Rapid penetration of small, customized wireless devices and enormous growth of wireless communication technologies has already set the stage for large-scale deployment of wireless sensor networks. While the need to minimize the energy consumption has driven significant researches in wireless sensor networks, offering some precise quality of service (QoS) for multimedia transmission over sensor networks has not received significant attention. However, the emerging new applications like video surveillance, telemedicine and traffic monitoring needs transmission of wireless multimedia over sensor networks. Naturally, offering some better QoS for wireless multimedia over sensor networks raises significant challenges. The network needs to cope up with battery-constraints, while providing improved QoS (end-to-end delay and bandwidth requirement). This calls for a suitable sensory MAC protocol capable of achieving application-specific QoS. In this paper, we have proposed a new QoS-based sensory MAC protocol, which not only adapts to application-oriented QoS, but also attempts to conserve energy without violating QoS-constraints. Performance modeling, analysis and simulation results demonstrate that the proposed protocol is capable of providing lower delay and better throughput, at the cost of reasonable energy consumption, in comparison to other existing sensory MAC protocols.     

基于M/M/c排队模型的云计算中心能耗管理策略

由于云计算中心在降低能耗的同时还需要保证服务质量（QoS）,针对用户访问云计算中心的排队机制,给出一种云计算任务排队模型,在此基础上提出一种基于M/M/c排队过程的云计算中心能耗管理算法,通过求解该模型获得了平均等待时间、阻塞概率等性能指标进而建立系统的能耗模型。同时用参量ERP（Energy-Response time Product）作为排队网络的反馈量,引入反馈策略及服务器休眠预留机制,动态调整云计算中心服务器服务数。仿真结果表明,与其他策略进行比较该策略能够在保证QoS值的情况下,有效降低系统的能耗,避免了服务器资源浪费。     

一种基于QoS参数归约的云计算环境能效评估方法

高性能、低功耗且具有QoS保障的高能效问题是云计算领域的一个研究难点。目前的研究主要是通过限定一个约束条件寻求另外指标的最优来实现三者之间的折衷或均衡,缺乏一种有效的能效计算方法和评估模型将三者整合,以更好地描述云环境能效的“程度”。提出一种云环境下QoS参数的归约方法和加权的能效模型,把系统性能作为一个关键因素引入QoS,并将离散的多个QoS参数度量值归约到同一个量纲区域内,获得评价权重矩阵,求得用户最终的QoS评价值,以单位能耗所提供的整体QoS水平值作为能效值,并且建立云数据中心的能效分级标识,最终将云环境下能效值刻画为一个定性的概念,实现了对云环境下能效的定性评估。此外,分别对单机环境和同构、异构的云计算环境中云数据中心的能效进行了评估分析,并进行了实验验证。实验结果表明,所提出的能效模型和评估方法在评价云系统的QoS水平和能源消耗方面是有效的。     

Energy efficient and QoS based routing protocol for wireless sensor networks

The increasing demand for real-time applications in Wireless Sensor Networks (WSNs) has made the Quality of Service (QoS) based communication protocols an interesting and hot research topic. Satisfying Quality of Service (QoS) requirements (e.g. bandwidth and delay constraints) for the different QoS based applications of WSNs raises significant challenges. More precisely, the networking protocols need to cope up with energy constraints, while providing precise QoS guarantee. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. In many of these applications (such as multimedia applications, or real-time and mission critical applications), the network traffic is mixed of delay sensitive and delay tolerant traffic. Hence, QoS routing becomes an important issue. In this paper, we propose an Energy Efficient and QoS aware multipath routing protocol (abbreviated shortly as EQSR) that maximizes the network lifetime through balancing energy consumption across multiple nodes, uses the concept of service differentiation to allow delay sensitive traffic to reach the sink node within an acceptable delay, reduces the end to end delay through spreading out the traffic across multiple paths, and increases the throughput through introducing data redundancy. EQSR uses the residual energy, node available buffer size, and Signal-to-Noise Ratio (SNR) to predict the best next hop through the paths construction phase. Based on the concept of service differentiation, EQSR protocol employs a queuing model to handle both real-time and non-real-time traffic.     

