Stigmergy-based Load Scheduling in a Demand Side Management Context

This work proposes an approach, based on a fundamental coordination mechanism from nature, namely stigmergy. The proposed meta-heuristic is utilized to distributively calculate global schedules for a population of customers provided with intelligent devices. These schedules maximize renewable energy sources utilization. Furthermore, this approach is adapted and utilized as a coordination mechanism of autonomous customers to modify their consumption behavior in a real-time optimization context

A Ground Robot for Search And Rescue in Hostile Environment

The recent sheer developments in the field of robotics has encouraged the researcher to consider the robots assisting human in different aspects of life. In this context, search and rescue is a very interesting ambient where the capabilities offered by the robots can be used to not only augment the quality of service but also impose lower risk to the human members of the rescue team. To this purpose, project SHERPA has been defined to investigate an intelligent heterogeneous robotic team in a search and rescue mission. The robotic team includes flying robots such as fixed wing and quad copters for the purpose of patrolling and surveillance and a ground rover that is mainly considered to provide a mobile power replenishment service for the quadrotors. Navigation of the ground rover on the unstructured outdoor environment defined by the SHERPA is of the main focuses of this thesis. Due to roughness of the terrain, there are a lot of issues on the way of a successful localization. Moreover, the planning has to be compatible with the robot and environment constraints to avoid imposing a risk of mechanical damage to the system. To accomplish the battery exchange operation, the rover is equipped with two auxiliary devices namely "Sherpa box" and "Sherpa robotic arm". In this thesis, firstly, designs of the two devices are introduced to the reader in details. Secondly, their integration with the ground rover will be covered. Finally two important benchmarks of the SHERPA project, namely "human leashing" and "battery exchange operation", will be addressed

Multi-Tenant Geo-Distributed Data Analytics

University of Minnesota Ph.D. dissertation. July 2019. Major: Computer Science. Advisors: Abhishek Chandra, Jon Weissman. 1 computer file (PDF); x, 132 pages.Geo-distributed data analytics has gained much interest in recent years due to the need for extracting insights from geo-distributed data. Traditionally, data analytics has been done within a cluster/data center environment. However, analyzing geo-distributed data using existing cluster-based systems typically cannot satisfy the timeliness requirement of most applications and result in wasteful resource consumption due to the fundamental differences of the environments, especially due to the scarce, highly heterogeneous, and dynamic nature of the wide-area resources: compute power and network bandwidth. This thesis addresses the challenges faced by geo-distributed data analytics systems in ensuring high-performance and reliable execution of multiple data analytics applications/queries. Specifically, the focus is on sharing resources across multiple users, applications, and computing frameworks. Sharing resources is attractive as it increases resource utilization and reduces operational cost. However, ensuring high-performance execution of multiple applications in a shared environment is challenging as they may compete for the same resources, especially in a wide-area environment with scarce resources. Furthermore, dynamics such as workload variation, resource variation, stragglers, and failures are inevitable in large-scale distributed systems. These can cause large resource perturbation that significantly affect the performance of query executions. This thesis makes the following contributions. First, we present a resource sharing technique across multiple geo-distributed data analytics frameworks. The main challenge here is how to elastically partition resources while allowing high locality scheduling to each individual framework, which is critical to the execution performance of geo-distributed analytics queries. We then address the problem of how to identify and exploit common executions across multiple queries to mitigate wasteful resource consumption. We demonstrate that traditional multi-query optimization may degrade the overall query execution performance due to its lack of support for network awareness. Finally, we highlight the importance of adaptability in ensuring reliable query execution in the presence of dynamics, both for single and multiple query executions. We propose a systematic approach that can selectively determine which queries to adapt and how to adapt them based on the types of queries, dynamics, and optimization goals