Yu Chen Zhou

Business Process Centric Platform-as-a-Service Model and Technologies for Cloud Enabled Industry Solutions

With the popularity of cloud computing, Platform-as-a Service (PaaS) becomes one of the core technical enablers by enterprise to change the services to both customers and internal organizations. An application in an enterprise needs to take into account various specific requirements for hosting in private and hybrid cloud, with unique requirements on rapid development, simplicity for deployment and management, integration with existing solution and compliance to industry standards, etc. In this paper, a novel business process centric PaaS model is introduced, which is targeted at supporting above requirements for cloud enabled industry solutions in an enterprise. Firstly, the emerging requirements of PaaS for cloud enabled industry solutions and the general features to meet such requirements are discussed. Then, the architecture and patterns for integrating with existing solutions are introduced. And the technologies to implement such PaaS model are presented including codeless developer workspace and automatic application generator. As well, to enable this PaaS model for “programmable SaaS”, BPM multi-tenancy is introduced. Based on the model and technologies, we designed and implemented cloud enabled industry solutions for Telecommunication, Chemical and Petroleum, Financial and Healthcare industries. This paper demonstrates how these technologies and architectures significantly enhance the capability of PaaS in the context of industry solutions and enterprise environments.

Service Storm: A Self-Service Telecommunication Service Delivery Platform with Platform-as-a-Service Technology

To attract and serve an expanded customer base, telecommunication service providers need to provide more targeted, focused and personalized services, which brings challenges to both business model and technologies to development of value added services. In this paper, we introduce Service Storm, a novel self-service telecommunication Service Delivery Platform with Platform-as-a-Service technologies to support rapid and flexible construction and delivery of value added services in the cloud environment as an open developer community. Firstly, we analyze the characteristics of four categories of users including Individual User, Organization User, Telecommunication Operator, and Business Partner, and present the architecture of Service Storm to support new business model targeting at long tail applications as well as conventional business models. Then we highlight the key technologies including 1) Rapid assembly model and tools for codeless and off-premise integration of telecom services and application logic based on Web 2.0 technologies, 2) Cloud based resource isolation, management, capacity planning and scaling for dynamic topology deployment and elastic infrastructure support, and 3) Automatic deployment and monitoring in runtime. Finally we illustrate the sample of SMS and Web based mobile workflow for insurance order management to show the advantages of Service Storm. We demonstrate how these technologies and architectures enable the new business model for telecommunication operator and significantly enhance the diversity of value added services with lower cost and shorter time to market.

Context Aware Service Policy Orchestration

While service oriented computing is gaining popularity and design methodology for functional features is becoming mature, design of non-functional features and governance capabilities is increasingly important. WS-policy has been touted as a standard way to specify service policy for these features and map service policy to upper layers of SOA policy model for SOA governance. This paper introduces a novel model-driven method to orchestrate service policy within system context in design time. By applying top-down SOA design methodology, firstly the logical service policy model is created to describe relation among service policy in logical level, and then it is transformed to physical service policy model with more factors of real system topology considered. Finally, service policy deployment model is generated to describe the relation among policy related artifacts and policy repositories, and guide and automate the deployment of policies in runtime environment as well. Leveraging system topology and behavior information, service policies are orchestrated vertically and horizontally according to service policy dependency, business policy, service level agreement (SLA), and domain specific constraints. Meta-policy based service policy conflict detection and resolution is highlighted. We demonstrate how this approach significantly enhances the capability to ensure the correctness and consistency of service policy within service oriented computing environment.

Enabling Integrated Information Framework as Cloud Services for Chemical and Petroleum Industry

Business agility is of vital importance to chemical and petroleum industry, especially in rapid response to diagnose and exchange of real-time information and other relevant data sources. From IT perspective, emerging delivery models such as cloud computing offer the possibility to build the elastic infrastructure and flexible computing platform, thus enable enterprises to focus on their core competences. In this paper we investigate how the new delivery models of cloud service achieve the improvement of the efficiency by means of Integrated Information Framework (IIF) of chemical and petroleum. In infrastructural level, the capabilities and implementation mechanism of cloud based IIF solution lifecycle management are presented. Based on such management foundation, a business process environment delivered in Platform-as-a-Service business model is provided for rapid development of business agility. Finally, IIF in public and enterprise SaaS models to enable the chemical and petroleum value chain and service market is discussed. This paper demonstrates how the cloud computing technologies and architecture patterns significantly enhance the capability and agility of not only chemical and petroleum using IIF, but also the enterprise with similar requirements, business models and IT architectures in other industries.

