基于萤火虫群优化的虚拟机放置方法

利用虚拟机放置策略对云数据中心的物理资源利用效率进行优化十分必要。提出了基于萤火虫群优化的虚拟机放置（glowworm swarm optimization based VM placement,Gso-wmp）方法。GSO-VMP方法将物理主机的处理器使用效率表示为荧光素值，当一个虚拟机被放置到物理主机上时，该物理主机的荧光素值都要进行更新；能够在局部径向范围内搜索到更多的可用物理主机，完成虚拟机放置，减少了虚拟机的迁移次数，从而间接地节省了物理主机的能量消耗。使用CloudSim作为GSO-VMP的仿真环境进行仿真，实验结果表明，GSO-VMP方法使得云数据中心的能耗降低、多维物理资源利用率提高。     

面向节点异构的能耗感知虚拟网络映射算法

在底层网络节点异构的环境中,能耗优化的虚拟网络映射问题并不是最小化工作节点和链路数。该文针对此问题,构建底层网络节点和链路的负载能耗模型,并以能耗最优为目标,建立虚拟网络映射问题的数学模型,提出一种能耗感知虚拟网络映射算法。该算法在节点映射阶段以最小化能耗和协调链路映射为原则,将虚拟节点映射至综合资源能力最大的底层节点上,并采用改进的能耗感知k最短路径法进行链路映射。仿真结果表明,该算法显著减少虚拟网络映射的能耗,且底层网络节点异构性越大,能耗优势更为明显。     

异构备份式的虚拟网映射方法研究

在云计算和数据中心环境中,底层单个物理服务器的失效将对上层虚拟网络的服务性能造成很大的影响,现有利用冗余备份的方法能够在一定程度上降低底层物理设备失效带来的影响,但未考虑到物理服务器的同构性所带来的问题,为此,该文提出一种异构备份式的虚拟网映射方法。首先,只对关键的虚拟机进行冗余备份,降低备份资源的开销;然后,确保提供备份虚拟机的物理服务器与原物理服务器的系统类型的异构性,提高虚拟网的弹性能力;最后,以最小化链路资源开销作为虚拟网的映射目标,进一步降低备份资源的开销。实验表明,该方法在保证虚拟网络映射性能的前提下,能够大大提高虚拟网络的弹性能力。     

WDM网络中基于物理损伤的组播节能保护算法

针对存在物理损伤约束的生存性组播网络中的能耗问题,提出了一种基于物理损伤的组播节能保护算法。该算法通过修改工作、保护链路的能耗代价,优化工作、保护路径选择,再进行物理损伤约束判断,在工作、保护路径满足物理损伤约束的条件下实现网络能耗最小化。仿真结果表明,该算法在满足物理损伤约束的条件下,可以降低网络能耗。     

基于5G接入网络的多优先级虚拟网络功能迁移开销与网络能耗联合优化算法

针对5G接入网络中虚拟网络功能(VNF)部署完成后,其资源需求发生动态变化,导致网络中物理机(PM)资源利用率过高或过低这一问题,该文首先将网络中PM的资源使用情况划分5个不同分区,提出一种多优先级VNF迁移请求队列调度模型。其次基于该模型,对VNF迁移开销的最小化及网络能耗的最小化建立联合优化模型。最后提出一种基于5G接入网络的多优先级VNF迁移开销与网络能耗联合优化算法对其进行求解。仿真结果表明,该算法在有效实现VNF迁移开销与网络能耗折中的同时,提高了PM资源利用率,保证了PM性能并均衡各PM负载。     

基于布谷鸟搜索的虚拟机放置算法

针对虚拟机放置问题,引入了布谷鸟搜索算法。首先,将虚拟机放置方案映射为鸟巢,并按照适应度高低将其分成顶巢和底巢。其次,通过扰动函数对底巢和顶巢进行扰动。最后,通过选择、迭代得到最佳放置方案。该算法可用于云数据中心的物理机整合,使放置物理机数量最小化。通过Cloudsim进行仿真,仿真结果表明,比起重排序分组遗传算法、分组遗传算法、改进的最小加载和改进的降序首次适应算法,提出的方法不仅避免了局部最优,而且具有更高的性能优势。     

基于遗传算法的虚拟机动态放置研究

在云数据中心（IDC）,虚拟机部署（VMP）策略是指如何在数据中心有限的物理资源中内放置合理的虚拟机（VM）。高效的虚拟机部署策略将更好的实现物理资源的整合和利用,最大化实现资源利用和能源节约的效果。该文中,根据虚拟机的存储和CPU两种资源为目标,研究资源利用率最大化的虚拟机放置策略。该策略基于遗传算法,根据虚拟机实际资源（内存和CPU）需求动态,动态的为虚拟机分配资源,实现最大限度降低数据中心资源利用不足和过度利用等概率。在最后,使用CloudSim仿真软件进行模拟实验,通过实验证明,较最佳拟合递减算法（BFD）相比,使用遗传算法对虚拟机进行动态分配,数据中心的资源利用率有较大的提升,同时也说明将多资源需求作为虚拟机放置策略考虑的重要性。     

