设计基于SaaS的弹性云平台和优化调度方法

运用虚拟化的技术而设计出的云平台可以更好地在实践中提升人们的工作效率,然而在资源管理方面以及服务调度方面也带来一定的困难.因此,在充分分析和研究SaaS(软件服务)的基础上,结合基本的服务调度而重点提出了优化SaaS资源调度的方式,从而使得云平台能够更好地发挥出其积极作用,为人们的生活和工作提供良好的服务.因此,本文主要结合弹性云平台中的服务情况而分析具体的调度任务和服务弹性的方法,进而可以更好地提升云平台的性能以及资源的利用率.     

基于Docker的云资源弹性调度策略

针对云资源弹性调度问题,结合Ceph数据存储的特点,提出一种基于Docker容器的云资源弹性调度策略。首先,指出Docker容器数据卷不能跨主机的特性给应用在线迁移带来了困难,并对Ceph集群的数据存储方法进行改进;然后,建立了一个基于节点综合负载的资源调度优化模型;最后,将Ceph集群和Docker容器的特点相结合,利用Docker Swarm实现了既考虑数据存储、又考虑集群负载的应用容器部署算法和应用在线迁移算法。实验结果表明,与一些调度策略相比,该调度策略对集群资源进行了更细粒度的划分,实现了云平台资源的弹性调度,并在保证应用性能的同时,达到了合理利用云平台资源和降低数据中心运营成本的目的。     

一种SOA云服务平台架构研究与应用

在完美SOA分布式系统架构平台基础上,面向服务进行云架构模式的分析,建立以云端用户、云服务平台中心和云服务提供者构成的云架构模式,实现SOA云平台体系架构,将云服务提供者分成云服务接口层、云服务调度管理层、云计算服务层和云物理服务层。经过改进提高了云平台的服务能力和执行效率,并给出了提高该云平台调度效率的调度算法。通过数字校园云进行了云平台实例演示,显示了SOA云服务平台的架构和调度算法的快速服务响应能力。     

一种SWJS云平台研究与应用

根据SOA架构理念,以JSB、WCF、SDO等技术为基础,进行了面向服务的云平台的架构,建成由云端用户请求层、云服务代理中心（JSB层）、云服务资源层、数据服务层、数据与系统资源层组成的SWJS云平台,JSB、WCF、SDO等技术在服务封装、数据服务与调度、跨平台通信等方面显示了协同性和快速性,经过教师工作绩效评定系统平台的实例演示,SWJS云平台具有较强的稳定性、快速响应性和服务能力。     

基于NGPD的PaaS平台研究与实现*

云计算是一种将计算和存储任务分配到由大量计算机构成的云中的计算模式。云计算提供三种类型的服务：基础设施即服务（IaaS）、平台即服务(PaaS)和软件即服务(SaaS)。虽然PaaS在云系统中起到了关键的作用,但是目前的PaaS平台存在着服务协同性低、资源匮乏、用户界面不友好等问题。本文设计并实现了基于准则调控和策略驱动的自治式服务协同模型（NGPD）的PaaS平台,其利用自治计算元素实现了自治式服务协同,这是尝试解决现有PaaS平台支持提供云平台型服务不足的探索性研究,为云用户提供了更加高效和经济的平台开发环境。     

面向云网融合SaaS安全的虚拟网络功能映射方法

在云网融合背景下,承载软件即服务（SaaS）业务功能的云基础设施可能横跨多个数据中心和归属网络,难以保证云资源安全可控。为缩短SaaS业务服务的处理时延,设计基于冗余执行和交叉检验的SaaS组合服务模式,并对容器、Hypervisor和云基础设施的安全威胁进行建模,建立拟态化虚拟网络功能映射模型和安全性优化机制。在此基础上,提出基于近端策略优化的PJM算法。实验结果表明,与CCMF、JEGA和QVNE算法相比,PJM算法在满足安全性约束的条件下,能够降低约12.2%业务端到端时延。     

