SaaS平台访问控制研究

Saas平台软件交付模式将应用软件以服务的形式提供给客户,可缩减硬件采购、系统管理上的开销。由于租户数据统一存储于服务提供商处,如何在维持较高资源利用率的同时保障租户的数据安全是一个挑战性问题。针对租户角色复杂、各租户数据共存而又独立访问的要求,结合基于角色的访问控制模型,构建了支持多租户、多角色、方便租户权限管理的SaaS平台的访问控制模型。和传统基于角色的访问控制模型相比,该模型增加了租户的概念,以租户为基本单元实施平台的访问控制,提高了SaaS平台访问控制的安全性和可管理性。分析了用户访问SaaS平台的具体流程,给出了模型的形式语言描述,实现了SaaS餐饮管理平台访问控制的数据库的物理模型,为SaaS平台开发提供参考。     

SDSN@RT: A middleware environment for single-instance multitenant cloud applications

With the single-instance multitenancy (SIMT) model for composite Software-as-a-Service (SaaS) applications, a single composite application instance can host multiple tenants, yielding the benefits of better service and resource utilization and reduced operational cost for the SaaS provider. An SIMT application needs to share services and their aggregation (the application) among its tenants while supporting variations in the functional and performance requirements of the tenants. The SaaS provider requires a middleware environment that can deploy, enact, and manage a designed SIMT application, to achieve the varied requirements of the different tenants in a controlled manner. This paper presents the SDSN@RT (software-defined service networks at runtime) middleware environment that can meet the aforementioned requirements. SDSN@RT represents an SIMT composite cloud application as a multitenant service network, where the same service network simultaneously hosts a set of virtual service networks, one for each tenant. A service network connects a set of services and coordinates the interactions between them. A virtual service network realizes the requirements for a specific tenant and can be deployed, configured, and logically isolated in the service network at runtime. SDSN@RT also supports the monitoring and runtime changes of the deployed multitenant service networks. We show the feasibility of SDSN@RT with a prototype implementation and demonstrate its capabilities to host SIMT applications and support their changes with a case study. The performance study of the prototype implementation shows that the runtime capabilities of our middleware incur little overhead.     

Scalable SaaS Indexing Algorithms with Automated Redundancy and Recovery Management

Software-as-a-Service (SaaS) is a new software delivery model with Multi-Tenancy Architecture (MTA). An SaaS system is often mission critical as it often supports a large number of tenants, and each tenant supports a large number of users. This paper proposes a scalable index management algorithm based on B+ tree but with automated redundancy and recovery management as the tree maintains two copies of data. The redundancy and recovery management is done at the SaaS level as data are duplicated with tenant information rather than at the PaaS level where data are duplicated in chunks. Using this approach, an SaaS system can scale out or in based on the dynamic workload. This paper also uses tenant similarity measures to cluster tenants in a multi-level scalability architecture where similar tenants can be grouped together for effcient processing. The scalability mechanism also includes an automated migration strategies to enhance the SaaS performance. The proposed scheme with automated recovery and scalability has been simulated, the results show that the proposed algorithm can scale well with increasing workloads.     

Adaptable,model-driven security engineering for SaaS cloud-based applications

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple “tenants” of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants—i.e. multi-tenancy—increases tenants’ concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants’ needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants’ security requirements. We use abstract models to capture service provider and multiple tenants’ security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.     

