Using Rules and Data Dependencies for the Recovery of Concurrent Processes in a Service-Oriented Environment


This paper presents a recovery algorithm for service execution failure in the context of concurrent process execution. The recovery algorithm was specifically designed to support a rule-based approach to user-defined correctness in execution environments that support a relaxed form of isolation for service execution. Data dependencies are analyzed from data changes that are extracted from database transaction log files and generated as a stream of deltas from Delta-Enabled Grid Services. The deltas are merged by time stamp to create a global schedule of data changes that, together with the process execution context, are used to identify processes that are read and write dependent on failed processes. Process interference rules are used to express semantic conditions that determine if a process that is dependent on a failed process should recover or continue execution. The recovery algorithm integrates a service composition model that supports nested processes, compensation, contingency, and rollback procedures with the data dependency analysis process and rule execution procedure to provide a new approach for addressing consistency among concurrent processes that access shared data. We present the recovery algorithm and also discuss our results with simulation and evaluation of the concurrent process recovery algorithm.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Multitask Learning Model for Traffic Flow and Speed Forecasting ABSTRACT: Accurate short-term traffic state forecasting is beneficial to Intelligent Transportation Systems (ITS) research and applications. This
PROJECT TITLE : A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks ABSTRACT: In deregulated energy markets, accurate electricity price forecasting (EPF)
PROJECT TITLE : A Supervised Machine Learning Algorithm for Heart Rate Detection Using Doppler Motion-Sensing Radar ABSTRACT: The development of vital sign radar technology has shown to be an effective tool for measuring various
PROJECT TITLE : Comparing Different Resampling Methods in Predicting Students Performance Using Machine Learning Techniques ABSTRACT: Predicting students' performance is one of the most valuable and important research areas in
PROJECT TITLE : Convolutional Recurrent Neural Networks for Glucose Prediction ABSTRACT: Blood glucose control is critical for diabetes management. Machine learning techniques are used in current digital therapy approaches for

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry