基于议价机制的网格资源分配研究

资源管理和分配是网格计算的核心问题,用市场机制可有效地管理和分配网格资源。提出了一个基于议价机制的网格资源分配模型,设计了资源分配策略。模拟实验表明该模型能够较好地适应网格环境的动态变化,并实现各个网格计算资源的负载平衡。     

一种云环境下基于QoS约束的资源分配策略

由于资源非均匀分布以及硬件具有较大异构性,导致资源服务时效率不高。针对上述的不足,根据用户对资源的偏好,建立任务与资源的映射模型,设计一种低负载和低成本的资源分配策略,实现系统负载均衡。实验结果表明,该策略在满足Qo S约束的条件下,有效地提高资源利用率。     

移动智能体应用于网络信息系统

本文介绍了移动智能体的技术特征、一般运行模式及其技术实现手段,用JAVA技术实现了它的一种模型形式。详细介绍了移动智能体技术在网络信息系统纺织行业信息服务平台中的一些应用,该系统依照功能可以分为若干个自主智能实体-Agent,用Agent结构封装信息处理基本单元,这些Agent具有自治性/能动性强,主动合作性好等特点。针对Web信息处理系统的工作特点,运用移动智能体技术使Web服务器组达到动态负载平衡,使应用服务器组实现动态资源共享,构造了系统中几个关键部分的Agent信息流模型,确定了这种信息处理系统基本的工作模式。这种机制使Agent可以自主地根据所处环境及自身的状况,来自觉地调整下一步的运作,使系统有限的资源能够实时得到合理的调配利用。     

高性能集群系统中资源负载量化的研究

负载均衡一直是高性能集群系统中资源分配追求的一个主要目标,能否有效地进行负载识别则直接关系到负载均衡的最终实现。本文针对计算资源和通信资源这两类作业争用的主要资源,分别讨论了其负载的衡量以及负载的量化方法,并通过具体的实验来加以说明。     

Usage-based dynamic pricing of Web services for optimizing resource allocation

Web services technology is becoming an important technological trend in Web application development and integration. Based on open standards, such as SOAP, WSDL, and UDDI, Web services allow Web-based applications to communicate with each other through standardized XML messaging and to form loosely coupled distributed systems. Although the open feature of Web services benefits service providers in servicing consumers, the unlimited computing resources access of Web services to network bandwidth, storage throughput, and CPU time may lead to overexploitation of the resources when applications based on the Web services technology are widely accepted. Therefore, it is critical to optimize the operation of Web services, subject to the QoS requirements of service requests, to assure the total benefits of the service providers and the service consumers. This paper proposes a usage-based dynamic pricing approach to optimizing resource allocation of Web services in the principle of economics, and reports on a pilot implementation demonstrating the technical feasibility of the proposed approach.

Heavy traffic optimal resource allocation algorithms for cloud computing clusters

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with the MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.     

A hybrid approach to adaptive load sharing and its performance

The average response time of tasks in a distributed system depends on the strategy by which workload is shared among the nodes of the system. A common approach to load sharing is to resort to some distributed algorithm that arranges for task transfer between nodes based on information on the system's state. In this paper, we depict a hybrid approach to adaptive load sharing which outperforms existing algorithms, and is especially effective in response to peaks of workload, under both heavy and light system load conditions. The strategy we propose is novel in that it relies on a fully distributed algorithm when the system is heavily loaded, but resorts to a centrally coordinated one when parts of the system become idle. The transition from one algorithm to the other is performed automatically, and the simplicity of the algorithms proposed makes it possible to use a centralized component without incurring in scalability problems and presenting instabilities. Both algorithms are very lightweight and do not need any tuning of parameters. Simulations show that the hybrid approach performs well under all load conditions and task generation patterns, it is weakly sensitive to processing overhead and communication delays, and scales well (to hundred of nodes) despite the use of a centralized component.     

Resource scheduling and load balancing in distributed robotic control systems

This paper describes the latest advances made to a software architecture designed to control multiple miniature robots. As the robots themselves have very limited computational capabilities, a distributed control system is needed to coordinate tasks among a large number of robots. Two of the major challenges facing such a system are the scheduling of access to system resources and the distribution of work across multiple workstations. This paper discusses solutions to these problems in the context of a distributed surveillance task.     

