An optimization approach for decentralized QoS‐based scheduling based on utility and pricing in Grid computing

This paper presents an optimization approach for decentralized Quality of Service (QoS)‐based scheduling based on utility and pricing in Grid computing. The paper assumes that the quality dimensions can be easily formulated as utility functions to express quality preferences for each task agent. The utility values are calculated by the user‐supplied utility function that can be formulated with the task parameters. The QoS constraint Grid resource scheduling problem is formulated into a utility optimization problem. The QoS‐based Grid resource scheduling optimization is decomposed into two subproblems by applying the Lagrangian method. In the Grid, a Grid task agent acts as a consumer paying for the Grid resource and the resource providers receive profits from task agents. A pricing‐based QoS scheduling algorithm is used to perform optimally decentralized QoS‐based resource scheduling. The experiments investigate the effect of the QoS metrics on the global utility and compare the performance of the proposed algorithm with other economical Grid resource scheduling algorithms. Copyright © 2006 John Wiley & Sons, Ltd.     

Quality of Service Support for Multimedia Applications in Third Generation Mobile Networks Using Adaptive Scheduling

Third generation mobile network will support services such as video-telephony, video-conferencing and other multimedia applications. Therefore, this network must provide quality of service (QoS) to these applications consistent with that offered by fixed networks. However, this is a very challenging task due to the instability of the wireless channel and the diverse quality of service requirements dictated by different multimedia applications. In this paper we introduce a resource allocation algorithm for the wireless downlink that takes into account the wireless channel characteristics, the QoS required by the applications as well as a pricing value function. Our solution is based on an adaptive scheduling algorithm originally developed for scheduling real-time processes during transient surges. This algorithm tends to maximize the wireless network operator profit while satisfying the customers' quality requests.     

IaaS环境下多租户安全资源分配算法和安全服务调度框架

针对基础设施即服务（IaaS）环境下多租户使用安全服务时由于安全资源有限和安全资源分配不均导致的效率低下问题,提出了一个租户安全资源调度框架。首先以最小最大公平算法为基础,结合Fair Scheduler的调度思想为租户设定了最小共享量和资源需求量属性;然后通过安全服务资源分配算法在保证租户最小共享量满足的前提下,尽可能公平地满足租户的资源需求;最后结合租户内任务调度和租户间资源抢占算法,实现了租户安全服务调度框架。实验结果表明,在随机资源分配条件下,安全服务资源分配算法与传统资源分配算法相比在资源利用率和作业效率上均有明显提高,安全服务调度框架可以有效解决多租户安全资源的分配和强占问题。     

面向5G网络云原生应用资源调度的博弈优化策略

随着5G网络和云原生技术的发展,面向服务的5G云原生核心网应运而生,传统应用正朝着云原生化方向发展。目前云原生服务提供商和云原生应用商数量众多且关系复杂,使得应用在云原生化过程中的资源调度面临新挑战。提出一种5G网络云原生应用资源调度优化策略,将云原生应用商和云原生服务提供商构建为多主多从的Stackelberg博弈模型,对传统收益进行具体描述并联合能耗构建利润函数和策略空间,证明给定一组微服务资源定价的情况下存在云原生应用商的纳什均衡点。在此基础上,引入柯西分布对策略进行优化,提高其收敛性能,通过分布式迭代方法得到云原生服务提供商的最佳微服务定价和云原生应用商的最佳微服务租用比例。仿真结果表明,相比ACA算法、QOS PA算法以及GOS策略,该策略能够有效提高网络收益和用户体验质量,同时降低应用开发能耗。     

A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid

This paper proposes a Stackelberg game approach to maximize the profit of the electricity retailer (utility company) and minimize the payment bills of its customers. The electricity retailer determines the retail price through the proposed smart energy pricing scheme to optimally adjust the real-time pricing with the aim to maximize its profit. The price information is sent to the customers through a smart meter. According to the announced price, the customers can automatically manage the energy use of appliances in the households by the proposed optimal electricity consumption scheduling system with the aim to minimize their electricity bills. We model the interactions between the retailer and its electricity customers as a 1-leader, N-follower Stackelberg game. At the leader’s side, i.e., for the retailer, we adopt genetic algorithms to maximize its profit while at the followers’ side, i.e., for customers, we develop an analytical solution to the linear programming problem to minimize their bills. Simulation results show that the proposed approach is beneficial for both the customers and the retailer.     

