Hong Cai’s research while affiliated with IBM and other places

A computer implemented method for locating isolation points in an application under multi-tenant environment includes scanning, using a computer device an application by using scanning rules, to obtain potential isolation points and relationships between the potential isolation points; specifying at least one isolation point among the potential isolation points; and screening an isolation point from the potential isolation points by using relationships between the specified at least one isolation point and the remaining potential isolation points.

SaaS (Software as a Service) provides new business opportunities for application providers to serve more customers in a scalable and cost-effective way. SaaS also raises new challenges and one of them is multi-tenancy. Multi-tenancy is the requirement of deploying only one shared application to serve multiple customers (i.e. tenant) instead of deploying one dedicated application for each customer. This paper describes the authors’ practice of developing and deploying multi-tenant technologies. This paper targets a technology that could quickly enable existing Java EE (Enterprise Edition) applications to be multi-tenancy enabled thus having the benefit of quick time to market. This paper describes the overall framework of multi-tenant SaaS platform, how to migrate an existing Java EE application, how to provision the multi-tenant application, and how to onboard the tenants. The paper also shows experiments which compare the economics of multi-tenant SaaS deployment versus traditional application deployment (one application for one tenant) with precise data.

SaaS (Software as a Service) provides new business opportunities for application providers to serve more customers in a scalable and cost-effective way. SaaS also raises new challenges and one of them is multi-tenancy. Multi-tenancy is the requirement of deploying only one shared application to serve multiple customers (i.e. tenant) instead of deploying one dedicated application for each customer. This paper describes the authors’ practice of developing and deploying multi-tenant technologies. This paper targets a technology that could quickly enable existing Java EE (Enterprise Edition) applications to be multi-tenancy enabled thus having the benefit of quick time to market. This paper describes the overall framework of multi-tenant SaaS platform, how to migrate an existing Java EE application, how to provision the multi-tenant application, and how to onboard the tenants. The paper also shows experiments which compare the economics of multi-tenant SaaS deployment versus traditional application deployment (one application for one tenant) with precise data.

Virtual machines are allocated on demand in virtualized cloud platforms to provide flexible and reliable services. The major difficulty lies in satisfying the conflicting objectives of reducing response time while lowering resource costs. In this paper, a mathematical multi-tier framework for virtual machine allocation is proposed, which can be used to capture the performance of the cloud platform. We first use simulations to derive virtual resource allocation policies, and later use real benchmarking applications to verify the effectiveness of this framework. Experimental results show that the model can be simply and effectively used to satisfy the response time requirement as well as lowering the cost of using the virtual machine resources.

Provisioning Virtual machines on demand is significant in elastic compute cloud for reliable service delivery. The importance and major difficulty lies in satisfying the conflicting objectives of satisfying contracted service level agreement while lowering used resource costs. In this paper, the authors propose a mathematical multi-tier framework for adaptive virtual resource allocation problem. The framework captures the performance of the virtualized cloud platform gracefully. The authors first use simulations to derive virtual resource allocation policies, and later use real benchmarking applications, to verify the effectiveness of this framework. Experimental results show that the model can be simply and effectively used to satisfy the response time requirement as well as lowering the cost of using the virtual machine resources.

Selecting and utilizing proper virtual machines in a virtualized cloud platform to achieve high availability, throughput, reliability, as well as low cost and makespan is very important. The importance lies in the adaptive resource provisioning to satisfy variant of workloads. An Adaptive Accessing Aware Algorithm (A5) is proposed in this paper to deal with this conflicting objective optimization problem. The main strategy of A5 is selecting adaptive upper/lower bound of service capacity to decide the time for scheduling redundant virtual machines and a Pareto-front based multi-objective optimization method to decide the number of scheduling virtual machines. We carried out experiments in simulation, which show that A5 can achieve much higher performance improvements in four different workload testing environments, compared with other three commonly used methods. Keywords— Pareto-front; redundancy; upper/lower bound; virtualized cloud platform; virtual machine 1 Manuscript submitted to International Journal on Modeling, Simulation and Scientific Computing on 24 July 2010; revised version submitted on 25 Sept. 2010. Part of this work is performed when Fan Zhang is a intern student in IBM China Development Lab supported by 2010-2011 IBM Ph.D. Fellowship.

This paper presents AMREF, an Adaptive Map Reduce Framework designed for an effective use of computational resources in data center networks to deal with real time data intensive applications. AMREF entails its adaptivity from adaptive splitter, adaptive mappers and adaptive reducers in a stochastic control manner. We use three methods, feedback control, stochastic learning with smooth filter and kalman filter to implement the framwork. Comparison among the three methods suggests they can be effectively and efficiently used to reduce the makspan in three different real-world workload scenarios.

It has become more and more obvious that in Cloud Computing, applications are key drivers to make Cloud business a success. Multi-tenancy is a critical technology to allow one instance of application serving multiple customers at the same time to share Cloud resources and achieve high operational efficiency. There are different options of realizing multi-tenancy, in this paper we describe a transparent approach of making existing Web applications to support multi-tenancy and run in the public Cloud. Our approach includes intercepting the Web requests and deriving the tenant context, carrying the tenant context with a thread in the Web container, manipulating the isolation points (application artifacts that need to be isolated) in a Web application, and propagating tenant context to remote Cloud resources (such as database server, and message queue server) when necessary. With this approach, volumes of existing Web applications could quickly be provisioned through a public Cloud platform without rewriting the original source code. We have also implement a real system based on the common multi-tenancy model that separate the concerns of application developer, SaaS operator, tenant administrator, and tenant user. We finally integrate the SaaS multi-tenancy technique with Cloud platform services.

This paper aims to explore the cloud computing based service innovation in home textile industrial clusters, which is regarded as a service system. Firstly, the challenges facing home textile industrial clusters (industrial upgrading, structural adjustment, and brand building & management) are analyzed. Secondly, the producer services in the cluster service system are studied. Moreover, it is proposed that the producer services can be enabled and enhanced by innovated cloud platform. The presented IT service platform based on cloud computing and SaaS (Software as a Service) can help enhance the competitiveness of the textile industrial clusters, even innovating the business model.