Dynamic provisioning in multi-tenant service clouds

Cloud-based systems promise an on-demand service provisioning system along with a ??pay-as-you-use?? policy. In the case of multi-tenant systems this would mean dynamic creation of a tenant by integrating existing cloud-based services on the fly. Presently, dynamic creation of a tenant is handled by building the required components from scratch. Although multi-tenant systems help providers save cost by allocating multiple tenants to the same instance of an application, they incur huge reconfiguration costs. Cost and time spent on these reconfiguration activities can be reduced by re-constructing tenants from existing tenant configurations supported by service providers. Multi-tenant cloud-based systems also lack the facility of allowing clients to specify their requirements. Giving clients the flexibility to specify requirements helps them avoid spending an excessive amount of time and effort looking through a list of services, many of which might not be relevant to them. Moreover, dynamic provisioning in the cloud requires an integrated solution across the technology stack (software, platform and infrastructure) combining functional, non-functional and resource allocation requirements. Existing research works in the area of web service matching, although numerous, still fall short, since they usually consider each requirement type in isolation and cannot provide an integrated solution. To that end, in this paper we investigate the features needed for dynamic service provisioning on the cloud. We propose a novel User Interface-Tenant Selector-Customizer (UTC) model and approach, which enables cloud-based services to be systematically modeled and provisioned as variants of existing service tenants in the cloud. Our approach considers functional, non-functional and resource allocation requirements, which are explicitly specified by the client via the user interface component of the model. To the best of our knowledge, ours is the first such integrated approach. We illustrate our ideas using a realistic running example, and also present a proof-of-concept prototype built using IBM??s Rational Software Architect modeling tool. We also present experimental results demonstrating the applicability of our matching algorithm. Our results show significant reduction in matching time with the help of an elimination process that reduces the search space needed for performing matching.     

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.     

面向SaaS应用基于键值对模式的多租户索引研究

面向SaaS应用的多租户数据库为满足租户的数据隔离和按需定制的需求,需要提供支持隔离和易于定制的数据存储机制及索引机制.基于键值对存储方式,提出元数据驱动的映射表索引模型,该模型根据租户定制需求,为租户业务数据形成各自的索引元数据,通过元数据驱动实现了索引数据的隔离及定制效果;给出索引的维护策略,根据租户数据访问请求进行索引切片,以逐渐细化的索引切片作为数据访问的基本单位,快速返回租户结果集.实验结果表明,该方案在数据访问分布均衡的情况下,使索引维护及数据访问具有较好的总体性能.     

基于可变性模型的可复用与可定制SaaS软件开发方法

云计算环境下,软件通过互联网向租户提供服务,这种基于互联网的软件交付模式称为SaaS（软件即服务）.与传统软件交付模式相比,SaaS软件通常运行于软件供应商的服务器端,同时为多个租户提供服务.由于需要支持不同租户的个性化需求,SaaS软件应具备足够的灵活性,以应对快速变化的租户需求;而且针对某一个租户的变更,不应影响其他租户.通过扩展课题组前期开发的基于可变性管理的适应性服务组装方法及其支持平台,提出了一种云计算环境下可复用、可定制的SaaS软件开发方法,开发了相应的支持平台,包括支持SaaS模式的服务组装引擎和远程定制工具.该方法针对不同租户的共性需求,提供一个抽象服务组装模型,支持平台在运行阶段解释执行抽象服务组装模型,根据租户的个性化需求派生不同的流程实例,这些运行时流程实例多态共存、互不影响.采用一个特定领域的SaaS软件实例来验证该方法的可行性,评估了支持平台的性能.实验结果表明,该方法及其支持平台可以支持多实例多租户的交付模式.     

Efficient customization of multi-tenant Software-as-a-Service applications with service lines

Application-level multi-tenancy is an architectural approach for Software-as-a-Service (SaaS) applications which enables high operational cost efficiency by sharing one application instance among multiple customer organizations (the so-called tenants). However, the focus on increased resource sharing typically results in a one-size-fits-all approach. In principle, the shared application instance satisfies only the requirements common to all tenants, without supporting potentially different and varying requirements of these tenants. As a consequence, multi-tenant SaaS applications are inherently limited in terms of flexibility and variability.This paper presents an integrated service engineering method, called service line engineering, that supports co-existing tenant-specific configurations and that facilitates the development and management of customizable, multi-tenant SaaS applications, without compromising scalability. Specifically, the method spans the design, implementation, configuration, composition, operations and maintenance of a SaaS application that bundles all variations that are based on a common core.We validate this work by illustrating the benefits of our method in the development of a real-world SaaS offering for document processing. We explicitly show that the effort to configure and compose an application variant for each individual tenant is significantly reduced, though at the expense of a higher initial development effort.     

