Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.Comment: 16 pages, 5 figures, Vision Pape

Survey of Enhancing Security of Cloud Using Fog Computing

Nowadays Fog Computing has become a vast research area in the domain of cloud computing. Due to its ability of extending the cloud services towards the edge of the network, reduced service latency and improved Quality of Services, which provides better user experience. However, the qualities of Fog Computing emerge new security and protection challenges. The Current security and protection estimations for cloud computing cannot be straightforwardly applied to the fog computing because of its portability and heterogeneity. So these issues in fog computing arises new research challenges and opportunities. This survey features about existing security concerns for fog computing and new proposed system to tackle some of the issues in fog computing related to security and privacy, thereby enhancing the cloud security

Resource identification in fog-to-cloud systems: toward an identity management strategy

og-to-Cloud (F2C) is a novel paradigm aiming at extending the cloud computing capabilities to the edge of the network through the hierarchical and coordinated management of both, centralized cloud datacenters and distributed fog resources. It will allow all kinds of devices that are capable to connect to the F2C network to share its idle resources and access both, service provider and third parties’ resources to expand its own capabilities. However, despite the numerous advantages offered by the F2C model, such as the possibility of offloading delay-sensitive tasks to a nearby device and using the cloud infrastructure in the execution of resource-intensive tasks, the list of open challenges that needs to be addressed to have a deployable F2C system is pretty long. In this paper we focus on the resource identification challenge, proposing an identity management system (IDMS) solution that starts assigning identifiers (IDs) to the devices in the F2C network in a decentralized fashion using hashes and afterwards, manages the usage of those IDs applying a fragmentation technique. The obtained results during the validation phase show that our proposal not only meets the desired IDMS characteristics, but also that the fragmentation strategy is aligned with the constrained nature of the devices in the lowest tier of the network hierarchy.Peer ReviewedPostprint (author's final draft

IWQoS 2017

Producción CientíficaThe promises of SDN and NFV technologies to boost innovation and to reduce the time-to-market of new services is changing the way in which residential networks will be deployed, managed and maintained in the near future. New user-centric management models for residential networks combining SDN-based residential gateways and cloud technologies have already been proposed, providing flexibility and ease of deployment. Extending the scope of SDN technologies to optical access networks and bringing cloud technologies to the edge of the network enable the creation of advanced residential networks in which complex service function chains can be established to provide traffic differentiation. In this context, this paper defines a novel network management model based on a user-centric approach that allows residential users to define and control access network resources and the dynamic provision of traffic differentiation to fulfill QoS requirements.Ministerio de Economía, Industria y Competitividad (context of GREDOS project TEC2015 -67834- R, TEC2014-53071- C3 -2P and Elastic Networks TEC2015-71932- REDT

Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

Fog computing is an emerging paradigm that aims to improve the efficiency and QoS of cloud computing by extending the cloud to the edge of the network. This paper develops a comprehensive energy efficiency analysis framework based on mathematical modeling and heuristics to study the offloading of virtual machine (VM) services from the cloud to the fog. The analysis addresses the impact of different factors including the traffic between the VM and its users, the VM workload, the workload versus number of users profile and the proximity of fog nodes to users. Overall, the power consumption can be reduced if the VM users’ traffic is high and/or the VMs have a linear power profile. In such a linear profile case, the creation of multiple VM replicas does not increase the computing power consumption significantly (there may be a slight increase due to idle / baseline power consumption) if the number of users remains constant, however the VM replicas can be brought closer to the end users, thus reducing the transport network power consumption. In our scenario, the optimum placement of VMs over a cloud-fog architecture significantly decreased the total power consumption by 56% and 64% under high user data rates compared to optimized distributed clouds placement and placement in the existing AT&T network cloud locations, respectively

On the Analysis of Non-euclidean data: Sparsification, Classification and Generation

In light of the groundbreaking achievements of convolutional neural networks (CNNs) in 2D image processing, there has been a pronounced interest in adapting these methods to non-Euclidean data, such as graphs and 3D geometric data. Point clouds, in particular, present unique challenges as they are sparse, unordered, and locality-sensitive, making the adaptation of CNNs to point cloud processing a non-trivial task. Similar challenges are encountered in the context of graph data. Consequently, the exploration of extending successful neural processing paradigms from 2D images to these non-Euclidean domains has emerged as a vibrant and dynamic research area. This thesis focuses on advancing graph neural networks (GNNs) and analyzing 3D point clouds, emphasizing sparsification, classification and generation. For graph neural networks, a significant contribution is the introduction of Sparse Graph Attention Networks (SGAT), integrating a sparse attention mechanism into graph attention networks (GATs) through $L_0$-norm regularization. SGAT excels in edge removal (50\%-80\% on large graphs), enhancing interpretability without compromising performance on assortative graphs and improving it on disassortative graphs. In 3D point cloud analysis, an autoregressive approach, APSNet, formulates task-oriented point cloud sampling as a sequential generation process, and develops an attention-based point cloud sampling network that optimally samples 8 points out of 1024, tailoring the process for tasks like 3D point cloud classification, reconstruction, and registration. Extending into a non-autoregressive method, PTSNet, a point transformer, utilizes a transformer-based dynamic query generator. This innovation enables PTSNet to capture long-range correlations, mitigating issues like gradient vanishing and reducing duplicate samples compared to LSTM-based methods. Lastly, the thesis proposes GDPNet, first hybrid Generative and Discriminative PointNet, extending the Joint Energy-based Model (JEM) for point cloud generation and classification. GDPNet retains strong discriminative power of modern PointNet classifiers, while generating point cloud samples rivaling state-of-the-art generative approaches