Autonomic Overload Management For Large-Scale Virtualized Network Functions

The explosion of data traffic in telecommunication networks has been impressive in the last few years. To keep up with the high demand and staying profitable, Telcos are embracing the Network Function Virtualization (NFV) paradigm by shifting from hardware network appliances to software virtual network functions, which are expected to support extremely large scale architectures, providing both high performance and high reliability. The main objective of this dissertation is to provide frameworks and techniques to enable proper overload detection and mitigation for the emerging virtualized software-based network services. The thesis contribution is threefold. First, it proposes a novel approach to quickly detect performance anomalies in complex and large-scale VNF services. Second, it presents NFV-Throttle, an autonomic overload control framework to protect NFV services from overload within a short period of time, allowing to preserve the QoS of traffic flows admitted by network services in response to both traffic spikes (up to 10x the available capacity) and capacity reduction due to infrastructure problems (such as CPU contention). Third, it proposes DRACO, to manage overload problems arising in novel large-scale multi-tier applications, such as complex stateful network functions in which the state is spread across modern key-value stores to achieve both scalability and performance. DRACO performs a fine-grained admission control, by tuning the amount and type of traffic according to datastore node dependencies among the tiers (which are dynamically discovered at run-time), and to the current capacity of individual nodes, in order to mitigate overloads and preventing hot-spots. This thesis presents the implementation details and an extensive experimental evaluation for all the above overload management solutions, by means of a virtualized IP Multimedia Subsystem (IMS), which provides modern multimedia services for Telco operators, such as Videoconferencing and VoLTE, and which is one of the top use-cases of the NFV technology

A Case for a Programmable Edge Storage Middleware

Edge computing is a fast-growing computing paradigm where data is processed at the local site where it is generated, close to the end-devices. This can benefit a set of disruptive applications like autonomous driving, augmented reality, and collaborative machine learning, which produce incredible amounts of data that need to be shared, processed and stored at the edge to meet low latency requirements. However, edge storage poses new challenges due to the scarcity and heterogeneity of edge infrastructures and the diversity of edge applications. In particular, edge applications may impose conflicting constraints and optimizations that are hard to be reconciled on the limited, hard-to-scale edge resources. In this vision paper we argue that a new middleware for constrained edge resources is needed, providing a unified storage service for diverse edge applications. We identify programmability as a critical feature that should be leveraged to optimize the resource sharing while delivering the specialization needed for edge applications. Following this line, we make a case for eBPF and present the design for Griffin - a flexible, lightweight programmable edge storage middleware powered by eBPF

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Designing Scalable Mechanisms for Geo-Distributed Platform Services in the Presence of Client Mobility

Situation-awareness applications require low-latency response and high network bandwidth, hence benefiting from geo-distributed Edge infrastructures. The developers of these applications typically rely on several platform services, such as Kubernetes, Apache Cassandra and Pulsar, for managing their compute and data components across the geo-distributed Edge infrastructure. Situation-awareness applications impose peculiar requirements on the compute and data placement policies of the platform services. Firstly, the processing logic of these applications is closely tied to the physical environment that it is interacting with. Hence, the access pattern to compute and data exhibits strong spatial affinity. Secondly, the network topology of Edge infrastructure is heterogeneous, wherein communication latency forms a significant portion of the end-to-end compute and data access latency. Therefore, the placement of compute and data components has to be cognizant of the spatial affinity and latency requirements of the applications. However, clients of situation-awareness applications, such as vehicles and drones, are typically mobile – making the compute and data access pattern dynamic and complicating the management of data and compute components. Constant changes in the network connectivity and spatial locality of clients due to client mobility results in making the current placement of compute and data components unsuitable for meeting the latency and spatial affinity requirements of the application. Constant client mobility necessitates that client location and latency offered by the platform services be continuously monitored to detect when application requirements are violated and to adapt the compute and data placement. The control and monitoring modules of off-the-shelf platform services do not have the necessary primitives to incorporate spatial affinity and network topology awareness into their compute and data placement policies. The spatial location of clients is not considered as an input for decision- making in their control modules. Furthermore, they do not perform fine-grained end-to-end monitoring of observed latency to detect and adapt to performance degradations due to client mobility. This dissertation presents three mechanisms that inform the compute and data placement policies of platform services, so that application requirements can be met. M1: Dynamic Spatial Context Management for system entities – clients and data and compute components – to ensure spatial affinity requirements are satisfied. M2: Network Proximity Estimation to provide topology-awareness to the data and compute placement policies of platform services. M3: End-to-End Latency Monitoring to enable collection, aggregation and analysis of per-application metrics in a geo-distributed manner to provide end-to-end insights into application performance. The thesis of our work is that the aforementioned mechanisms are fundamental building blocks for the compute and data management policies of platform services, and that by incorporating them, platform services can meet application requirements at the Edge. Furthermore, the proposed mechanisms can be implemented in a way that offers high scalability to handle high levels of client activity. We demonstrate by construction the efficacy and scalability of the proposed mechanisms for building dynamic compute and data orchestration policies by incorporating them in the control and monitoring modules of three different platform services. Specifically, we incorporate these mechanisms into a topic-based publish-subscribe system (ePulsar), an application orchestration platform (OneEdge), and a key-value store (FogStore). We conduct extensive performance evaluation of these enhanced platform services to showcase how the new mechanisms aid in dynamically adapting the compute/data orchestration decisions to satisfy performance requirements of applicationsPh.D

Training binary neural networks without floating point precision

The main goal of this work is to improve the efficiency of training binary neural networks, which are low latency and low energy networks. The main contribution of this work is the proposal of two solutions comprised of topology changes and strategy training that allow the network to achieve near the state-of-the-art performance and efficient training. The time required for training and the memory required in the process are two factors that contribute to efficient training.Comment: 74 pages, Master's thesi