基于云计算的工商办公自动化系统的设计与实现

办公自动化系统是为适应现代无纸化及网络化办公的趋势,更好地服务于现代办公操作,基于B/S模式而开发的一套应用于工商系统的办公自动化系统.系统在设计与实现上基于云服务模式,将系统构架分为基础设施层(IaaS)、系统平台层(PaaS)、应用服务层(SaaS).基于云服务平台的设计,能够提升系统的可靠性和数据存储安全性,实现硬件资源共享和动态调整,实现应用弹性部署,为各机关提供个性化定制服务,降低运维成本、实现节能减排,并提升技术的先进性,并奠定业务的可扩展基础.     

一种基于SaaS的公共服务平台实现方案

首先简要地分析SaaS（Software as a Service）模式的特点,在此基础上,提出基于SaaS模式、包含运营管理和服务管理等模块的公共服务平台。然后论述平台的功能和使用流程,实现平台的概要设计,并在综合考虑元数据配置、多租户数据模型、安全模型等多方面因素后,给出一种初步实现SaaS模式公共服务平台的方法。     

云计算环境中 SaaS 的接入控制和调度策略研究

SaaS 是一种基于网络的软件应用模式,是服务提供商将应用软件统一部署在自己的服务器上,用户根据自己的实际需要,通过互联网向服务提供商订购并支付自己所需的服务.在未来,SaaS 模式是占主导地位的云服务模型.文中阐述 SaaS 的基本概念,介绍了 SaaS 的参考结构以及服务流程,分析概括了不同类型的服务要求的接入控制策略,总结了不同性能要求作业的调度策略,最后结合已有的云计算环境下的 SaaS 接入控制和调度策略研究成果,展望了未来的研究方向和亟待解决的关键问题     

云计算环境中SaaS的接入控制和调度策略研究

SaaS是一种基于网络的软件应用模式,是服务提供商将应用软件统一部署在自己的服务器上,用户根据自己的实际需要,通过互联网向服务提供商订购并支付自己所需的服务。在未来,SaaS模式是占主导地位的云服务模型。文中阐述SaaS的基本概念,介绍了SaaS的参考结构以及服务流程,分析概括了不同类型的服务要求的接人控制策略,总结了不同性能要求作业的调度策略,最后结合已有的云计算环境下的SaaS接入控制和调度策略研究成果,展望了未来的研究方向和亟待解决的关键问题。     

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.     

QoS约束的云经济资源管理模型研究

提供用户满意的、具有QoS约束的云计算应用是云计算面临的一大难题。提出了以商品市场为原型的云计算经济资源管理模型,其通过云用户与供应商的SLA协商,实现应用服务层QoS到资源设备层QoS的映射,最后利用效用函数的管理策略实现资源的优化调度。     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

Optimization-based resource allocation for software as a service application in cloud computing

Software as a service (SaaS) is a software that is developed and hosted by the SaaS vendor. SaaS cloud provides software as services to the users through the internet. To provide good quality of service for the user, the SaaS relies on the resources leased from infrastructure as a service cloud providers. As the SaaS services rapidly expand their application scopes, it is important to optimize resource allocation in SaaS cloud. The paper presents optimization-based resource allocation approach for software as a service application in cloud. The paper uses optimization decomposition approach to solve cloud resource allocation for satisfying the cloud user’s needs and the profits of the cloud providers. The paper also proposes a SaaS cloud resource allocation algorithm. The experiments are designed to compare the performance of the proposed algorithm with other two related algorithms.     

