Joint QoS optimization for layered computational grid

Many existing grid resource allocation and scheduling algorithms mainly focus on isolated layers of the grid architecture. The inflexibility of the strict layering structure results in an inefficient utilization of the grid resources. This paper takes a system view of the computational grid and aims to jointly optimize global QoS by adopting cross-layer design. Cross-layer design is based on information exchange and joint optimization among multiple grid layers. Parameters from different layers are provided to a cross-layer optimizer, which selects the values of the layer specific parameters maximizing joint global QoS. The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer. In simulation part, we compare the performance of the global joint QoS optimization approach with application layer local optimization and resource layer local optimization approach, respectively.     

Optimization decomposition approach for layered QoS scheduling in grid computing

The paper presents optimization decomposition based layered Quality of Service (QoS) scheduling for computational grid. Cross layer joint QoS scheduling is studied by considering the global problem as decomposed into three sub-problems: resource allocation at the fabric layer, service composing at the collective layer, and user satisfaction degree at the application layer. The paper proposes a complete solution from optimization modeling, Lagrange relaxation based decomposition, to solutions for each sub-problem Lagrange relaxation based decomposition. These aspects match the vertical organization of the considered grid system: each layer trade with adjacent layers to find a global optimum of the whole grid system. Through multi-layer QoS joint optimization approach, grid global QoS optimization can be achieved. The cross layer policy produces an optimal set of grid resources, service compositions, and user’s payments at the fabric layer, collective layer and application layer, respectively, to maximize global grid QoS. The owner of each layer obtains inputs from other layers, tries to maximize its own utility and provides outputs back to other layers. This iterative process lasts until presumably all layers arrive at the same solution.     

A global optimization approach for three layers of computational grid stack

This paper presents a global optimization approach for three layers of grid stack. In grid environment, the Quality of Service (QoS) needs to be supported at every layer of the grid architecture. However, less attention has been paid to incorporating QoS at multiple layers of the grid architecture. The primary objective of most existing scheduling mechanisms is to improve QoS at single grid layer, while the QoS optimization at multiple grid layers is seldom considered. In the paper, we consider the requirements of fabric layer, collective layer and application layer at the same time, and propose a global optimization approach for three layers of grid stack. The paper deals with global optimization as optimization decomposition. The global optimization for three layers of grid stack can be decomposed into three sub-problems at different grid layers: resource allocation at the fabric layer, service integration at the collective layer, and application QoS optimization problem at the application layer. A distributed iterative algorithm for three-layer optimization is proposed. The convergence of the iterative algorithm is proved. The simulations are conducted to test Three-Layer Optimization Algorithm.     

Multi-level scheduling for global optimization in grid computing

The paper presents a multi-level scheduling algorithm for global optimization in grid computing. This algorithm provides a global optimization through a cross-layer optimization realized by decomposing the optimization problem in different sub-problems each of them corresponding to one among the grid layers such as application layer, collective layer and fabric layer. The QoS of an abstraction level is a utility function that assigns at every level a different value and that depends on the kind of task that is executed on the grid. The global QoS is given by processing of the utility function values of the three different levels, using the Lagrangian method. Multi-level QoS scheduling algorithm is evaluated in terms of system efficiency and their economic efficiency, respectively. Economic efficiency includes user utility, service provider’s revenue and grid global utility. System efficiency includes execution success ratio and resource allocation ratio.     

Multi-layer optimization in service-oriented sensor grid

Sensor devices such as video cameras, infrared sensors and microphones are being widely exploited in grid application. The paper deals with multi-layer optimization in service oriented sensor grid to optimize utility function of sensor grid, subject to resource constraints at resource layer, service composition constraints at service layer and user preferences constraints at application layer respectively. The multi-layer optimization problem can be decomposed into three subproblems: sensor grid resource allocation problem, service composing problem, and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of sensor grid resources and service demand. The proposed algorithm decomposes global sensor grid optimization problem into a sequence of three sub-problems at three layers via an iterative algorithm. The simulations are conducted to validate the efficiency of the multi-layer optimization algorithm. The experiments compare the performance of the multi-layer global optimization approach with application layer local optimization and resource layer local optimization approach respectively.     

