Resource sharing in network slicing and human-machine interactions

In this thesis we explore two novel resource allocation models. The first addresses challenges associated with dynamic sharing of network resources by multiple tenants/services via network slicing. The second focuses on a data-driven approach to the optimization of resource allocation in interactive human-machine processes. In our first thrust we investigate how to allocate shared storage, computation, and/or connectivity resources distributed amongst multiple tenants/ virtual service providers which have dynamic loads. It is expected that next generation of wireless network will be shared by an increasing number of data-intensive mobile applications (e.g., autonomous cars, IoT, interactive 360° video streaming), and tenants/service providers. A key functional requirement for such infrastructure is enabling efficient sharing of heterogeneous resource among tenants/service providers supporting spatially varying and dynamic user demands, both from the point of view of enabling the deployment and performance management to diverse service providers and/or tenants, as well as means to increase utilization and reduce CAPEX/OPEX associated with deploying possible new infrastructures. To that end, we propose a novel dynamic resource sharing policy, namely, Share Constrained Proportional Fair (SCPF), which allocates a predefined ‘share’ of a pool of (distributed) resources to each slice. We provide a characterization of the achievable performance gains over General Processor Sharing (GPS), and Static Slicing (SS), i.e., fixed allocation of resources to slices. We also characterize the associated share dimensioning problem, asking when a particular set of load profiles and QoS requirements are feasible, as well as what should be an appropriate pricing strategy. We further consider possible slice-based admission control scheme where slices engage in an underlying game to maximize their carried loads subject to performance requirements. In order to accommodate settings where one would wish to provision different types of resources which are coupled through user demands, we generalize SCPF to a more general resource allocation criterion, namely, Share Constrained Slicing (SCS), which extends traditional α—fairness criterion, by striking a balance among inter- and intra-slice fairness vs. overall efficiency. We show that SCS has several desirable properties including slice-level protection, envyfreeness, and load-driven elasticity. In practice, mobile users' dynamics could make the cost of implementing SCS high, so we also study the feasibility of using a dynamically weighted max-min fair policy as a surrogate resource allocation scheme. For a setting with stochastic loads and elastic user requirements, we model the user dynamics under SCS as a queuing network and establish the stability condition. Finally, and perhaps surprisingly, we show via extensive simulation that while SCS (and/or the surrogate weighted max-min allocation) provides inter-slice protection, they can also achieve improved job delay and/or perceived throughput, as compared to other weighted max-min based allocation schemes whose intra-slice weight allocation is not share-constrained, e.g., traditional max-min and/or discriminatory processor sharing. In our second thrust we study how to optimize resource allocation in the context of human-machine interactions. Examples of such processes could include systems aimed at assisting humans in interactive learning, workload allocation, or web-search advertising. We devise an innovative framework to enable the optimization of a reward over an interactive process in a data-driven manner. This is a challenging problem for several reasons: (1) humans' behavior is not easily modeled and may reflect biases, memory and be sensitive to sequencing, all of which should/could be inferred from data; (2) because these interactions are typically sequential and transient, inferring such complex models for human behavior is difficult; (3) furthermore, in order to collect data on human-machine interactions one must choose a machine policy which in turn may bias inferences on human behavior. In this thesis we approach the problem of jointly estimating human behavior and optimizing machine policies via Alternating Entropy-Reward Ascent (AREA) algorithm. We characterize AREA in terms of its space and time complexity and convergence. We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-makingElectrical and Computer Engineerin

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines

Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing (HPC), and machine learning (ML) training and scoring—become increasingly common in practice. Interestingly, systems of these areas share many compilation and runtime techniques, and the used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource management, data formats and representations, as well as execution strategies differ substantially. DAPHNE is an open and extensible system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for increasing productivity and eliminating unnecessary overheads. In this paper, we make a case for IDA pipelines, describe the overall DAPHNE system architecture, its key components, and the design of a vectorized execution engine for computational storage, HW accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas, DuckDB, and TensorFlow show promising results

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will

Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates