An energy‐saving algorithm for cloud resource management using a Kalman filter

Cloud computing has emerged as a promising technique to provide storage and computing component on‐demand services over a network. In this paper, we present an energy‐saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

VRAS: A Lightweight Local Resource Allocation System for Virtual Machine Monitor

Traditional computing resource allocations in virtualization environment devote to provide fairness of resource distribution when the overall workload of host is heavy. That makes those allocations lack of efficiency under light workloads. To target this, we design and implement a lightweight resource allocation system, virtual resource allocation system (VRAS). Considering the fact that workloads can be balanced by migrating virtual machines to other hosts, we propose a request driven mechanism to focus on resource allocation under light workloads. We also present some allocation strategies used in VRAS to explain how it works on processor and memory resources. Our experiment results demonstrate that VRAS can result in throughput improvements of 28 % for RUBiS application, and the network overhead reduction of 81 %, comparing with the traditional allocation methods.     

基于流量演化感知的服务功能链在线弹性编排策略

随着网络功能虚拟化（NFV）的引入，运营商能够提供更为灵活的网络服务.然而现有服务功能链（SFC）编排局限于静态反应式策略，业务流量发生变化时网络资源供应量难以匹配负载需求，虚拟网络功能（VNF）频繁部署与迁移，运营开销增大.针对上述问题，该文提出一种基于流量演化感知的服务功能链在线弹性编排策略（OEOP），该策略将在线学习引入到SFC流量演化感知的过程，预先确定细粒度的VNF弹性需求.此外，以实时更新的SFC路径与节点负载两因子为导向，完成新增VNF的在线弹性部署，代替VNF迁移应对系统负载变化.仿真表明，该策略明显增强了虚拟资源供应量与负载需求的匹配特性，VNF吞吐量利用率提高10.2%~24.8%，运营开销平均降低26.7%.     

Object Request Brokers in Mobile Computing

The widespread use of wireless and mobile networks and devices requiresspecial programming techniques and solutions. The object request brokers ofmobile environments have to adopt these techniques and offer servicesdealing with the problems of mobility. Most of the existing object request brokers however were developed for fixed networks assuming reliable transport protocols (mostly TCP), while the mobile networks cannot offer high qualitytransport.In this paper we give an overview of the challenges and solutions in mobilecomputing and present our ORB(M) framework implementing services based onthe solutions. Extending the framework doesn't require the assistance of itsdevelopers, the user can implement application-specific semantic elementsand deploy various new methods bound to the process of remote invocation.The user can form arbitrary new invocation semantics based on these elementsand customise the invocation semantics used by a given method. Our newmobility-related semantic elements offer solutions to the challengesof mobility allowing the user to concentrate on theessential problems of the application and handling.     

Impact of Interference on Multi-Hop Wireless Network Performance

In this paper, we address the following question: given a specific placement of wireless nodes in physical space and a specific traffic workload, what is the maximum throughput that can be supported by the resulting network? Unlike previous work that has focused on computing asymptotic performance bounds under assumptions of homogeneity or randomness in the network topology and/or workload, we work with any given network and workload specified as inputs.A key issue impacting performance is wireless interference between neighboring nodes. We model such interference using a conflict graph, and present methods for computing upper and lower bounds on the optimal throughput for the given network and workload. To compute these bounds, we assume that packet transmissions at the individual nodes can be finely controlled and carefully scheduled by an omniscient and omnipotent central entity, which is unrealistic. Nevertheless, using ns-2 simulations, we show that the routes derived from our analysis often yield noticeably better throughput than the default shortest path routes even in the presence of uncoordinated packet transmissions and MAC contention. This suggests that there is opportunity for achieving throughput gains by employing an interference-aware routing protocol.     