QoS保证的资源竞争与用户需求策略研究

QoS issues are widely being studied for Internet. It is a key issue to how effectively control open resources of network in term of user's QoS need. In this paper, we formulize the resources of network and analyze its status.Further, we analyze competition for the resource of network between applications and user's need, and then point out the object of resources allocation. This paper will be useful reference for QoS study.     

Probabilistic resource allocation in heterogeneous distributed systems with random failures

The problem of finding efficient workload distribution techniques is becoming increasingly important today for heterogeneous distributed systems where the availability of compute nodes may change spontaneously over time. Resource-allocation policies designed for such systems should maximize the performance and, at the same time, be robust against failure and recovery of compute nodes. Such a policy, based on the concepts of the Derman–Lieberman–Ross theorem, is proposed in this work, and is applied to a simulated model of a dedicated system composed of a set of heterogeneous image processing servers. Assuming that each image results in a “reward” if its processing is completed before a certain deadline, the goal for the resource allocation policy is to maximize the expected cumulative reward. An extensive analysis was done to study the performance of the proposed policy and compare it with the performance of some existing policies adapted to this environment. Our experiments conducted for various types of task-machine heterogeneity illustrate the potential of our method for solving resource allocation problems in a broad spectrum of distributed systems that experience high failure rates.     

Reprint of “Robust partial-load experiments with Showstopper”

Concurrent execution of multiple applications leads to varying partial utilization of shared resources. Understanding system behavior in these conditions is essential for making concurrent execution efficient. Unfortunately, anticipating behavior of shared resources at partial utilization in complex systems is difficult, realistic experiments that reproduce and examine such behavior are therefore needed.To facilitate experiments at partial utilization, we present a tool that accurately controls the processor utilization of arbitrary concurrent workloads, either establishing constant partial load or replaying a variable load trace. We validate the ability of the tool to enforce the configured partial utilization on multiple platforms, and use the tool to collect novel information on system behavior at partial utilization levels.In detail, our experiments show how to examine the complex relationship between utilization and throughput, useful for tasks such as performance debugging or system dimensioning, and we show this relationship for the DaCapo benchmarks. Further, we show that CPU pinning (a technique used to improve workload isolation) can benefit from dynamic response to system utilization, improving system efficiency with partial utilization. Finally, we show that the overhead of virtualization also changes with partial utilization and CPU allocation.     

An opportunistic resource allocation approach for mixed QoS and non-QoS connections in OFDMA wireless networks

An opportunistic resource allocation approach is proposed to guarantee both fair resource allocation and high system throughput under combinations of QoS and non-QoS connections in OFDMA networks. This approach features dynamic connection classification and packet prioritization based on real-time network conditions and QoS constraints. A classifier is first employed to prioritize QoS connections by observing the channel state of each subscriber station and the utilization of network resources. It performs a finite-horizon Markov decision process with dynamic rules affected by system load. The transmission order of packets is then determined by an opportunistic multiservice scheduler according to the QoS requirements of connections and the output of the classifier. Having the scheduling result, an allocator assigns slots to the scheduled packets, and its output is linked back to the connection classifier through a resource usage observer for all subscriber stations. The sub-channel allocation problem is also solved by cooperation between the slot allocator and the packet scheduler. Results of numerical analysis and NS2 simulation confirm the advantages claimed above. The same conclusion can also be drawn from the comparison with several existing approaches in terms of system throughput, service successful ratio, average spectral efficiency, and system revenue.     

