DNN offloading and resource management over
edge computing systems

Ορφέας, Φιλιππόπουλος; Orfeas, Filippopoulos

dc.contributor.author	Ορφέας, Φιλιππόπουλος	el
dc.contributor.author	Orfeas, Filippopoulos	en
dc.date.accessioned	2024-04-24T10:38:05Z
dc.date.available	2024-04-24T10:38:05Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/59281
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.26977
dc.rights	Default License
dc.subject	Deep Learning	en
dc.subject	Υπολογιστικό νέφος	el
dc.subject	Ενισχυτική Μάθηση	el
dc.subject	Βαθειά Μηχανική Μάθηση	el
dc.subject	Serverless Computing	en
dc.subject	Edge Computing	en
dc.subject	Cloud Computing	en
dc.subject	Reinforcement Learning	en
dc.title	DNN offloading and resource management over edge computing systems	en
heal.type	bachelorThesis
heal.classification	Computer Science	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-10-31
heal.abstract	In today’s digital era, the explosive growth of data and the increasing prominence of artificial intel- ligence (AI) have made neural networks a linchpin of modern computing. These versatile AI models serve as the foundation for diverse applications, from image recognition to natural language processing, transforming industries and our digital landscape. As neural networks take center stage, the demand for their efficient execution becomes increasingly critical. Traditionally, neural networks are deployed in Cloud (Cloud computing) environments, which are known for their extensive computational resources located within data centers. While this approach offers significant computational power, it introduces challenges related to latency and network avail- ability. These challenges can be particularly limiting for applications that demand real-time respon- siveness. In this work, We deploy neural networks in the Edge (Edge computing). Edge computing represents an alternative approach to neural network deployment, seeking to address the limitations of traditional cloud environments. It brings computation closer to data sources, enabling real-time data processing. In this way We reduce exeuction latency of neural networks and enhance the responsiveness of ap- plications, making it ideal for scenarios where timely decision-making is critical. However, the edge environment presents its set of challenges. The devices operating at the edge vary widely in com- putational capacity, from high-performance servers to resource-constrained IoT devices. Managing this heterogeneity and efficiently allocating resources to ensure optimal neural network execution is complex. For this reason, We make use of Serverless computing. Serverless computing abstracts the complexities of infrastructure management, simplifying resource scaling, reducing operational overhead and optimizing resource utilization. This approach aligns seamlessly with edge environments. By leveraging Serverless computing in the Edge, We designed and developed a complete and robust framework for deploying neural networks in an edge cluster. On top of our framework is a Reinforcement Learning (RL) algorithm. Its core mission is twofold: first, to ensure that neural networks execution latency is within the defined SLAs, meeting response time targets; second, to optimize energy consumption by allocating tasks to energy-efficient devices whenever this is feasible. This RL algorithm plays a pivotal role in enhancing the overall efficiency and responsiveness of our system.	en
heal.advisorName	Soudris, Dimitrios	en
heal.committeeMemberName	Soudris, Dimitrios	en
heal.committeeMemberName	Tsanakas, Panagiotis	en
heal.committeeMemberName	Xidis, Sotirios	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	127 σ.	el
heal.fullTextAvailability	false