基于最小购买模式的多产品定价方法

通过与顾客的在线交互,电子商务网站能够获得消费者反馈的有关参考数据。这些有效的数据结合完善的分析工具可使企业通过价格的优化来增加效益。基于这些有效数据研究了多产品定价问题,其中,给定消费者有关产品的参考数据(包括预算和定购产品列表),通过设定一个企业多个产品的价格,极大化企业的总收益;提出了基于最小购买模式的多产品定价问题模型,设计了针对该模型的遗传算法。仿真实例证明了该模型的有效性。     

基于云环境下的资源评价模型研究

分析了己有的调度机制和常用的任务调度算法,并在此基础上设计了资源评价模型。将资源评价模型加入调度系统中,资源信息由评价模块进行分析和评价,并提供给任务调度器,实现资源和任务的优化匹配,提高了服务质量(QoS)。     

Scheduling and flexible control of bandwidth and in-transit services for end-to-end application workflows

End-to-end scientific application workflows that integrate high-end experiments and instruments with large scale simulations and end-user displays are becoming increasingly important. These workflows require complex couplings and data sharing between distributed components involving large data volumes and present varying hard (in-time data delivery) and soft (in-transit processing) quality of service (QoS) requirements. As a result, supporting efficient data transport is critical for such workflows. In this paper, we leverage software-defined networking (SDN) to address issues of data transport service control and resource provisioning to meet varying QoS requirements from multiple coupled workflows sharing the same service medium. Specifically, we present a flexible control and a disciplined resource scheduling approach for data transport services for science networks. Furthermore, we emulate an SDN testbed on top of the FutureGrid virtualized testbed and use it to evaluate our approach for a realistic scientific workflow. Our results show that SDN-based control and resource scheduling based on simple intuitive models can meet the requirements of the targeted workflows with high resource utilization.     

Joint optimization of overlapping phases in MapReduce

MapReduce is a scalable parallel computing framework for big data processing. It exhibits multiple processing phases, and thus an efficient job scheduling mechanism is crucial for ensuring efficient resource utilization. There are a variety of scheduling challenges within the MapReduce architecture, and this paper studies the challenges that result from the overlapping of the “map” and “shuffle” phases. We propose a new, general model for this scheduling problem, and validate this model using cluster experiments. Further, we prove that scheduling to minimize average response time in this model is strongly NP-hard in the offline case and that no online algorithm can be constant-competitive. However, we provide two online algorithms that match the performance of the offline optimal when given a slightly faster service rate (i.e., in the resource augmentation framework). Finally, we validate the algorithms using a workload trace from a Google cluster and show that the algorithms are near optimal in practical settings.     

FRP: a fast resource placement algorithm in distributed cloud computing platform

We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.     

Unlocking the Advantages of Dynamic Service Selection and Pricing

Bandwidth limitations, resource greedy applications verbose mark-up languages and an increasing number of voice and data users are straining the air interface of wireless networks. Hence, novel approaches and new algorithms to manage wireless bandwidth are needed. In addition, usage based pricing is becoming increasingly prevalent (pre-paid cell phones, calling cards, non-contract minutes, etc.). This paper unlocks the potential to improving the performance of overall system behavior by allowing users to change service level and/or service provider for a (small) price. The ability to dynamically re-negotiate service gives the user the power to control QoS while minimizing usage cost. On the other hand, the ability to change service level pricing dynamically allows the service providers to manage traffic better, improve resource usage and most importantly maximize their profit. This provides a surprising win-win situation for BOTH the service providers AND the users. In this paper we present easy to implement on-line algorithms to minimize the overall usage cost to individual mobile users. This on-line algorithm continuously receives pricing information and evaluates minimum QoS requirements. The algorithm then determines appropriate service level, chooses a service provider and sets a time for re-negotiation dynamically. Our algorithm can handle many practical issues such as capacity limitations, arbitrary price fluctuations and loss/gain of service providers due to mobility. Our results do not assume any specific technologies and can be applied to any environment that can employ dynamic pricing, including wired networks. In fact, dynamic pricing is becoming increasingly desirable since service provider and capacity changes are a growing by-product of mobility. Arriving and departing users at/from a cell tower (or wireless LAN) can effectively reduce or increase the available bandwidth in a cell (or LAN transmission area) and represents a natural opportunity for a pricing change.     

考虑生活垃圾可利用率的闭环供应链差别定价决策

基于可回收垃圾的可利用程度与消费者对其再制品的偏好性不同的实际情况,建立闭环供应链的差别定价模型,从而分析闭环供应链总利润随废品可利用率和消费者偏好的变化规律。结果发现,当再制品和残值单位总价值大于新产品的单位成本价格偏好时,总利润随着消费者偏好系数的增大而增大。但在分散决策情形下,会造成各企业节点之间效率的流失。据此,提出以集中决策下的均衡结果作为标准,建立了一种协调契约收益共享机制。最后通过算例分析,得到收益共享比例的具体取值范围,实现了供应链中各节点之间的有效协调。     

A hybrid heuristic workflow scheduling algorithm for cloud computing environments