优先级融合动态电压频率调节的云计算任务调度算法研究

为了在云计算任务调度过程中保证云设备效率的同时提高资源利用率,提出了一种基于优先级和动态电压频率调节的调度算法。首先,为调度算法定义了问题模型以规范异构服务器的性能;然后,利用优先级为任务提供可行的组合或调度;最后,利用动态电压频率调节为服务器提供适当的电压和频率供应,并且向虚拟机管理器发送分配结果。利用可扩展的模拟工具Cloud Sim进行实验评估了本文方法的能耗和调度时间,结果表明,本文方法的执行时间与MMF-DFVS方法相当,而能耗比MMF-DFVS降低了5%-25%。     

多目标的Internet路由优化控制算法

研究通过优化链路权值以控制网络路由来实施流量工程.以网络拥塞最小化和时延最小化为流量工程目标,建立了多目标的全局路由优化数学模型.求解该问题是NP困难的,提出一种混沌群搜索优化算法进行求解.算法采用群局部搜索,利用混沌变量产生一组分布好的初始解,并在邻域搜索进程中应用扩展贪心思想,提高了算法的全局搜索能力.仿真结果表明所提算法能够有效减少由于流量分布不平衡造成的网络拥塞,同时限制长路径,提高了网络性能.     

云数据中心面向低能源消耗的虚拟机分配策略

虚拟机分配策略是提高云数据中心的物理主机利用率和降低能源消耗的关键技术。文中提出云数据中心面向低能源消耗的虚拟机分配策略LEC-VM。LEC-VM包括2个组成部分:虚拟机放置策略和虚拟机迁移优化策略。通过放置策略将云数据中心的虚拟机分配到最合适的物理节点之上,保证整个系统的CPU利用率低于一个给定的阈值;通过迁移优化策略,根据系统的当前状态动态迁移虚拟机,对物理主机的资源进行优化。利用CloudSim作为云数据中心的云端测试环境。测试结果表明,LEC-VM可以减少云数据中心的SLA违规,保证云计算的服务质量,与其他的虚拟机分配策略比较起来,可以降低能源消耗。     

IaaS云中面向能源效率优化的虚拟机调度算法(英文)

In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.     

TRACTOR: Traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization

Technology providers heavily exploit the usage of edge-cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network-traffic effectiveness. In this study, we present a multi-objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR , which utilizes an artificial bee colony optimization algorithm for power and network-aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three-tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters.     

Workload Prediction Based Virtual Machine Migration and Optimal Switching Strategy for Cloud Power Management

Virtual Machine (VM) Migration has been popular nowadays, as it helps to balance the load effectively. Various VM migration-based approaches are modeled for better VM placement but remain the challenge because of inappropriate load balancing. Thus, workload prediction-based VM migration is introduced to improve the energy efficiency of the system. Importantly, load prediction is very important to enhance resource allocation and utilization. Chaotic Fruitfly Rider Neural Network is devised by combining Rider neural network and chaotic Fruitfly optimization algorithm to predict load. Moreover, the fitness for predicting the load is based on old-time load, resource constraint, and network parameters. Once the load is predicted, the power optimization is performed using VM migration and optimal switching strategy. When the load is found overloaded, the VM migration is performed using the proposed Harris Hawks spider monkey optimization (HHSMO). Thus, the optimal finding of VM for executing the removed task is found out using the proposed HHSMO. The fitness function utilized for the VM migration is based on power, load, and resource parameter. If the load predicted is underloaded, the optimal switch ON/OFF is done optimally by switch ON/OFF the servers using the proposed HHSMO algorithm. Through the migration and switching strategy, the power consumption is optimized. The performance of the proposed model is evaluated in terms of power consumption, load, and resource utilization. The proposed HHSMO achieves the minimal power consumption of 0.0181, the minimal load of 0.002, and the minimal resource utilization of 0.0376.

     

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine （VM） is becoming a major issue for cloud providers. The existing virtual machine placement （VMP） solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization （MLU）. On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.     