Joint Scheduling and Resource Allocation with Fairness Based on the Signal-to-Leakage-plus-Noise Ratio in the Downlink of CoMP Systems

Recent research has shown that coordinated multi point (CoMP) transmission can provide significant gains in terms of the overall cell capacity and cell-edge user throughput [1]. The main purpose of this paper is to enhance the overall cell throughput, the cell-edge user’s throughput, and the fairness among user equipment terminals (UEs) in LTE-Advanced (LTE-A) systems using CoMP. Towards that end, we propose two novel resource allocation (RA) strategies based on the Signal-to-Leakage-plus-Noise-Ratio (SLNR) for the downlink of CoMP transmission in LTE-A systems. The proposed RA strategies select the UEs that can efficiently share the same resource block (RB) without degrading the overall throughput by using the SLNR metric. Moreover, a fairness algorithm is proposed to achieve certain level of fairness among the UEs and to improve the cell-edge UEs throughput. In addition, we compare the proposed strategies to the RA based on the more common Signal-to-Interference-plus-Noise-Ratio (SINR) strategy. The SLNR-based RA is shown to provide significant gains in throughput reaching up to 80 % in the overall system and is shown to have even less complexity than the typical SINR-based RA. Moreover, by evaluating the proposed strategies in terms of the average cell throughput, cell-edge user throughput, and fairness among UEs, simulations show that the proposed strategies present superior performance compared to the more common SINR strategy. With such advantages as enhanced throughput and lower complexity, the proposed schemes are suitable for application in practical cellular systems.     

802．11无线局域网中AP放置与信道配置的联合算法

在多小区无线局域网规划中,接入访问点(AP)放置与信道配置是两个重要的任务。在给定用户业务需求的条件下,谋求最大系统吞吐率和资源分配的公平性是网络规划的目标,其中公平性由吞吐率均衡因子表征。在传统的规划策略中,AP放置与信道配置被分割为两个先后的步骤;而该文提出的目标函数则将这两个问题联合解决以得到更好的系统性能。当采用全局遍历时,对目标函数寻求全局最优解计算量庞大;因此该文又提出了一种有效的局部最优解搜索算法补丁算法,此算法计算量小,同时能较好地逼近全局最优解。     

H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略

针对异构云无线接入网络(H-CRAN)网络下基于网络切片的在线无线资源动态优化问题,该文通过综合考虑业务接入控制、拥塞控制、资源分配和复用,建立一个以最大化网络平均和吞吐量为目标,受限于基站(BS)发射功率、系统稳定性、不同切片的服务质量(QoS)需求和资源分配等约束的随机优化模型,并进而提出了一种联合拥塞控制和资源分配的网络切片动态资源调度算法。该算法会在每个资源调度时隙内动态地为性能需求各异的网络切片中的用户分配资源。仿真结果表明,该文算法能在满足各切片用户QoS需求和维持网络稳定的基础上,提升网络整体吞吐量,并且还可通过调整控制参量的取值实现时延和吞吐量间的动态平衡。     

一种支持硬件加速的虚拟网络功能部署模型

为解决以软件实现的虚拟网络功能(VNF)性能受限问题,软件定义网络和网络功能虚拟化(SDN/NFV)等新型网络架构引入了硬件加速资源。硬件加速资源的部署,使得VNF能够为日益增长的数据流量提供服务保障。该文针对已有研究未考虑具有高性能数据处理需求的服务链VNF部署问题,提出一种支持硬件加速的VNF部署模型。该模型基于硬件加速资源的承载特性,在保证未加速VNF到商用服务器的优化部署下,优先实现交换机中加速资源的复用,并根据网络业务的性能需求,灵活调整加速资源与VNF的映射约束。仿真实验表明,与其他典型部署方法相比,在引入相同硬件加速资源的情况下,该模型可以承载更多的业务流量,满足服务链高性能数据处理需求,有效提高了部署在网络中加速硬件的资源利用率。     

