Modeling and performance evaluation of QoS-aware job scheduling of computational grids

To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated QoS-aware job dispatching policy is proposed, which correlates priorities of incoming jobs used for job selecting at the local scheduler of the grid node with the job dispatching policies at the global scheduler for computational grids. The stochastic high-level Petri net （SHLPN） model of a two-level hierarchy computational grid architecture is presented, and a model refinement is made to reduce the complexity of the model solution. A performance analysis technique based on the SHLPN is proposed to investigate the QoS-aware job scheduling policy. Numerical results show that the QoS-aware job dispatching policy outperforms the QoS-unaware job dispatching policy in balancing the high-priority jobs, and thus enables priority-based QoS.

共享制造环境下基于Z-RIM的QoS多方异质评价方法

为解决共享制造环境下决策信息异质、候选服务数量动态变化的制造服务QoS评价问题,基于平台、服务需求方、服务提供方的三方决策信息,提出一种符合共享制造特点的QoS多方异质制造服务评价方法。通过精确数、语言变量和Z-number处理异质信息;基于共识最大化计算决策方权重向量,利用TrFWAA算子集结多方信息,依据离差最大化模型计算属性权重;在此基础上,采用RIM对各服务进行评估。结合共享制造环境下的制造业服务优选案例对提出的方法进行验证分析,结果表明该方法具有较好的实用性和优越性。

基于SDN的卫星网络端到端QoS类映射研究

针对在多个卫星网络域环境下域间协作困难,以及如何在各个卫星网络域内选择合适的QoS类实现端到端QoS类映射的问题,基于软件定义卫星网络架构,首先,提出一种SDN卫星网络域间通信方案,以及面向多QoS类的端到端服务交付数据包处理方法。其次,提出一种基于多准则决策的控制模块。模块利用ELECTRE方法,在ELECTRE方法中通过综合权重法计算出各个卫星网络域内QoS指标相应的权重系数,并且利用ELECTRE方法对每个QoS类的净支配指数进行排序,从而为端到端路径的每个卫星网络域选择合适的QoS类。最后,与现有端到端QoS类映射方案进行比较,仿真结果表明,上述方法在多卫星网络域环境下保证端到端的时延、时延抖动、丢包率和代价。

Dynamic QoS Mapping and Adaptive Semi-Persistent Scheduling in 5G-TSN Integrated Networks

The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication service.To guarantee the endto-end(E2E)QoS(Quality of Service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this paper.It is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its flexibility.An adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in 5GS.It includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share algorithm.Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.

A QoS-Aware Packet Scheduling Mechanism in Cognitive Radio Networks for Smart Grid Applications

Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffi c. Therefore, a novel Qo S-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a Qo S-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.

基于遗传-蚁群优化算法的QoS组播路由算法设计

为了提高网络路由性能,提出并设计了一种基于遗传-蚁群优化算法的服务质量(quality of service,QoS)组播路由算法。首先,设计了自适应变频采集策略用于采集网络与节点信息,以此获得网络和节点的状态,为后续路由优化提供数据支持;其次,计算路径代价,将路径代价最小作为优化目标,建立QoS组播路由优化模型,并设置相关约束条件;最后,结合遗传算法和蚁群算法提出一种遗传-蚁群优化算法求解上述模型,输出最优路径,完成路由优化。实验结果表明,所提算法可有效降低路径长度与路径代价,提高搜索效率与路由请求成功率,优化后的路由时延抖动较小。

QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics

Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications.However,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment.Besides this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and makespan.This situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider requirements.Moreover,the task scheduling in the Cloud is a prevalent NP-Hard problem.Motivated by these concerns,this paper introduces and implements a QoS-aware Energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of workload.The proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics viz.cuckoo search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO policy.Further,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload traces.The comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristics scheduling policies.

