Liya Fan

A Cost-Effective Approach to Delivering Analytics as a Service

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.

A Non-intrusive Multi-tenant Database Software for Large Scale SaaS Application

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.

Deliver Bioinformatics Services in Public Cloud: Challenges and Research Framework

Bioinformatics is a developing interdisciplinary science which combines information technologies into biological researches. The techniques from this emerging field have shown great potential in many business areas including drug design, agriculture, and so on. Meanwhile, this new computational field has also been one of the largest consumers of computational power, as the analyses in bioinformatics are often extremely computationally or data intensive. Although there are already several projects which have done tentative exploration on deploying bioinformatics applications to cloud environments, the deployment is ad-hoc and restricted to a single private cloud environment. Moreover, the complexity of various demands of bench biologists and bioinformaticians also brings new challenges to bioinformatics cloud development. In this paper, we first identify the key participants and their interactions in a public bioinformatics cloud environment, where bioinformatic analyses are consumed as services on top of a cloud infrastructure. After that, we propose a research framework to discuss the domain-specific technical challenges in delivering such a solution. Finally, we summarize the existing related research efforts based on our framework and introduce our ongoing Web Lab project.

A Semi-Automatic Approach of Transforming Applications to be Multi-Tenancy Enabled

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.