A dynamic fog service provisioning approach for IoT applications

Internet of Things (IoT) is an ecosystem that can improve the life quality of humans through smart services, thereby facilitating everyday tasks. Connecting to cloud and utilizing its services are now public and common, and the experts seek to find some ways to complete cloud computing to use it in IoT, which in next decades will make everything online. Fog computing, where the cloud computing expands to the edge of the network, is one way to achieve the objectives of delay reduction, immediate processing, and network congestion. Since IoT devices produce variations of workloads over time, IoT application services will experience traffic trace fluctuations. So knowing about the distribution of future workloads required to handle IoT workload while meeting the QoS constraint. As a result, in the context of fog computing, the main objective of resource management is dynamic resource provisioning such that it avoids the excess or dearth of provisioning. In the present work, we first propose a distributed computing framework for autonomic resource management in the context of fog computing. Then, we provide a customized version of a provisioning system for IoT services based on control MAPE‐k loop. The system makes use of a reinforcement learning technique as decision maker in planning phase and support vector regression technique in analysis phase. At the end, we conduct a family of simulation‐based experiments to assess the performance of our introduced system. The average delay, cost, and delay violation are decreased by 1.95%, 11%, and 5.1%, respectively, compared with existing solutions.     

ARMS — Application of AI and OR Methods to Resource Management

This paper documents work on automating resource management in BT Retail. BT Retail manages BT's access network and provides telecommunications services to its customers. BT Retail's field engineers are allocated jobs via an information system known as Work Manager. In order to proactively position the engineers (i.e. resources) so as to service jobs in an optimal manner resource managers are involved in analysing the profiles of engineers in the light of incoming jobs and ‘selecting’ those profiles that will yield best quality of service (QoS) and reduce operational costs. A profile is a set of attributes that define a resource's capabilities (i.e. skills), capacity (i.e. availability), and location (i.e. area). Resource planning involves identifying an ‘optimal’ set of resource profiles. Accurate workload forecasting is sine qua non for optimal resource planning. To this end we have developed ARMS, Automated Resource Management System, a suite of components for workload forecasting and resource planning. This revised version was published online in July 2006 with corrections to the Cover Date.     

Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing

Mobile Edge Computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices. In this paper, we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization. Specifically, we first focus on a static multiuser computation offloading scenario and jointly optimize users' offloading decisions, transmission durations, and Edge Servers' (ESs) resource allocations. Owing to the nonconvexity of our joint optimization problem, we identify its layered structure and decompose it into two problems: a subproblem and a top problem. For the subproblem, we propose a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs’ resource allocations under a given transmission duration. Second, we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem. Further, after solving the static scenario, we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload. To efficiently address this dynamic scenario, we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner. Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios. We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.     

A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

Application Deployment on Catallactic Grid Middleware

An architecture based on a decentralized market view integrates grid applications with catallactic middleware. A prototype application showed the concept's feasibility, as well as the middleware's effectiveness in balancing query-request workload across multiple grid services. Grid computing researchers have shown significant interest in using an economic paradigm for exchanging grid resources and services. With this approach, applications can use a market mechanism to schedule services access, thus giving them a fair, efficient way to share resources in high-demand periods. Most existing approaches rely on centralized brokers to coordinate resource access, and they're typically implemented over existing grid middleware. We propose an alternative approach based on F.A. Hayek's Catallaxy mechanism. Catallaxy's free-market, self-organizing coordination mechanisms adjust prices within the market based on the particular demands for a specific (scarce) resource     

Design of spread spectrum multicode CDMA transport architecture formultimedia services

We investigate a new application of the well-known spread spectrum code division multiple access (SS-CDMA) techniques to multimedia services related to the development of the next-generation wireless mobile networks interconnecting with a wireline ATM-based broadband network. Such services allow users to share novel multimedia applications without any geographical restrictions. However, since the mobile radio channel has a fixed limited bandwidth, the traditional SS-CDMA system may not be sufficient to accommodate the variable bit rate (VBR) multimedia services requested by multiple mobile users simultaneously. Moreover, the traffic load at the base station can change dynamically due to the time-varying throughput requirement of these requested multimedia services. To tackle this difficulty, a multicode CDMA (MC-CDMA) technique is proposed to provide multirate multimedia services by varying the number of spreading codes assigned to each user in order to meet its throughput requirement. In MC-CDMA, a spreading code can be used to transmit information at a basic bit rate. Users (video or data) who need higher transmission rates can use multiple codes in parallel. Meanwhile, the maximum available number of codes in the MC-CDMA system is still limited. Hence, a cost-effective dynamic code allocation scheme has then been proposed to dynamically assign an appropriate number of codes to each user for achieving the maximum resource utilization for multiuser multimedia services via the mobile radio channel. Finally, a number of real multimedia titles generated from the well-known MacroMind Director are conducted to evaluate performance     