The QoS-based MCDM system for SaaS ERP applications with Social Network

Cloud computing delivers almost all of its services including software, user’s data, system resources, processes and their computation over the Internet. Cloud computing consists of three main classes; Software as a Service, Infrastructure as a Service and Platform as a Service. Using Software as a Service (SaaS), users are able to rent application software and databases which they then install onto their computer in the traditional way. In the Enterprise Resource Planning (ERP) system, the system service environment changed so as to allow the application of the SaaS in the cloud computing environment. This change was implemented in order to provide the ERP system service to users in a cheaper, more convenient and efficient form through the Internet as opposed to having to set up their own computer. Recently many SaaS ERP packages are available on the Internet. For this reason, it is very difficult for users to find the SaaS ERP package that would best suit their requirements. The QoS (Quality of Service) model can provide a solution to this problem. However, according to recent research, not only quality attributes’ identification for SaaS ERP, but also a process for finding and recommending software in the cloud computing environment, has proved to be lacking. In this paper, we propose a QoS model for SaaS ERP. The proposed QoS model consists of 6 criteria; Functionality, Reliability, Usability, Efficiency, Maintainability and Business. Using this QoS model, we propose a Multi Criteria Decision Making (MCDM) system that finds the best fit for the SaaS ERP in the cloud computing environment and makes recommendations to users in priority order. In order to organize the quality clusters, we organized an expert group and got their opinion to organize the quality clusters using Social Network Group. Social Networks can be used efficiently to get opinion by various types of expert groups. In order to establish the priority, we used pairwise comparisons to calculate the priority weights of each quality attribute while accounting for their interrelation. Finally, using the quality network model and priority weights, this study evaluated three types of SaaS ERPs. Our results show how to find the most suitable SaaS ERPs according to their correlation with the criteria and to recommend a SaaS ERP package which best suits users’ needs.     

A semantic interoperability framework for software as a service systems in cloud computing environments

In cloud computing environments in software as a service (SaaS) level, interoperability refers to the ability of SaaS systems on one cloud provider to communicate with SaaS systems on another cloud provider. One of the most important barriers to the adoption of SaaS systems in cloud computing environments is interoperability. A common tactic for enabling interoperability is the use of an interoperability framework or model. During the past few years, in cloud SaaS level, various interoperability frameworks and models have been developed to provide interoperability between systems. The syntactic interoperability of SaaS systems have already been intensively researched. However, not enough consideration has been given to semantic interoperability issues. Achieving semantic interoperability is a challenge within the world of SaaS in cloud computing environments. Therefore, a semantic interoperability framework for SaaS systems in cloud computing environments is needed. We develop a semantic interoperability framework for cloud SaaS systems. The capabilities and value of service oriented architecture for semantic interoperability within cloud SaaS systems have been studied and demonstrated. This paper is accomplished through a number of steps (research methodology). It begins with a study on related works in the literature. Then, problem statement and research objectives are explained. In the next step, semantic interoperability requirements for SaaS systems in cloud computing environments that are needed to support are analyzed. The details of the proposed semantic interoperability framework for SaaS systems in cloud computing environments are presented. It includes the design of the proposed semantic interoperability framework. Finally, the evaluation methods of the semantic interoperability framework are elaborated. In order to evaluate the effectiveness of the proposed semantic interoperability framework for SaaS systems in cloud computing environments, extensive experimentation and statistical analysis have been performed. The experiments and statistical analysis specify that the proposed semantic interoperability framework for cloud SaaS systems is able to establish semantic interoperability between cloud SaaS systems in a more efficient way. It is concluded that using the proposed framework, there is a significant improvement in the effectiveness of semantic interoperability of SaaS systems in cloud computing environments.     

云计算资源调度研究综述

资源调度是云计算的一个主要研究方向.首先对云计算资源调度的相关研究现状进行深入调查和分析;然后重点讨论以降低云计算数据中心能耗为目标的资源调度方法、以提高系统资源利用率为目标的资源管理方法、基于经济学的云资源管理模型,给出最小能耗的云计算资源调度模型和最小服务器数量的云计算资源调度模型,并深入分析和比较现有的云资源调度方法;最后指出云计算资源管理的未来重要研究方向:基于预测的资源调度、能耗与性能折衷的调度、面向不同应用负载的资源管理策略与机制、面向计算能力(CPU、内存)和网络带宽的综合资源分配、多目标优化的资源调度,以便为云计算研究提供有益的参考.     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.