基于深度强化学习的无人机辅助移动边缘计算系统能耗优化

近年来,部署搭载有移动边缘计算(MEC)服务器的无人机(UAVs)为地面用户提供计算资源已成为一种新兴的技术。针对无人机辅助多用户移动边缘计算系统,该文构建了以最小化用户平均能耗为目标的模型,联合优化无人机的飞行轨迹和用户计算策略的调度。通过深度强化学习(DRL)求解能耗优化问题,提出基于柔性参与者-评论者(SAC)的优化算法。该算法应用最大熵的思想来探索最优策略并使用高效迭代更新获得最优策略,通过保留所有高回报值的策略,增强算法的探索能力,提高训练过程的收敛速度。仿真结果表明与已有算法相比,所提算法能有效降低用户的平均能耗,并具有很好的稳定性和收敛性。     

Energy‐efficient multiple‐domain bidirectional scheme for G.hn applications

This paper presents an energy‐efficient relaying scheme for G.hn standard. We propose a multi‐domain bidirectional communication network with network coding at the physical layer in order to increase network coverage. The logical link control stack was also modified and supplemented with additional functionality. This reduces the power consumption in the network and enhances the performance while reducing collisions for inter‐domain network access. We consider domain selection to minimize the total energy consumption of the network and present optimal power allocation for the given QoS of each end node. Energy efficiency is evaluated in terms of transmit energy per bit for relay networks with bidirectional symmetric and asymmetric traffic flows. Simulation results show that the proposed multi‐domain bidirectional communication provides improved performance and higher energy savings than the single‐domain unidirectional network, especially in powerline communication channel, which is the worst medium of the three G.hn media. Finally, it was demonstrated that improved energy efficiency can be achieved with appropriate domain selection. Copyright © 2015 John Wiley & Sons, Ltd.     

AODV_RR: A Maximum Transmission Range Based Ad Hoc on-Demand Distance Vector Routing in MANET

Nodes in a mobile ad hoc network are battery constrained devices and energy efficiency becomes an important consideration. In a multi-hop mobile ad hoc network the most common method to achieve energy efficiency is the transmission power control scheme in which a node transmits the data packets to its nearest neighbor which is at minimum required power level. However this scheme minimizes only the transmission power within the node’s neighborhood and energy efficiency at the link level is possible. With this scheme it is not possible to minimize the overall energy consumption of the network and the communication overhead of the network is not minimized. An analysis has been performed and our results have proved that instead of using low transmission power, the routing strategy needs to be controlled and only certain nodes are to be allowed to receive and process this routing request based on the received signal strength, then the overall energy consumption of the network can be minimized and the communication overhead is also minimized. The modified routing strategy is applied to the basic ad hoc on-demand distance vector (AODV) routing protocol and a maximum transmission range based ad hoc on-demand distance vector routing protocol named AODV range routing (AODV_RR) is proposed and studied under different network sizes. Measurable difference in performance is realized and the proposed AODV_RR perform better than normal AODV with respect to all the selected metrics.     

基于时频联合碎片感知的资源均衡虚拟光网络映射算法

为了解决虚拟光网络映射中带宽阻塞率较高以及底层资源消耗不均匀问题,论文提出一种基于时间域-频谱域碎片感知的虚拟网络映射(FA-VNM)算法。该文综合考虑频隙在时间域和频谱域上的碎片问题,设计时频联合碎片公式最小化分配过程中的频谱碎片。进一步,为了均衡网络中的资源消耗,在FA-VNM算法基础上提出基于节点度数的负载均衡感知虚拟网络映射(LB-VNM)算法,设计物理节点平均资源承载能力的公式,优先映射物理节点平均资源承载能力大的节点;为了均衡路径上资源使用,考虑路径权重值,并根据每条路径的权重值对虚拟链路进行映射,从而降低阻塞率。仿真结果表明,所提算法能有效降低阻塞率,提高资源利用率。     

Design of energy‐efficient cloud systems via network and resource virtualization

Data centers play a crucial role in the delivery of cloud services by enabling on‐demand access to the shared resources such as software, platform and infrastructure. Virtual machine (VM) allocation is one of the challenging tasks in data center management since user requirements, typically expressed as service‐level agreements, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors to greenhouse gas emissions of IT services. In this paper, we propose a holistic approach for a large‐scale cloud system where the cloud services are provisioned by several data centers interconnected over the backbone network. Leveraging the possibility to virtualize the backbone topology in order to bypass IP routers, which are major power consumers in the core network, we propose a mixed integer linear programming (MILP) formulation for VM placement that aims at minimizing both power consumption at the virtualized backbone network and resource usage inside data centers. Since the general holistic MILP formulation requires heavy and long‐running computations, we partition the problem into two sub‐problems, namely, intra and inter‐data center VM placement. In addition, for the inter‐data center VM placement, we also propose a heuristic to solve the virtualized backbone topology reconfiguration computation in reasonable time. We thoroughly assessed the performance of our proposed solution, comparing it with another notable MILP proposal in the literature; collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization and fairness for medium size data centers. Copyright © 2013 John Wiley & Sons, Ltd.