Dynamic and adaptive execution models for data stream mining applications in mobile edge cloud computing systems / Muhammad Habib Ur Rehman - Muhammad Habib , Ur Rehman (2016)
Mobile edge cloud computing (MECC) systems extend computational, networking, and storage capabilities of centralized cloud computing systems through edge servers at one-hop wireless distances from mobile devices. Mobile data stream mining (MDSM) applications in MECC systems involve massive heterogeneity at application and platform levels. At application level, the program components need to handle continuously streaming data in order to perform knowledge discovery operations. At platform level, the MDSM applications need to seamlessly switch the execution processes among mobile devices, edge servers, and cloud computing servers. However, the execution of MDSM applications in MECC systems becomes hard due to multiple factors. The critical factors of complexity at application level include data size and data rate of continuously streaming data, the selection of data fusion and data preprocessing methods, the choice of learning models, learning rates and learning modes, and the adoption of data mining algorithms. Alternately, the platform level complexity increases due to mobility and limited availability of computational and battery power resources in mobile devices, high coupling between application components, and dependency over Internet connections. Considering the complexity factors, existing literature proposes static execution models for MDSM applications. The execution models are based on either standalone mobile devices, mobile-to-mobile, mobile-to-edge, or mobile-to-cloud communication models. This thesis presents the novel architecture which utilizes far-edge mobile devices as a primary execution platform for MDSM applications. At the secondary level, the architecture executes MDSM applications by enabling direct communication among nearer mobile devices through localWi-Fi routers without connecting to the Internet. At tertiary level, the architecture enables far-edge to cloud communication in case of unavailability of onboard computational and battery power resources and in the absence of any other mobile devices in the locality. This thesis also presents the dynamic and adaptive execution models in order to handle the complexity at application and platform levels. The dynamic execution model facilitates the data-intensive MDSM applications having low computational complexity. However, the adaptive execution model facilitates in seamless execution of MDSM applications having low data-intensity but high computational complexities. Multiple evaluation methods were used in order to verify and validate the performance of proposed architecture and execution models. The validation and verification of the proposed architecture were performed using High-Level Petri Nets (HLPN) and Z3 Solver. The simulation results revealed that all states in the HLPN model were reachable and the overall design presented a workable solution. However, proposed architecture faced the state explosion problem wherein conventional static execution models fail because the system may enter in multiple states of execution from a single state. The proposed dynamic and adaptive execution models help address the issue of the state explosion problem. To this end, the proposed execution models were tested with multiple MDSM applications mapping to a real-world use-case for activity detection using MECC systems. The experimental evaluation was made in terms of battery power consumption, memory utilization, makespan, accuracy, and the amount of data reduced in mobile devices. The comparison showed that proposed dynamic and adaptive execution models outperformed the static execution models in multiple aspects.