Jicheng Fu

Achieving high performance web applications by service and database replications at edge servers

Edge server replication is an effective solution to achieve high performance in dynamic web applications, such as web services. Many web services involve frequent accesses to large-scale backend databases. Current database replication techniques are not directly applicable to edge server architectures. There is no algorithm to dynamically and automatically select the tables for replication. Also, most of the solutions do not consider the potentially limited disk sizes at the edge servers and the needs for them to serve many application sites. In this paper, we present a novel weighted table graph based database replication approach for edge servers to address these problems. Every step in our approach is based on quantitative computation, so it can generate an accurate result. Experimental studies show that our database replication approach significantly improves the performance of web systems in terms of client response latency, web application server offloading, and network bandwidth saving.

Simple and fast strong cyclic planning for fully-observable nondeterministic planning problems

We address a difficult, yet under-investigated class of planning problems: fully-observable nondeterministic (FOND) planning problems with strong cyclic solutions. The difficulty of these strong cyclic FOND planning problems stems from the large size of the state space. Hence, to achieve efficient planning, a planner has to cope with the explosion in the size of the state space by planning along the directions that allow the goal to be reached quickly. A major challenge is: how would one know which states and search directions are relevant before the search for a solution has even begun? We first describe an NDP-motivated strong cyclic algorithm that, without addressing the above challenge, can already outperform state-of-the-art FOND planners, and then extend this NDP-motivated planner with a novel heuristic that addresses the challenge.

Virtual Services in Cloud Computing

Cloud computing has aroused wide research interests and has been accepted by industry. Services are playing the essential role in cloud computing as cloud computing refers to “both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services”. Therefore, service-oriented architecture should play an important role in cloud computing. In addition, one of the characteristics of cloud computing is to make services available on demand. Given a group of services, different demands may involve different set of services and in different order. This is related to services reuse and composition. However, existing methods only solve service composition in a binary manner: Either a solution exists or no solutions at all. In this paper, we propose the concept of virtual services, which physically do not exist, but are conceptually treated in the same way as physical services. Virtual services are useful when traditional service compositions fail. Virtual services can connect the existing physical services and enable the composition process to succeed. The specifications of virtual services will provide valuable information about how to develop real services to meet the requirements of the given demand. In this paper, we present algorithms that can help identify the virtual services in the case of composition failure and provide the specification of virtual services for further analysis and development.

Model-Driven Prototyping Based Requirements Elicitation

This paper presents a requirements elicitation approach that is based on model-driven prototyping. Model-driven development fits naturally in evolutionary prototyping because modeling and design are not treated merely as documents but as key parts of the development process. A novel rapid program synthesis approach is applied to speed up the prototype development. MDA, AI planning, and component-based software development techniques are seamlessly integrated together in the approach to achieve rapid prototyping. More importantly, the rapid program synthesis approach can ensure the correctness of the generated code, which is another favorable factor in enabling the development of a production quality prototype in a timely manner.

Automated Discovery of Loop Invariants for High-Assurance Programs Synthesized Using AI Planning Techniques

The discovery of loop invariants is a great challenge for the independent verification of automatically synthesized programs. This verification is needed to achieve high confidence in the correctness of the synthesized code, i.e., assurance that no latent defects in the synthesizer itself could have led to the synthesis of an incorrect program. To address this problem, we present an automated loop invariant discovery approach for programs synthesized using a combination of AI planning and component-based software development techniques. Specifically, a plan (denoting the synthesized code) is generated by an enhanced Graphplan planner first. The loop invariants can be automatically discovered based on the same planning graph used to synthesize the code. The correctness can be independently verified via standard loop invariant proof steps, including initialization, maintenance, and termination. The proposed approach not only has a rigorous theoretical basis, but is also guaranteed to produce accurate invariants by removing spurious invariants that are independent of the concerned loop. In combination with other loop invariant detection techniques, the proposed approach can produce loop invariants for complex programs and, thus, greatly facilitate high-confidence automated verification of synthesized systems.

Automated AI Planning and Code Pattern Based Code Synthesis

The past decade has seen great progress in the development of embedded real-time systems, which are playing increasingly important roles in various application domains. However, the growing complexity of these systems has revealed the urgent need to develop advanced techniques to reduce the time-to-market as well as the overall system development cost. One method for achieving both of these goals is automated code synthesis combined with component based software development (CBSD). This enables the synthesizer to focus on generating the glue code needed to assemble an application from existing components. The main challenge is how to enable the synthesizer to recognize and generate complex conditional or loop statements. In this paper, a pattern-based code synthesis approach is enhanced to enable it to synthesize new loop statements. Specifically, we use an extended version of Graphplan to help the synthesizer to recognize and generate new loop statements. The paper proposes a planning domain model for code patterns and an automated code synthesis system, which integrates the enhanced AI planner with the code pattern integration system (CPIS) to fully automate the code synthesis process