Cloud computing is a relatively new concept in the distributed systems and is widely accepted as a new solution for high performance and distributed computing. Its dynamisms in providing virtual resources for organisations and laboratories and its pay-per-use policy make it very popular. A workflow models a process consisting of a series of steps that shape an application. Workflow scheduling is the method for assigning each workflow task to a processing resource in a way that specific workflow rules are satisfied. Some scheduling algorithms for workflows may assume some quality of service parameter such as cost and deadline. Some efforts have been done on workflow scheduling on cloud computing environments with different service level agreements. But most of them suffer from low speed. Here, we introduce a new hybrid heuristic algorithm based on particle swarm optimisation (PSO) and gravitation search algorithms. The proposed algorithm, in addition to processing cost and transfer cost, takes deadline limitations into account. The proposed workflow scheduling approach can be used by both end-users and utility providers. The CloudSim toolkit is used as a cloud environment simulator and the Amazon EC2 pricing is the reference pricing used. Our experimental result shows about 70% cost reduction, in comparison to non-heuristic implementations, 30% cost reduction in comparison to PSO, 30% cost reduction in comparison to gravitational search algorithm and 50% cost reduction in comparison to hybrid genetic-gravitational algorithm.     

Utilization-based pricing for power management and profit optimization in data centers

Traditional load balancing approaches may spread the load on more computers as long as the performance in terms of response time or cost is minimized. Nowadays power is a growing cost factor for data centers. In this paper, from the service provider’s point of view, the load balancing decision is made based on whether power consumption can be reduced or more profit can be earned. To achieve this, we design pricing algorithms to influence the load distribution. Both algorithms take into account the utilization of computers besides other factors, such as prices and power costs. In the first algorithm, we design pricing functions with respect to the computer utilization to encourage or discourage resource usage. In the second algorithm, we focus on the profit that a service provider can earn after deducting power cost from its revenue. We formulate this profit optimization problem and derive the optimum price solution.     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

一种远程设备系统动态调度模型

远程设备系统作为科学仪器设备共享的重要支撑平台,是网格应用领域的主要研究分支之一.其中的调度模型研究又是新兴而备受关注的研究课题.远程设备调度不同于传统的计算机资源调度;它是指在设备系统中,从当前可用的资源集合中分配出满足任务需求的最优或者次优的设备资源给用户.而目前的远程设备调度模型主要采用分时调度的策略,未能体现出远程设备资源服务质量的特性.提出了一种新的基于服务质量的远程设备动态调度模型,能有效满足设备供求双方的需求.模型经仿真实验验证,可以合理地为设备使用者分配到性能和价格双项指标综合表现最优的设备,同时利用反馈机制动态调节设备的性能评价参数和使用价格,进而达到均衡负载、提高设备利用率的目的.原理和仿真结果的双重论证证实了调度模型很好地改进了远程设备系统的整体服务质量,平衡了设备提供者和使用者的双方利益.     

IEEE802.16e的实时上行链路调度服务优化算法

深入分析了IEEE802.16e建议的三种实时调度服务算法：UGS、rtPS、ertPS，并在此基础上提出了一种优化的实时调度服务算法：irtPS。该优化的调度服务算法在保证变化数据率实时上行链路的延时性能基础上，最大限度地提高了实时上行链路的资源利用率。数学建模分析结果显示，在保证延时性能的前提下，该优化的调度服务算法的系统容量较IEEE802.16e建议算法的系统容量有显著增加。     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

A novel pricing approach to support QoS in 3G networks

Pricing in 3G and other communication networks may control and manage the utilisation of network resources. The available network resources get strained with increased usage levels, which results in poor service to the users. Most users prefer receiving high quality services at affordable costs. This requires the provision of QoS guarantees for network services at a low cost. In a real business scenario, this relationship is hard to achieve; moreover revenue sources for network operators have been shifting from the provision of network access to provisioning of rich services, e.g. multimedia services. To attain a functional compromise, we propose a pricing scheme that relies on service profiles to manage resource utilisation in a DiffServ-enabled 3G network. The service profiles define the QoS achieved for accessing services through a common resource pool, in which resource sharing is used to maximise network resource utilisation, user satisfaction and profits for the network operators. In an NGN scenario users would select pricing profiles according to their budgets, and the network will map these profiles to a set of QoS options that may translate to the choice of an access network for service access. In this paper, we present the mathematical model of the proposed pricing scheme, the proposed design of an evaluation framework, QoS performance results, and a service provisioning scenario illustrating the applicability of the proposed pricing scheme.     

考虑公平关切和制造商竞争的平台型供应链定价、低碳水平和服务水平决策

随着网络购物的普及和消费者低碳偏好的增强,同时考虑到公平关切对制造商竞争最优决策的影响,构建一种由普通制造商、低碳制造商及电商平台组成的平台型供应链决策模型,分析普通制造商在不具有公平关切、具有有利不公平厌恶和不利不公平厌恶情形下的均衡决策,并在不利不公平厌恶下提出服务成本共担契约来改进供应链.研究发现:1)低碳制造商...