Three-layer control policy for grid resource management

The paper proposes a three-layer control policy for grid resource management, which applies a bottom-up approach to dynamic resource management. In order to hierarchically allocate resources in the system to maximize system utility, different controllers are deployed at three levels: local controller, group controller, and global controller. Global control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity. While local control adapts a single application to small changes at fine granularity. Global control exploits the interlayer coupling of fabric layer, collective layer and application layer to perform less frequent and effective control actions for a system-wide optimization. The interaction between grid layers is controlled through the use of the pricing variable, which coordinates the user demand at the application layer and supply of resources at the fabric layer. The paper proposes pricing-based three-layer control algorithms. This paper demonstrates the benefits of the control algorithm through simulations.     

An efficient coordination scheme between layers of grid architecture

Grid architecture is protocol architecture, with protocols defining the basic mechanisms by which virtual organization (VO) users and resources negotiate, establish, manage, and exploit sharing relationships. A standard based open architecture facilitates extensibility, interoperability, portability, and code sharing; standard protocols make it easy to define standard services that provide enhanced capabilities. Grid architecture organizes into layers. In the paper, a three-layer coordination of grid architecture is proposed. The aim is to optimize the end-to-end quality of the dynamic grid application as well as efficiently utilizing the grid resources. A joint coordination algorithm, which exploits the interlayer coupling of fabric layer, collective layer and application layer, is proposed. The algorithm aims to achieve a system-wide optimization based on the user's preferences. We formulate the integrated coordination of three layers into a constrained optimization problem. Utility functions are used to express grid user' preferences, resource provider's benefit function and system's objectives. The optimization framework provides a layered approach to the sum utility maximization problem. The advantage of the proposed joint three-layer coordination is demonstrated through simulations.     

Multi-Layer Resource Management in Cloud Computing

The paper studies multi-layer optimization in service oriented cloud computing to optimize the utility function of cloud computing, subject to resource constraints of an IaaS provider at the resource layer, service provisioning constraints of a SaaS provider at the service layer, and user QoS (quality of service) constraints of cloud users at application layer, respectively. The multi-layer optimization problem can be decomposed into three subproblems: cloud computing resource allocation problem, SaaS service provisioning problem, and user QoS maximization problem. The proposed algorithm decomposes the global optimization problem of cloud computing into three sub-problems via an iterative algorithm. The experiments are conducted to test the efficiency of the proposed algorithm with varying environmental parameters. The experiments also compare the performance of the proposed approach with other related work.     

Adaptive power allocation with quality-of-service guarantee in cognitive radio networks

This paper proposes a novel power control policy for a cognitive radio network as an effort to maximize throughput under the average interference power constraint. The underlined policy ensures delay-related quality of service (QoS) requirements with reduced interference to the primary user. In this work we also take into account the peak and average transmit power constraints for the secondary user. An optimization problem associated with the power control policy is formulated based on a cross-layer framework, where the queue on data link layer is serviced by the power control policy at the physical layer. A recursive algorithm under the power constraints is developed to solve for the optimal solution. It is shown that the reduction of average interference to the primary user is related closely to the QoS requirements. The analysis derives the average interference power limits to the primary user in fading channels with guaranteed QoS requirements for the secondary user. The numerical results show the effectiveness of the proposed power control policy.     

分级跨层设计的宽带无线接入网QoS架构

针对当前跨层设计导致信号流向混乱和处理时延长等问题,提出一种新的分级跨层设计的宽带无线接入网QoS架构。该架构具有链路独立层、链路独立-链路依赖的业务接口与链路依赖层,并给出各分级内及接口的QoS管理结构及模块功能。其中,物理层和无线链路层构成的链路依赖层可实现链路对信道的自适应,IP层及其上的各层构成的链路独立层在链路独立-链路依赖接口,通过业务QoS参数与无线资源的映射实现端到端的自适应,信号流向简单且处理时延减小。仿真结果表明,该架构的资源利用率明显优于分层结构。     

无线传感器网络中基于信息质量的跨层QoS支持

本文中提出了一种基于数据的信息质量而提供QoS支持的跨层协议,文中先阐述了无线传感器网络与传统网络在QoS支持上的不同的需求和特点,并提出了跨层优化的概念,指出无线传感器网络采用跨层优化的可行性和必要性.本文通过划分优先级来对网络提供不同的服务质量.数据包的优先级主要反应了数据包中所含的信息质量,由网络的应用层决定,而后再根据数据优先级的不同在MAC层提供不同的服务,从而实现跨层的QoS支持.     

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

This paper is to solve efficient QoS based resource scheduling in computational grid. It defines a set of QoS dimensions with utility function for each dimensions, uses a market model for distributed optimization to maximize the global utility. The user specifies its requirement by a utility function. A utility function can be specified for each QoS dimension. In the grid, grid task agent acted as consumer pay for the grid resource and resource providers get profits from task agents. The task agent' utility can then be defined as a weighted sum of single-dimensional QoS utility function. QoS based grid resource scheduling optimization is decomposed to two subproblems: joint optimization of resource user and resource provider in grid market. An iterative multiple QoS scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The grid users propose payment for the resource providers, while the resource providers set a price for each resource. The experiments show that optimal QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.     

Tradeoffs between energy consumption and QoS in mobile grid

Mobile grid, which combines grid and mobile computing, supports mobile users and resources in a seamless and transparent way. However, mobility, QoS support, energy management, and service provisioning pose challenges to mobile grid. The paper presents a tradeoff policy between energy consumption and QoS in the mobile grid environment. Utility function is used to specify each QoS dimension; we formulate the problem of energy and QoS tradeoff by utility optimization. The work is different from the classical energy aware scheduling, which usually takes the consumed energy as the constraints; our utility model regards consumed energy as one of the components of measure of the utility values, which indicates the tradeoff of application satisfaction and consumed energy. It is a more accurate utility model for abstracting the energy characteristics and QoS requirement for mobile users and resources in mobile grid. The paper also proposes a distributed energy–QoS tradeoff algorithm. The performance evaluation of our energy–QoS tradeoff algorithm is evaluated and compared with other energy and deadline constrained scheduling algorithm.     

Classification-Based System For Cross-Layer Optimized Wireless Video Transmission

Joint optimization strategies across various layers of the protocol stack have recently been proposed for improving the performance of real-time video transmission over wireless networks. In this paper, we propose a new, low complexity system for determining the optimal cross-layer strategies for wireless multimedia transmission based on classification and machine learning techniques. We first determine offline the optimal cross-layer strategy for various video sequences and channel conditions (training data). Subsequently, we extract relevant and easy to compute content features, encoder-specific parameters, and channel resources from the training data, and train a statistical classifier based on these optimal results. At run-time, we predict using the classifier the optimal cross-layer compression and transmission strategy using these simple, on-the-fly computed features. Hence, we consider the complex problem of finding the optimal cross-layer strategy during the training phase only, and rely at transmission-time on low-complexity classification techniques. We illustrate the proposed classification-based system by performing MAC-application layer optimizations for video transmission over 802.11a wireless LANs. Specifically, we predict the optimal MAC retry limits for the various video packets and compare our results against both optimal and conventionally used ad-hoc cross-layer solutions. Our results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to optimized ad-hoc solutions. The improvements are especially important at high packet-loss rates (5% and higher), where deploying a judicious mixture of strategies at the various layers becomes essential. Furthermore, our proposed classification-based system can be easily modified to include other layers from the OSI stack during the cross-layer optimization.     

Tradeoff between utility and lifetime in energy-constrained wireless sensor networks

We study the tradeoff between network utility and network lifetime using a cross-layer optimization approach. The tradeoff model in this paper is based on the framework of layering as optimization decomposition. Our tradeoff model is the first one that incorporates time slots allocation into this framework. By using Lagrangian dual decomposition method, we decompose the tradeoff model into two subproblems: routing problem at network layer and resource allocation problem at medium access control (MAC) layer. The interfaces between the layers are precisely the dual variables. A partially distributed algorithm is proposed to solve the nonlinear, convex, and separable tradeoff model. Numerical simulation results are presented to support our algorithm.     

A Low-Complexity Cross-Layer Optimization Algorithm for Video Communication Over Wireless Networks

Recent years have witnessed a rapid increment in video applications over wireless networks including on-demand video streaming and video phoning. This growth has also brought the need to find a good compromise in the conflict between resource limitations affecting mobile devices and the desire for high-quality multimedia services. It is possible to face this problem adopting a cross-layer strategy that jointly tunes the parameters of each layer in the network protocol stack. In this optimization strategy, complexity is one of the most significant issues because of the limited computational resources and power supply. The paper presents a low-complexity cross-layer algorithm that is able to jointly tune the parameters of different protocol layers by adopting simple but effective models. The quality of the reconstructed video sequence, the produced bit rate, and the service class associated to each packet are seen as functions of the percentage of null DCT coefficients. This modeling permits to find a closed-form solution to the joint optimization problem that can be computed with a limited number of operations and grants, at the same time, a good visual quality in the reconstructed sequence.     

电力线通信系统中跨层的用户调度和资源分配

针对多用户多业务基于正交频分多址的电力线通信系统,提出一种在数据链路控制层进行用户调度和在物理层进行资源分配的多层多目标最优的跨层资源分配算法,其用户调度根据所有用户的服务质量(QoS)满意程度、QoS要求、业务包模型、信道状态信息和队列状态信息,从所有用户中选出要服务的用户和确定这些用户的最优跨层参数;其资源分配则根据所有调度用户的QoS要求、最优跨层参数和信道状态信息,先把功率按地窖注水原理分给每个子载波,再把每个子载波最优地分给调度用户并采用逐比特加载查表算法调整其上分配的功率和比特。最后在典型的电力线信道环境下对算法进行仿真,结果表明新算法在系统资源大范围变化时也能保障用户的服务质量,同时有效地提高系统资源的利用。     

一种AODV跨层优化方法及仿真测试分析

针对Ad Hoc网络中传统AODV协议不支持QoS的缺陷,提出了一种多约束QoS AODV跨层优化方法MQ-AODV（Multi-Constrained QoS AODV）。该方法采用跨层设计的思想,除以传统AODV协议的转发跳数作为路由选择判据之外,而且加入应用层的QoS需求信息和MAC层的多种QoS指标作为路由选择判据,并通过改进AODV路由表结构和路由查找算法,提高AODV路由协议性能。仿真测试结果表明MQ-AODV协议缩短了传输时延,增强了路由协议的适应性和鲁棒性。     

An adaptive cross-layer design for multiservice scheduling in OFDMA based mobile WiMAX systems

We consider a cross-layer scheme for the downlink multiuser Orthogonal Frequency Division Multiple Access (OFDMA) for mobile WiMAX (Worldwide Interoperability for Microwave Access) based on IEEE802.16e standard. We propose an intelligent Medium Access Control (MAC) that adapts with the physical layer (PHY) in response to the different QoS requirements of the diverse service flows in the system. This adaptation is represented by a joint packet scheduling and slot allocation scheme interaction in both MAC and PHY layers. Based on this interaction, a fair and efficient QoS guaranteed resource allocation for a mixture of real-time and non real-time service flows is achieved. We provide extensive simulation results showing the performance aspects of the proposed cross-layer scheme.     

基于多媒体业务的无线网络跨层优化与仿真

由于无线信道的时变衰落特性以及不同用户对服务质量(QoS)的动态要求,传统的无线网络协议栈分层设计思想难以满足未来移动通信系统的需求.提出一种基于多用户公平性准则的跨层优化设计方法以解决该问题.基于多用户无线传输环境下的多媒体业务特性,联合物理层,数据链路层和应用层进行参数提取和优化设计,从应用层角度出发,提出了该跨层设计准则.在保证用户间的公平性同时最大化各用户观察到的视频流质量,充分有效地利用了无线资源.仿真结果表明跨层优化准则能同时很好地改善用户间的公平性和系统整体性能.