Capturing and analyzing wheelchair maneuvering patterns with mobile cloud computing

Power wheelchairs have been widely used to provide independent mobility to people with disabilities. Despite great advancements in power wheelchair technology, research shows that wheelchair related accidents occur frequently. To ensure safe maneuverability, capturing wheelchair maneuvering patterns is fundamental to enable other research, such as safe robotic assistance for wheelchair users. In this study, we propose to record, store, and analyze wheelchair maneuvering data by means of mobile cloud computing. Specifically, the accelerometer and gyroscope sensors in smart phones are used to record wheelchair maneuvering data in real-time. Then, the recorded data are periodically transmitted to the cloud for storage and analysis. The analyzed results are then made available to various types of users, such as mobile phone users, traditional desktop users, etc. The combination of mobile computing and cloud computing leverages the advantages of both techniques and extends the smart phones capabilities of computing and data storage via the Internet. We performed a case study to implement the mobile cloud computing framework using Android smart phones and Google App Engine, a popular cloud computing platform. Experimental results demonstrated the feasibility of the proposed mobile cloud computing framework.

Development of intelligent model to determine favorable wheelchair tilt and recline angles for people with spinal cord injury

Machine-learning techniques have found widespread applications in bioinformatics. Such techniques provide invaluable insight on understanding the complex biomedical mechanisms and predicting the optimal individualized intervention for patients. In our case, we are particularly interested in developing an individualized clinical guideline on wheelchair tilt and recline usage for people with spinal cord injury (SCI). The current clinical practice suggests uniform settings to all patients. However, our previous study revealed that the response of skin blood flow to wheelchair tilt and recline settings varied largely among patients. Our finding suggests that an individualized setting is needed for people with SCI to maximally utilize the residual neurological function to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop an intelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk for wheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct an intelligent model that can predict whether a given tilt and recline setting will be favorable to people with SCI based on neurological functions and SCI injury history. Our results indicate that the intelligent model significantly outperforms the traditional statistical approach in accurately classifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is the first study using intelligent models to predict the favorable wheelchair tilt and recline angles. Our methods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt and recline guidance for people with SCI.

Using Artificial Neural Network to Determine Favorable Wheelchair Tilt and Recline Usage in People with Spinal Cord Injury: Training ANN with Genetic Algorithm to Improve Generalization

People with spinal cord injury (SCI) are at risk for pressure ulcers because of their poor motor function and consequent prolonged sitting in wheelchairs. The current clinical practice typically uses the wheelchair tilt and recline to attain specific seating angles (sitting postures) to reduce seating pressure in order to prevent pressure ulcers. The rationale is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues. However, our study reveals that a particular tilt and recline setting may result in a significant increase of skin perfusion for one person with SCI, but may cause neutral or even negative effect on another person. Therefore, an individualized guidance on wheelchair tilt and recline usage is desirable in people with various levels of SCI. In this study, we intend to demonstrate the feasibility of using machine-learning techniques to classify and predict favorable wheelchair tilt and recline settings for individual wheelchair users with SCI. Specifically, we use artificial neural networks (ANNs) to classify whether a given tilt and recline setting would cause a positive, neutral, or negative skin perfusion response. The challenge, however, is that ANN is prone to over fitting, a situation in which ANN can perfectly classify the existing data while cannot correctly classify new (unseen) data. We investigate using the genetic algorithm (GA) to train ANN to reduce the chance of converging on local optima and improve the generalization capability of classifying unseen data. Our experimental results indicate that the GA-based ANN significantly improves the generalization ability and outperforms the traditional statistical approach and other commonly used classification techniques, such as BP-based ANN and support vector machine (SVM). To the best of our knowledge, there are no such intelligent systems available now. Our research fills in the gap in existing evidence.

Deductive glue code synthesis for embedded software systems based on code patterns

Automated code synthesis is a constructive process that can be used to generate programs from specifications. It can, thus, greatly reduce the software development cost and time. The use of formal code synthesis approach for software generation further increases the dependability of the system. Though code synthesis has many potential benefits, the synthesis techniques are still limited. Meanwhile, components are widely used in embedded system development. Applying code synthesis to component based software development (CBSD) process can greatly enhance the capability of code synthesis while reducing the component composition efforts. In this paper, we discuss the issues and techniques for applying deductive code synthesis techniques to CBSD. For deductive synthesis in CBSD, a rule base is the key for inferring appropriate component composition. We use the code patterns to guide the development of rules. Code patterns have been proposed to capture the typical usages of the components. Several general composition operations have been identified to facilitate systematic composition. We present the technique for rule development and automated generation of new patterns from existing code patterns. A case study of using this method in building a real-time control system is also presented