基于QoS约束的通信组网链路故障恢复探究

随着网络技术的不断发展,通信网的规模逐渐扩大,网络结构日渐趋于复杂化,发生故障的概率自然就会增高。当通信网出现故障后,必须尽快恢复,否则可能会造成巨大的经济损失,严重时甚至引发各类社会安全风险。智能通信网是解决上述问题的有效策略之一,对网络带宽、时延、丢包率提出了不同的要求。本文对如何恢复网络故障展开了研究,提出了一种基于QoS(Quality of Service)约束的通信组网链路故障恢复方法,根据用户对业务提出的QoS需求以及空闲网络资源,选择恢复路径,确保传输业务的可靠性。在当通信网发生故障后,该方法能够有针对性地快速解决故障,对通信技术的应用与发展具有实用价值。

Self-configuring scheduling scheme for IPv6 traffic with multiple QoS classes

This paper proposes a new queuing model and adaptive scheduling scheme which realizes multi-class QoS mechanism under DiffServ architecture. The queuing model is composed of two parallel output subqueues, each output sub-queue adopts random drop algorithm by setting different buffer threshold for different class traffic, so it can provide multi-class QoS. The new proposed scheduling scheme which adaptively changes the parameter A can guarantee the performance target of high class traffic, in the mean time, improve the QoS of low classes traffic.

云联盟三层架构:博弈型QoS建模

面对国内外大型公有云供应商的激烈竞争,中小云厂商的生存难度加大。为此,建立一个基于相互合作的云联盟成为了这些厂商的一种可行策略。然而,在追求个体最大利益和保障联盟整体服务质量(quality of service,QoS)之间存在着复杂的博弈关系。针对上述问题,一种基于QoS的云联盟模型被提出,其涵盖云计算的三层架构。在应用层至虚拟层,引入了一种基于差分进化(differential evolution,DE)算法的创新任务分配策略,专门用于处理多QoS任务分配问题。在虚拟层至物理层,设计了合作与竞争并存的虚拟机迁移模型,适用于在云联盟博弈计算环境下实现虚拟机迁移的能耗与QoS之间的平衡。实验结果表明,所提出的解决方案改进了云计算环境的服务质量,并揭示了在云联盟环境中,合作和竞争两种模式的相对优势。

云服务推荐中基于多源特征和多任务学习的时序QoS预测

随着云计算技术的普及,云服务数量指数级增长,用户不再满足于功能性需求,服务质量(Quality of Service,QoS)成为比较服务优劣的关键性能指标.如何在动态、复杂的云环境中实时、准确地预测服务质量并为用户推荐高质量服务成为热点问题.考虑到云服务器的负载、网络状态、用户接入云环境的偏好等随着时间变化,本文提出了基于多源特征和多任务学习的时序QoS预测方法(T-MST),它可以实时、准确地同时预测多种QoS属性.首先,TMST对用户、服务进行特征表示,通过Time2Vec刻画时序特征,再结合多种QoS属性的历史记录生成多源特征表示.其次,基于滑动窗口采用LSTM感知窗口内的时序关系,借助注意力机制细化窗口内不同时刻的关键性,从而构造待预测时刻的隐藏状态.最后,T-MST采用多任务预测层实现多种QoS属性的同时预测,它们共享上游模型,仅在预测层采用不同的感知模块以提升模型的鲁棒性和计算效率.本文基于真实世界的数据集进行了全面的实验验证,结果表明T-MST在吞吐量和响应时间的时序预测任务上平均绝对误差(Mean Absolute Error,MAE)分别平均提升了37.53%和20.38%,优于现有的时序QoS预测方法;而且TMST的计算效率更高,能够有效应对实时QoS预测的需求.

Proportional rate scheduling based on QoS in mobile WiMAX networks

A new scheduling scheme based on users＇ quality of service（QoS） in mobile WiMAX networks is presented.The proposed scheme tracks each user＇s average rate and adjusts the corresponding scheduling weight adaptively to result in：（a） each user＇s average rate is proportional to the corresponding QoS level;（b） the constraints of the minimal and/or maximal rates required by QoS can be satisfied;（c） the utility function of system is maximal under the constraints（a） and（b）.Theoretical analysis based on utility function and simulation results indicates the system throughput can be improved dramatically in the proposed scheme.