A Cross-Layer Optimization and Design approach under QoS constraints for green IP over WDM networks

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demands. It is important and valuable to find a way to save power for such networking systems. In this paper, we propose a Cross-Layer Optimization and Design (CLOD) approach to improve the energy efficiency of Internet Protocol (IP) over Wavelength Division Multiplexing (WDM) backbone networks under QoS constraints. CLOD makes use of the skew spatial distribution (i.e. 80–20 law) of traffic demands to construct the virtual topology of IP layer with a consideration of the cross-layer resource constraints. Also, CLOD takes advantage of the constraint-based routing to satisfy the QoS constraints in terms of allowed maximum hop count and allowed maximum link utilization. Taking into account the connection between base network design and network operation, CLOD dimensions the base network by using pattern of network operation and optimizes network power consumption by reconfiguring network to adapt traffic variation and the available resources. Simulation results indicate that CLOD can save power significantly and achieve near energy-proportional networks. In addition, an analysis on trade-off performance between the hop count and power consumption is given.     

Environmental-aware virtual data center network

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.     

Design and performance analysis of an inductive QoS routing algorithm

Routing mechanism is key to the success of large-scale, distributed communication and heterogeneous networks. Consequently, computing constrained shortest paths is fundamental to some important network functions such as QoS routing and traffic engineering. The problem of QoS routing with multiple additive constraints is known to be NP-complete but researchers have been designing heuristics and approximation algorithms for multi-constrained paths algorithms to propose pseudo-polynomial time algorithms. This paper introduces a polynomial time approximation quality of service (QoS) routing algorithm and constructs dynamic state-dependent routing policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize lexicographically various QoS criteria. The originality of our approach is based on the fact that our system is capable to take into account the dynamics of the network where no model of the network dynamics is assumed initially. Our approach samples, estimates, and builds the model of pertinent aspects of the environment which is very important in heterogeneous networks. The algorithm uses a model that combines both a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. Multiple paths are searched in parallel to find the K best qualified ones. To improve the overall network performance, a load adaptive balancing policy is defined and depends on a dynamic traffic path probability distribution function. We conducted a performance analysis of the proposed QoS routing algorithm using OPNET based on a platform simulated network. The obtained results demonstrate substantial performance improvements as well as the benefits of learning approaches over networks with dynamically changing traffic.     

Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.     

Energy-aware task scheduling in mobile cloud computing

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.     

An energy-efficient virtual machine placement and route scheduling scheme in data center networks

The increasing requirements of big data analytics and complex scientific computing impose significant burdens on cloud data centers. As a result, not only the computation but also the communication expenses in data centers are greatly increased. Previous work on green computing in data centers mainly focused on the energy consumption of the servers rather than the communication. However, for those emerging applications with big data-flows transmission, more energy consumption could be consumed by communication links, switching and aggregation elements. To this end, based on data-flows’ transmission characteristics, we proposes a novel Job-Aware Virtual Machine Placement and Route Scheduling (JAVPRS) scheme to reduce the energy consumption of data center networks (DCN) while still meeting as many network QoS (Quality of Service) requirements as possible. Our proposed scheme focuses on not just migrating large data flows, but also integrating small data flows to improve the utilization rate of the communication links. With more idle switches turned off, DCN’s energy consumption will thus be reduced. Besides the data flows’ migration and integration, the Traffic Engineering (TE) technique is also applied to decrease the transmission delay and increase the network throughput. To evaluate the performance of our proposed scheme, a number of simulation studies are performed. Compared to the selected benchmarks, the simulation results showed that JAVPRS can achieve 22.28%–35.72% energy saving while reducing communication delay by 5.8%–6.8% and improving network throughput by 13.3%.     

CGMP: cloud-assisted green multimedia processing

With continued advancements of mobile computing and communications, emerging novel multimedia services and applications have attracted lots of attention and been developed for mobile users, such as mobile social network, mobile cloud medical treatment, mobile cloud game. However, because of limited resources on mobile terminals, it is of great challenge to improve the energy efficiency of multimedia services. In this paper, we propose a cloud-assisted green multimedia processing architecture (CGMP) based on mobile cloud computing. Specifically, the tasks of multimedia processing with energy-extensive consumption can be offloaded to the cloud, and the face recognition algorithm with improved principal component analysis and nearest neighbor classifier is realized on CGMP based cloud platform. Experimental results show that the proposed scheme can effectively save the energy consumption of the equipment.     

