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Cost-Effective Large-Scale Digital Twins Notification System with Prioritization Consideration

Large-Scale Digital Twins Notification System (LSDTNS) monitors a Digital Twin (DT) cluster for a predefined critical state, and once it detects such a state, it sends a Notification Event (NE) to a predefined recipient. Additionally, the time from producing the DT's Complex Event (CE) to sending an alarm has to be less than a predefined deadline. However, addressing scalability and multi-objectives, such as deployment cost, resource utilization, and meeting the deadline, on top of process scheduling, presents a complex challenge. Therefore, this thesis presents a complex methodology consisting of three contributions that address system scalability, multi-objectivity and scheduling of CE processes using Reinforcement Learning (RL).
The first contribution proposes the IoT Notification System Architecture based on a micro-service-based notification methodology that allows for running and seamlessly switching between various CE reasoning algorithms. Our proposed IoT Notification System architecture addresses the scalability issue in state-of-the-art CE Recognition systems.
The second contribution proposes a novel methodology for multi-objective optimization for cloud provisioning (MOOP). MOOP is the first work dealing with multi-optimization objectives for microservice notification applications, where the notification load is variable and depends on the results of previous microservices subtasks. MOOP provides a multi-objective mathematical cloud resource deployment model and demonstrates effectiveness through the case study.
Finally, the thesis presents a Scheduler for large-scale Critical Notification applications based on a Deep Reinforcement Learning (SCN-DRL) scheduling approach for LSDTNS using RL. SCN-DRL is the first work dealing with multi-objective optimization for critical microservice notification applications using RL. During the performance evaluation, SCN-DRL demonstrates better performance than state-of-the-art heuristics. SCN-DRL shows steady performance when the notification workload increases from 10% to 90%. In addition, SCN-DRL, tested with three neural networks, shows that it is resilient to sudden container resources drop by 10%. Such resilience to resource container failures is an important attribute of a distributed system.

Identiferoai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/45752
Date19 December 2023
CreatorsVrbaski, Mira
ContributorsBolić, Miodrag, Majumdar, Shikharesh
PublisherUniversité d'Ottawa / University of Ottawa
Source SetsUniversité d’Ottawa
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf
RightsAttribution 4.0 International, http://creativecommons.org/licenses/by/4.0/

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