面向众核密码处理器的高效负载均衡技术

工作负载分配不均是制约众核密码平台资源利用率提高的重要因素,动态负载分配可提高平台资源利用率,但具有一定开销;所以更高的负载均衡频率并不一定带来更高的负载均衡增益。因此,该文建立了关于负载均衡增益率与负载均衡频率的数学模型。基于模型,提出一种面向众核密码平台的无冲突负载均衡策略和一种基于硬件作业队列的“可扩展-可移植”负载均衡引擎——“簇间微网络-簇内环阵列”。实验证明:在性能、延时功耗积、资源利用率和负载均衡度方面,该文设计的负载均衡引擎与基于“作业窃取”的软件技术相比平均优化约4.06倍、7.17倍、23.01%和2.15倍;与基于“作业窃取”的硬件技术相比约优化1.75倍、2.45倍、10.2%、和1.41倍;与理想硬件技术相比,密码算法吞吐率平均只降低了约5.67%(最低3%)。实验结果表明该文技术具有良好的可扩展性和可移植性。     

基于实时业务挤占的OFDMA系统的无线资源分配方案

为了在实时业务和非实时业务共享无线资源的场景中增加系统吞吐量,该文提出了一种基于实时业务挤占的无线资源分配方法,该方案首先实施统一调度,然后进行实时业务挤占过程。在保证对实时业务服务的情况下,实时业务挤占的无线资源分配方案提高了多用户分集效果,增加了系统的吞吐量。理论和仿真分析表明,与已有的传统的实时业务和非实时业务共享无线资源的调度方案相比,该方案能够提供更高的系统吞吐量和频谱效率。在实际应用中,该方案具有一定的可行性和可操作性。     

Dynamic resource allocation for DVB‐RCS networks

To provide high‐speed multimedia services using the digital video broadcasting‐return channel satellite (DVB‐RCS) standard, it is imperative to efficiently assign timeslots according to user demands and dynamically take into account the variations of the propagation conditions. In satellite networks operating above 10 GHz, rain fading constitutes the dominant factor deteriorating the quality of service. In this paper, a novel two‐phase resource allocation scheme for a DVB‐RCS compliant satellite network is proposed. The scheme operates in two phases, the resource calculation and the resource assignment. In the resource calculation phase, based on a dynamic channel model, an efficient algorithm is presented to determine the superframe design that maximizes system throughput. In the resource assignment phase, a novel Hybrid Bin‐packing algorithm is introduced maximizing the utilization of the multi‐frequency time division multiple access frame. The proposed algorithm has been compared with existing schemes exhibiting significantly better results with regard to overall system throughput. Copyright © 2008 John Wiley & Sons, Ltd.     

Secure quality of service handling: SQoSH

Proposals for programmable network infrastructures, such as active networks and open signaling, provide programmers with access to network resources and data structures. The motivation for providing these interfaces is accelerated introduction of new services, but exposure of the interfaces introduces many new security risks. We describe some of the security issues raised by active networks. We then describe our secure active network environment (SANE) architecture. SANE was designed as a security infrastructure for active networks, and was implemented in the SwitchWare architecture. SANE restricts the actions that loaded modules can perform by restricting the resources that can be named; this is further extended to remote invocation by means of cryptographic credentials. SANE can be extended to support restricted control of quality of service in a programmable network element. The Piglet lightweight device kernel provides a “virtual clock” type of scheduling discipline for network traffic, and exports several tuning knobs with which the clock can be adjusted. The ALIEN active loader provides safe access to these knobs to modules that operate on the network element. Thus, the proposed SQoSH architecture is able to provide safe, secure access to network resources, while allowing these resources to be managed by end users needing customized networking services. A desirable consequence of SQoSH's integration of access control and resource control is that a large class of denial-of-service attacks, unaddressed solely with access control and cryptographic protocols, can now be prevented