一种带租户演化容忍度的SaaS服务演化一致性判定方法

随着云技术的不断发展与成熟,软件即服务(SaaS)模式成为未来软件应用发展的主要趋势。在多元开放的网络生态环境中,SaaS服务若要有效应对用户需求及外部变化,就须具备演化能力。演化一致性 是指服务在演化后能保有原基础及与其他服务正常交互的能力。目前对演化一致性的判定多偏向于定性分析,且往往忽略了租户的感受,没有既定的显式标准对一致性进行定量度量并判定。针对此问题,从SaaS多租户单实例的应用模式出发,分层次细粒度地建立服务实例描述模型,引入一致性度量值来表示定量计算的结果,充分考虑租户的演化要求,提出一种带租户演化容忍度的判定方法,细粒度地判定演化一致性。最后,结合SaaS应用案例,采用所提方法对演化一致性进行分析判定,实际应用的反馈情况验证了该方法的可行性和有效性。     

一种SaaS交付平台的多租户数据迁移策略

现有的云数据放置策略未引入SaaS特征,租户作为独立个体的特征被忽略,导致多租户数据的混合放置,常用的数据迁移策略面临着识别及迁移租户数据的挑战。提出一种面向SaaS应用的云中多租户数据动态同步迁移策略,解决了共享存储模式下无法识别SaaS应用租户,难以使用快照、日志等数据库技术进行租户数据迁移的问题。通过SaaS平台数据层面的同步迁移,保证云中各数据节点的负载均衡及良好的用户体验。     

基于虚拟机部署策略的云平台容错即服务方法

针对如何充分利用云基础架构层资源,满足上层云应用系统租户对应用系统容错的需求多样性和高可靠性要求的问题,提出一种面向租户和云服务提供商的、基于虚拟机部署策略的云平台容错即服务方法。该方法根据租户的特定容错需求适配适合的容错方法及容错级别,据此计算并最优化云服务提供商的收益和资源使用量,在此基础上对提供容错服务的虚拟机进行优化部署,充分利用底层虚拟机资源为租户的云应用系统提供更为可靠的容错服务。实验结果表明,所提方法能够在保障云服务提供商收益的基础上,为多租户云应用系统实现更灵活且可靠性更高的容错服务。     

基于线性隐藏的租户副本混淆模型

在软件即服务模式下,针对租户定制的副本数据在明文存储情况下容易被服务提供商合谋欺诈问题,提出基于线性隐藏的数据混淆模型,通过混淆使得存储相同数据的租户副本具有不同的数据内容表现,防止服务提供商为节省存储空间,整个删除租户不常用数据副本,欺骗租户。使用蒙特卡罗随机单调函数对混淆模型进行拓展,制定关键字查询保序策略,可以在混淆后的租户副本数据上进行查询操作。实验结果表明,扩展的混淆模型在保序关键字上的查询性能接近明文查询。     

A search-based identification of variable microservices for enterprise SaaS

Recently, SaaS applications are developed as a composition of microservices that serve diverse tenants having similar but different requirements, and hence, can be developed as variability-intensive microservices. Manual identification of these microservices is difficult, time-consuming, and costly, since, they have to satisfy a set of quality metrics for several SaaS architecture configurations at the same time. In this paper, we tackle the multi-objective optimization problem of identifying variable microservices aiming optimal granularity (new metric proposed), commonality, and data convergence, with a search-based approach employing the MOEA/D algorithm. We empirically and experimentally evaluated the proposed method following the Goal-Question-Metric approach. The results show that the method is promising in identifying fully consistent, highly reusable, variable microservices with an acceptable multi-tenancy degree. Moreover, the identified microservices, although not structurally very similar to those identified by the expert architects, provide design quality measures (granularity, etc.) close to (and even better than) the experts.     