QoS保障的LTE-A飞蜂窝资源块分配与MCS选择研究

针对LTE-A飞蜂窝网络下行链路的资源块(Resource Block,RB)分配与调制编码策略(Modulation-and-Co-ding Scheme,MCS)选择问题,构建了整数线性规划模型,以在保障每个飞蜂窝用户最小吞吐量的需求下,最大化飞蜂窝系统吞吐量。其中,吞吐量是衡量网络性能最重要的服务质量(Quality of Service,QoS)指标之一。鉴于此问题是一个NP难问题,提出了一种ACOGA智能优化算法。该算法结合遗传算法(Genetic Algorithm,GA)与蚁群优化(Ant Colony Optimization,ACO)算法,可实现RB的动态分配与MCS的动态选择,并收敛到一种近优的分配策略。其中,GA算法动态地优化ACO算法中的参数配置,ACO算法利用优化后的参数配置执行RB分配与MCS选择。仿真表明,与采用静态参数配置的ACO算法相比较,ACOGA算法可使飞蜂窝系统的吞吐量提高12%以上,并显著提高了收敛速率。     

基于粒子群算法的无线信道资源分配算法研究

为了 最大化 多媒体无线信道资源分配的网络效用,提出了一种新的基于粒子群算法的信道时间分配算法。该算法能够优化分配给网络内每个设备的时间,以便为每位网络用户 提供最优化的服务质量(QoS)。所提算法结合了多样性增加函数以及基于个体最优值的学习方法,并基于自适应粒子群算法进行了改进,在持续增强QoS的同时加快了收敛速度。在多达40个设备的千兆网络环境内对所提算法进行了测试。实验结果表明,提出的算法能够大大提升资源分配能力,尤其是在网络规模较大的情况下。     

Non-preemptive scheduling to maximize the minimum global inter-completion time

Temporal load-balancing—“spreading out” the executions of tasks over time—is desirable in many applications. A form of temporal load-balancing is discussed, scheduling to maximize minimum minimum global inter-completion time (MGICT-scheduling). It is shown that MGICT-scheduling is, in general, NP-hard. A number of restricted classes of task systems are identified, which can be efficiently MGICT-scheduled. The technique is applied to a Defense Network System. Simulation results indicate that the proposed strategy achieves higher communication performance in multiprocessor systems. Specifically, our strategy significantly reduces average message delay and percentage of delayed messages.     

A novel resource scheduling algorithm for QoS-aware services on the Internet

The popularity and availability of Internet connection has opened up the opportunity for network-centric collaborative work that was impossible a few years ago. Contending traffic flows in this collaborative scenario share different kinds of resources such as network links, buffers, and router CPU. The goal should hence be overall fairness in the allocation of multiple resources rather than a specific resource. In this paper, firstly, we present a novel QoS-aware resource scheduling algorithm called Weighted Composite Bandwidth and CPU Scheduler (WCBCS), which jointly allocates the fair share of the link bandwidth as well as processing resource to all competing flows. WCBCS also uses a simple and adaptive online prediction scheme for reliably estimating the processing times of the incoming data packets. Secondly, we present some analytical results, extensive NS-2 simulation work, and experimental results from our implementation on Intel IXP2400 network processor. The simulation and implementation results show that our low complexity scheduling algorithm can efficiently maximise the CPU and bandwidth utilisation while maintaining guaranteed Quality of Service (QoS) for each individual flow.     

Traffic Engineering for wireless connectionless access networks supporting QoS-demanding media applications

This paper focuses on the problem of optimal QoS Traffic Engineering (TE) in Co-Channel Interference (CCI)-affected power-limited wireless access networks that support connectionless services. By exploiting the analytical tool offered by nonlinear optimization and following the emerging “Decomposition as Optimization” paradigm [1], the approach pursued in this paper allows to develop a resource allocation algorithm that is distributed, asynchronous, scalable and self-adaptive. Interestingly, the proposed algorithm enables each node of the network to distribute its outgoing traffic among all feasible next-hops in an optimal way, as measured by an assigned global cost function of general form. This optimal traffic distribution complies with several subjective as well as objective QoS requirements advanced by the supported media flows and involves only minimum information exchange between neighboring nodes. Furthermore, it allows for load-balanced multiple forwarding paths and it is able to self-perform optimal traffic re-distribution (i.e., re-routing) in the case of failure of the underlying wireless links. Finally, actual effectiveness of the overall proposed algorithm is numerically tested via performance comparisons against both DSDV-based single-path routing algorithms and interference-aware multipath routing algorithms.