Wenhao An’s research while affiliated with China Telecom Beijing Research Institute and other places

As a popular technique in Cloud computing, Multi-Tenancy (MT) can significantly ease software maintenance, and improve resource utilization. To make use of the MT technique, an application may need to be transformed to be MT-enabled. This process involves finding and processing a special kind of data entities named Global Isolation Points (GIPs). Practically, finding all GIPs of an application is challenging. Traditional method involves manually browsing the application code, requiring a great deal of human effort. To solve this problem, we introduce a toolkit named Auto-MT to help find and process GIPs of an application. Auto-MT is able to find new GIPs based on their relations to known GIPs. To characterize the relation, a novel graph called Value Flow Graph (VFG) is introduced, which models the value flows of data entities. It can also be used in other scenarios, like taint analysis. We have implemented Auto-MT as an Eclipse Plug-in, and applied it to transform Roller, a widely used Java application. Experimental results show that Auto-MT saves substantial human effort, and accelerates the process of transforming applications to be MT-enabled.

A method, apparatus, and a computer program product for storing and accessing multi-tenancy data. The method includes the steps of: creating a plurality of table sets in one or more databases, wherein each table set is used to store data of a group of tenants selected from a plurality of tenants; accessing data of a tenant in a table set in response to receiving a data access request from the tenant; and recording relationships between the tenants and the table sets in a multi-tenancy metadata repository, wherein the step of accessing the data of the tenant comprises the steps of finding the table set by querying the metadata repository and accessing the data of the tenant in the table set based on the result received from the query of the metadata repository.

Analytical solutions are considered as increasingly important for modern enterprises. Currently, systematical adoption of analytical solutions is limited to only a small set of large enterprises, as the deployment cost is high due to high performance hardware requirement and expensive analytics software. Moreover, such on-premises solutions are not suitable for the occasional analytics consumers. In order to accelerate the prevalence of analytical solutions, this paper explores the feasibility of leveraging SaaS (Software-as-a-Service) delivery model to provide analytics capabilities as services in a cost-effective way. The main contributions of our work include: (1) proposing a framework to enable enterprise tenants to consume analytics capabilities as services; (2) developing a method to enhance existing analytics platform to support multi-tenancy so that a single software instance can effectively support multiple concurrent tenants; (3) designing an SLA (Service Level Agreement) customization mechanism to satisfy the diverse analytics capability demands of tenants. A prototype system has been developed to evaluate the feasibility of our approach.

Multi-tenant is a key characteristic for cost effective Software as a Service (SaaS) applications which drive down total cost of ownership for both service consumers and providers. This paper describes our research in designing & building a cost-effective, secure, customizable, scalable and non-intrusive multi-tenant database which greatly accelerates the migration and development of SaaS applications. We analyze the requirements and gaps in traditional database when supporting SaaS scenario, and then propose a novel nonintrusive multi-tenant database framework to address these challenges. Some key considerations and different implementation approaches in designing and implementation such a framework are discussed and compared. This paper also identifies some potential database performance optimization approaches in the multi-tenant scenario.

Analytical Solutions increasingly play a key role in the modern enterprise business. Currently, such solutions are usually very costly for customer to consume, as the deployment cost is high due to high performance hardware requirement and complex software configuration. Moreover, such on-premises solutions are not suitable for the occasional analytics consumers. On the other hand, Analytical solutions are also hard for solution providers to deliver cost efficient service, since the cost is high in initial customer engagement as well as conducting incremental service. To deliver analytical solutions in a cost-effective way, we propose the idea of "analytical cloud", which is designed to provide on-demand decision support, analytical capabilities, and computational resources in a manageable cloud environment. In this paper, we summarize the potential business models supported by this new delivery model and identify the technical issues required to be addressed in such business models.