Liang-Jie Zhang

A Practical Architecture of Cloudification of Legacy Applications

Cloud computing has been attracting much attention since its birth. How to cloudify software systems especially legacy applications in the cloud era is becoming increasingly important. Based on RGPS meta-model framework and International standards-ISO/IEC 19763, an architecture for cloudification of legacy applications is proposed, which consists of three parts: a Web portal, a SaaS service supermarket, and a SaaS application development platform. In this paper, we take an open source software as an example to illustrate the proposed approach. Based on the architecture and supporting techniques on software virtualization and multi-tenancy, we develop a prototype Cloud CRM to demonstrate the basic procedure for cloudification of legacy applications, as well as the feasibility of the proposed approach.

CCRA: Cloud Computing Reference Architecture

As Cloud Computing has become more and more popular, various Cloud Computing architectures or infrastructure have been defined, given their specific circumstances for the applications. However, to effectively achieve the potential of cloud computing, there is need for the definition of system architecture of the software systems involved in the delivery of cloud computing, so that it can be used as a reference for the architects or software engineering. In this paper, reference architecture of Cloud Computing is proposed. Its objective and principles are illustrated. And case studies of a SaaS, PaaS platform architecture instantiated from CCRA are given.

A Ranking-Oriented Hybrid Approach to QoS-Aware Web Service Recommendation

Nowadays, more and more service consumers pay great attention to QoS (Quality of Service) when they find and select appropriate Web services. For most of the approaches to QoS-aware Web service recommendation, the list of Web services recommended to target users is generally obtained based on rating-oriented predictions, aiming at predicting the potential ratings that a target user may assign to the unrated services as accurately as possible. However, in some scenarios, high accuracy of rating predictions may not necessarily lead to satisfactory recommendation results. In this paper, we propose a ranking-oriented hybrid approach by combining item-based collaborative filtering techniques and latent factor models to address the problem of Web services ranking. In particular, the similarity between two Web services is measured in terms of the correlation coefficient between their rankings instead of between their ratings. Comprehensive experiments on the QoS data set composed of real-world Web services are conducted to test our approach, and the experimental results demonstrate that our approach outperforms other competing approaches.

A CCRA Based Mass Customization Development for Cloud Services

With the incredible popularity of cloud computing, the adoption of mass customization (MC) is significant for building a cloud computing system that could provide services provisioning in a manner of multi-tenancy. Because of lack of a standard architecture that supports MC development for cloud services, the existing metadata or model driven approaches have insufficient abilities to realize personalized requirements with mass production when applied to product development in large-scale enterprises. Aiming at these problems, this paper presents a novel MC-based development approach for enterprise-level business cloud services based on the specification of the Cloud Computing Reference Architecture (CCRA), and shares the practice about how the approach is applied to building Kingdee K/3 Collaboration Development Cloud (CDC). Successful practice has proved that by adopting our MC development approach, we can develop platforms and tools on the cloud at a low cost and more effectively.

A Key-Value Based Application Platform for Enterprise Big Data

Big data poses big challenges to modern enterprises. Traditional data-intensive business applications begin to fall behind the times, because of insufficient capabilities to process large data volumes, ever-changing streaming data and unstructured information. Furthermore, large, complex and all-in-one enterprise applications are no longer popular, whereas lightweight and fragmentary applications become welcome for the sake of the bursts of cloud computing and mobile internet. Below this kind of situation, this paper proposed a big data application platform for enterprises to simply develop and operate personalized data retrieval, data analysis, business intelligence and other data-intensive services. Unlike current related products, it is distinguished for the design of key-value based hybrid data storage and a service-oriented outsourcing architecture. The former is used to resolve the frequently-encountered issue of diversified and massive data storage, and the latter enables an open environment for third-party vendors, which promotes a self-increasing services ecosystem for big data application development. In order to validate the feasibility of our platform, the paper also developed a sentiment analysis application for Kingdee popular products based on the services provided by the platform. The result of our experiment proves that the platform shows enough potential to effectively facilitate data-intensive application development.

A Robust Retail POS System Based on Blockchain and Edge Computing.