Power consumption analysis of access network in 5G mobile communication infrastructures — An analytical quantification model

Energy consumption growth of the fifth-generation (5G) mobile network infrastructure can be significant due to the increased traffic demand for a massive number of end-users with increasing traffic volume, user density, and data rate. The emerging technologies of radio access networks (RAN), e.g., millimeter-wave (mm-wave) communication and large-scale antennas, make a considerable contribution to such an increase in energy consumption. The multiband 2-tier heterogeneous network (HetNet), cloud radio access network (C-RAN), and heterogeneous cloud radio access network (H-CRAN) are considered the prospective RAN architectures of the 5G mobile communication. This paper explores these novel architectures from the energy consumption and network power efficiency perspective considering the varying high volume traffic load, the number of antennas, varying bandwidth, and varying density of low power nodes (LPNs), integrated with mm-wave communication and large-scale multiple antennas. The architectural differences of these networks are highlighted and power consumption analytical models that characterize the energy consumption of radio resource heads (RRHs), base band unit (BBU) pool, fronthaul, macro base station (MBS), and small cell base stations (SCBs) in HetNet, C-RAN, and H-CRAN are developed. The network power efficiency with the consideration of propagation environment and network constraints is investigated to identify the energy-efficient architecture for the 5G mobile network. The simulation results reveal that the power consumption of all these architectures increases in all considered scenarios due to an increase in power consumption of radio frequency components and computation power. Moreover, CRAN is the most energy-efficient RAN architecture due to its cooperative processing and decreased cooling and site support devices and H-CRAN consumes most of the energy compared to other 5G RAN architectures mainly due to a high level of heterogeneity.     

一种无线传感器网络以数据为中心的QoS路由协议

提出一种以数据为中心的QoS路由协议(DDQP).DDQP支持两种QoS度量:可靠性和传输延迟;采用交叉层优化技术将传感器网络无线信道通信模型作为路由协议设计的依据,有效的节约了网络能源消耗;采用反压力重新路由机制在满足业务QoS的前提下尽可能均匀使用网络中节点的能源,不仅延长了网络生命期,而且有效的控制网络拥塞;采用以数据为中心的数据分发模式,具有良好的可扩展性.描述了DDQP设计的理论依据,并通过仿真实验验证了DDQP的高效可行性.     

面向延迟敏感型物联网应用的计算迁移策略

针对云计算网络延迟较长、能耗过高和边缘服务器计算资源有限的问题,提出了一种提高延迟敏感型物联网（IoT）应用服务质量（QoS）的边缘-云合作的漂移加惩罚计算迁移策略（DPCO）。首先,建立物联网-边缘-云系统模型,对业务模式、计算任务所经历的传输延迟和计算延迟、系统产生的计算能耗和传输能耗等进行数学建模;然后,以系统能耗和任务平均延迟为优化目标,以边缘服务器的队列稳定性为限制条件构建边缘-云合作的计算迁移优化模型;接着,以优化目标为惩罚函数,基于李雅普诺夫稳定性理论推导出计算迁移优化模型的漂移加惩罚函数特性。最后,基于推导结果提出了DPCO计算迁移算法,通过每时隙选择使当前漂移加惩罚函数最小化的计算迁移策略来降低长期的单位时间能耗和缩短系统平均延迟。与轻流雾处理（LFP）、基准边缘计算（EC）、基准云计算（CC）策略相比,DPCO的系统能耗最低,约是CC策略的2/3;任务平均延迟也最小,可减少为CC的1/5。实验结果表明,DPCO能够有效降低边缘-云计算系统的能量消耗,减少计算任务的端到端延迟,满足延迟敏感型IoT应用的QoS要求。     

基于注意力时空卷积和A2C的虚拟机主动容错优先迁移决策

针对边缘云环境的自动化和分布式特性、高度不可靠性及易变的工作负载问题,提出基于注意力时空卷积和A2C的虚拟机主动容错优先迁移决策模型AST-A2C。首先,采用带有注意力机制的长短期记忆网络(LSTM)提取各主机的时序特征,根据时序特征和多主机间的交互信息构建图网络,再利用图注意力网络(GAT)提取网络中不同主机间的关联信息,将其用于主机的故障信息编码。其次,设计可动态建立模型并不断生成改进决策的A2C模块,联合故障编码信息和调度决策信息进行优先迁移决策。最后,构建满足不同用户QoS要求和应用程序设置的自适应损失函数来优化调度决策。实验结果表明,该模型在故障检测、能源消耗、时延敏感性等方面优于最先进的基线,是提高边缘云计算可靠性的理想选择。