Context Model Based SOA Policy Framework

With the popularity of SOA, SOA policy becomes one of the core technical enablers for SOA governance and management. Different in several aspects from traditional policy for distributed system management, policy applied in SOA solutions needs to take into account various policy types and enforcement points in different layers of SOA solution stack and different phases of SOA lifecycle. As well, it has the unique requirements on compliance to match existing SOA technologies and characteristics, as simplicity, standardization, high performance, etc. In this paper, a novel context model based SOA policy management framework by innovatively extending W3C Service Modeling Language (SML) and ISO Schematron is introduced. Firstly, a common context model distilled from SOA policy types typically including service policy, service governance policy, application policy and business policy, is presented. Then, the core components - context model based policy engine and definition tool are described. Finally, it is illustrated how the unified definition tools and policy engine are manipulated within the context model based SOA policy framework to manage and enforce policies in typical scenarios as service meta-data management, service match making and business process management. Based on the project, we participated in the works for defining W3C SML V1.1 working draft and proposed the works introduced in this paper to W3C SML Working Group. This paper demonstrates how these technologies and architectures significantly enhance the capability of SOA governance and management throughout whole SOA lifecycle and spanning the layers of SOA solution stack.

Adaptive Analytic Service for Real-Time Internet of Things Applications.

Emerging Internet of Things(IoT) applications are moving from silo and small scale sensor data sharing to composite and large scale ones. With the rapid growth of application scale, IoT applications is going to leverage cloud infrastructure for scalable solutions and real-time services. Thus large volumes of heterogeneous and high frequency sensor data are fed into IoT cloud services for real-time actionable insight, which raises great challenges of performance and adaptability on cloud solutions. In this paper, we propose a streaming based processing infrastructure for high throughput and low latency IoT real-time analytics services. A data adaptive mechanism is also introduced for heterogeneous data stream integration, interpreting and processing with application logics, as well as context stream. We implemented the proposed mechanisms with spark streaming, and deployed real time IoT analytics service in cloud. Experiment results show that the service has a good scalability and high throughput for IoT data analytics.

Cognitive Acoustic Analytics Service for Internet of Things

The rapid development of the Internet of Things (IoT) has brought great changes for non-contact and non-destructive sensing and diagnosis. For every inanimate object can tell us something by the sound it makes, acoustic sensor demonstrates great advantages comparing to conventional electronic and mechanic sensors in such cases: overcoming environmental obstacles, mapping to existing use cases of detecting problems with human ears, low cost for deployment, etc. It could be widely applied to various domains, such as predictive maintenance of machinery, robot sensory, elderly and baby care in smart home, etc. Whether we can use the acoustic sensor data to understand what is happening and to predict what will happen relies heavily on the analytics capabilities we apply to the acoustic data, which has to overcome the obstacles of noise, disturbance and errors, and has to meet the requirement of real-time processing of high volume signals with large number of sensors. In this paper, we propose a scalable cognitive acoustics analytics service for IoT that provides the user an incremental learning approach to evolve their analytics capability on non-intuitive and unstructured acoustic data through the combination of acoustic signal processing and machine learning technology. It first performs acoustic signal processing and denoising, enables acoustic signal based abnormal detection based on sound intensity, spectral centroid, etc. Then based on the accumulated abnormal data, a supervised learning method is performed as baseline and a neural network based classifier is used to recognize acoustic events in different scenarios with various volume of sample data and requirement of accuracy. In addition, acoustic sensor arrays processing is supported for localization of moving acoustic source in more complex scenario. In this paper, we designed a hybrid computing structure. Finally, we conduct experiments on acoustic event recognition for machinery diagnosis, and show that the proposed system can achieve high accuracy.