New Retail has recently become one of the hottest concepts in the world, particularly in China. Many Internet technologies like Cloud Computing have been employed to address the limitations of the traditional retail industry, and significant progress has been made towards this direction. Despite these achievements, an intractable issue faced by the existing cloud-based retail POS systems is that they cannot provide continuous services when the Internet connections are interrupted. Towards this issue, in this paper, we leverage two new technologies, Blockchain and Edge Computing, to design and develop a new robust retail POS system. More specifically, this type of POS systems deployed in a retail store can use blockchain networking, trustworthiness, and security. We take all cash registers as nodes to build a POS blockchain network and store transaction records in the blockchain network to deal with unexpected network interruptions. Once the Internet connection recovers, a node in the blockchain network will be selected as a POS edge computing server to synchronize data with the POS cloud and resume regular communication between them. The advantages of the robust retail POS system over traditional POS systems include less dependency on the Internet in case of sudden interruptions and little or no hands-on intervention required for changes in our POS system caused by external changes.

Localized Mandarin Speech Synthesis Services for Enterprise Scenarios

Speech interaction systems have been gaining popularity in recent years. For these systems, the performance of speech synthesis has become a key factor to determine quality of service (QoS) and user experience in real-world speech interaction systems. How to improve the efficiency of speech synthesis has become a hot topic and represents one of the main streams in specific scenarios of human-computer interactions. In this paper, we propose a low-latency hidden Markov model (HMM)-based localized Mandarin speech synthesis architecture which uses a shared global variance for all the Gaussian mixture models (GMMs). Through this strategy, the memory consumption for loading the acoustic model has been reduced greatly. We also encapsulate the speech synthesis as a service using epoll mechanism so that the synthesis engine can be initialized by preloading the text analysis model and acoustic model, and can be invoked by multiple processes simultaneously, thus further improving the efficiency of speech synthesis. Experimental results demonstrate that our proposed method can significantly reduce the time latency while maintaining voice quality of synthesized speeches.

Comprehensive Evaluation of Urban Sustainable Innovation Ability Based on Factor Analysis Method

With the economic globalization, urban sustainableinnovation ability has become one of the important factor ofurban comprehensive competitiveness. Urban sustainableinnovation ability is referred to as an ability that a citytransforms the various factors such as information into newproducts, new services, which is also directly related to thesustainable development driving force and long-termcompetitiveness of city or region.

Business Graphing for Internet-Enabled Enterprises

User profiling is a typical big data service created and utilized by an increasing number of Internet venders, which maintains a customized model of interests or essential attributes of their existing users by looking for insights into their behaviors. The Internet industrys best practices indicate that user profiles can help venders much more sufficiently understand their customers. As a result, they can help vendors design rational products and provide personalized services. However, user profiling is not enough to satisfy the business goals of many Internet-enabled traditional enterprises, e.g., manufacturing. In these enterprises, users are just a kind of stakeholders in their complex business contexts. In addition to paying much attention to the user goals, the enterprises are usually more concerned about the operating statuses of its business, which cannot be covered by user profiling. Towards this issue, we propose the concept of business graph to serve as a basic data service to Internet-enabled enterprises, which could help these enterprises gain insights into their current business much more globally and dynamically. Compared with user profiles and knowledge graphs, the business graph has the following characteristics: 1) it reflects a global business insight rather than a user insight, 2) it depicts underlying relationships as well as real relationships, and 3) all the technologies are easy to realize. Corresponding platforms and industrial practices on business graphs are also introduced.

A Platform Based Distributed Service Framework for Large-Scale Cloud Ecosystem Development

In the era of Internet, service-oriented development becomes a popular software development paradigm for developing large-scale systems and cloud apps. Followed by this new emerging paradigm, functions would be designed and developed as components, deployed separately and provided as services for integration. In generally, experienced developers tend to introduce some reusable distributed service frameworks in their projects. These frameworks are usually developed based on Service-Oriented Architecture (SOA), and take charge of Remote Procedure Call (RPC), distributed service collaboration, remote service communication and other common duties. It has been proved that these distributed service frameworks are a kind of effective software infrastructures to facilitate development. However, in large-scale cloud ecosystems development, chaos might occur if interdependent remote services composed together to enable cloud services and complex systems. Towards this problem, this paper proposes a novel framework developed by leveraging a platform as a unified access gateway of remote services for different cloud services in the ecosystem. Experimental results show that the framework could effectively reduce the development difficulty of the large-scale cloud ecosystems, and improve the performance of the developed systems.