Produktdifferenzierung f��r?Software-as-a-Service-Anbieter

The market for the new provisioning type Software-as-a-Service (SaaS) has reached a significant size and still shows enormous growth rates. By varying size of SaaS products, providers can improve their market position and profits by successfully acting in the tension area of customer acquisition, pricing and costs. We first elaborate differences concerning product differentiation between classic software provisioning models and SaaS. Then, we introduce a micro-economic based decision model to maximize the return of a provider by finding an optimal granularity, i.e. by varying the size of services. This paper makes two contributions in this context: (1) it provides a conceptual foundation for product differentiation within the scope of SaaS and (2) it presents the first implementation of variable reproduction costs for web based software offers. The model is illustrated by a real world case with data from a SaaS provider.     

Data Partitioning and Redundancy Management for Robust Multi-Tenancy SaaS

Software-as-as-Service (SaaS) is a new approach for developing software, and it is characterized by its multi-tenancy architecture and its ability to provide flexible customization to individual tenant. However, the multi-tenancy architecture and customization requirements introduce many new issues in software, such as database design, database partition, scalability, recovery, and continuous testing. This paper proposes a hybrid test database design to support SaaS customization with two-layer database partitioning. The database is further extended with a new built-in redundancy with ontology so that the SaaS can recover from ontology, data or metadata failures. Furthermore, constraints in metadata can be used either as test cases or policies to support SaaS continuous testing and policy enforcement.     

Dynamic resource demand prediction and allocation in multi‐tenant service clouds

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

Toward the automation of a QoS-driven SLA establishment in the Cloud

Composite software as a service (SaaS)-based SOA offers opportunities for enterprises to offer value-added services. The cornerstone for such a business is service level agreements between Cloud customers and Cloud providers. In spite of the hype surrounding composite SaaS, standardized methods that enable a reliable management of service level agreements starting from the SLA derivation from the customer requirements to the SLA establishment between the two stockholders are still missing. To overcome such a drawback, we propose a method for SLA establishment guided by QoS for composite SaaS. Our method provides: (1) a requirement specification language for the Cloud customer to define the composition schemas of the requested services along with its QoS constraints; (2) a Cloud provider offer specification language and method to help in identifying the services and resources that satisfy the customer requirements; and (3) an SLA document definition language and method to specify a deployable composite SaaS on the Cloud. Our approach for SLA establishment embraces model-driven architecture principles to automate the SLA document generation from the customer requirements document. The automation is handled through model transformations along with enrichment algorithms to ensure the generation of complete SLA documents.     

面向多租约SaaS应用的负载均衡机制研究与实现

随着云计算的普及,SaaS作为一种新的软件交付模式得到了越来越广泛地关注.为了支持高速业务发展带来的大量用户的访问请求,多租约SaaS应用需要负载均衡机制以支持可扩展性.本文从对多租约SanS应用用户需求的分析入手,提出了一种面向租约功能类型的服务器负载模型和面向租约用户非功能需求的执行请求按需分配算法,设计了一个面向多租约SanS 应用的负载均衡系统.实验表明本文提出的面向多租约SaaS应用的负载均衡机制能在满足不同租约用户需求的同时提高系统整体执行效率.     

面向SaaS应用的SMTDM可伸缩多租户数据管理框架

云计算是一种可以弹性并按需提供资源的技术.在多租户共享数据存储模式下,如何实现数据的动态伸缩存储是云数据管理的关键.针对SaaS应用如何随着租户数量及请求规模的变化而进行自适应伸缩的问题,在分析数据存储层的伸缩性需求的基础上,基于Walraven等人提出的多租户架构思想,扩展典型的云应用架构,设计了一个多租户数据管理框架,实现存储资源的弹性.基于该框架,开发了一个面向网络管理领域的SaaS原型系统,验证了其有效性和可用性.