A NEW SCHEDULING ALGORITHM TO PROVIDE PROPORTIONAL QOS IN OPTICAL BURST SWITCHING NETWORKS

A new scheduling algorithm, which aims to provide proportional and controllable QoS (Quality of Service) in terms of burst loss probability for OBS (Optical Burst Switching) networks, is proposed on the ba- sis of a survey of QoS schemes in current OBS networks. With simulations, performance analysis and com- parisons are carried out in detail. The results show that, in the proposed scheme, burst loss probabilities are proportional to the given factors and the control of QoS performance can be achieved with better performance. This scheme will be beneficial to the OBS network management and the tariff policy making.

Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

As huge users are involved,there is a difficulty in spectrum allocation and scheduling in Cognitive Radio Networks(CRNs).Collision increases when there is no allocation of spectrum and these results in huge drop rate and network performance degradation.To solve these problems and allocate appropriate spectrum,a novel method is introduced termed as Quality of Service(QoS)Improvement Proper Scheduling(QIPS).The major contribution of the work is to design a new cross layer QoS Aware Scheduling based on Loss-based Proportional Fairness with Multihop(QoSAS-LBPFM).In Medium Access Control(MAC)multi-channel network environment mobile nodes practice concurrent broadcast between several channels.Acquiring the advantage of introduced cross layer design,the real-time channel conditions offered by Cognitive Radio(CR)function allows adaptive sub channel choice for every broadcast.To optimize the resources of network,the LBPFM adaptively plans the radio resources for allocating to diverse services without lessening the quality of service.Results obtained from simulation proved that QoSAS-LBPFM provides enhanced QoS guaranteed performance against other existing QIPS algorithm.

Cross-layer Resource Allocation on Broadband Power Line Based on Novel QoS-priority Scheduling Function in MAC Layer

Traditional resource allocation algorithms use the hierarchical system, which does not apply to the bad channel environment in broadband power line communication system. Introducing the idea of cross-layer can improve the utilization of resources and ensure the QoS of services. This paper proposes a cross-layer resource allocation on broadband power line based on QoS priority scheduling function on MAC layer. Firstly, the algorithm considers both of real-time users’ requirements for delay and non-real-time users’ requirements for queue length. And then user priority function is proposed. Then each user’s scheduled packets number is calculated according to its priority function. The scheduling sequences are based on the utility function. In physical layer, according to the scheduled packets, the algorithm allocates physical resources for packets. The simulation results show that the proposed algorithm give consideration to both latency and throughput of the system with improving users’ QoS.

基于SDN的空间信息网络自适应QoS路由算法的设计与实现

为了满足用户对网络服务质量的要求,本文提出一种基于SDN的空间信息网络自适应QoS路由算法。该算法采用SDN控制器对网络进行全局控制和管理,通过收集网络拓扑信息和质量参数,动态调整路由策略,实现网络的自适应QoS路由。实验结果表明,该算法能够有效提高网络服务质量,优化网络性能。

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

A QoS-aware scheduling algorithm for combined-input-crosspoint-queued switch

In this paper, we study the quality of service requirement and guaranteed-rate scheduling problem for combined-input-crosspoint-queued (CICQ) switches. Using crosspoint buffers between input queues and output ports, the input and output scheduling can be decoupled in CICQ switches. Here we propose a new guaranteed-rate scheduling algorithm which is divided seperately into input and output scheduling. Simulation results show that our algorithm can perform better than existing scheduling schemes in both packet loss and jitter control.

星地异构网络联合QoS保障关键技术

针对星地异构网络的拓扑结构高动态、星地链路高差异、网络资源强受限等特征给星地融合业务高效承载带来重大挑战的问题,深入分析星地异构网络发展现状,提出星地异构网络联合QoS保障系统架构。并在此基础上,针对跨域资源统一编排管理、星地联合QoS指标参考体系构建、联合QoS动态策略调控、星地资源按需适配与部署等关键技术进行探讨。

TRS Scheduling for Improved QoS Performance in Cloud System

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.