Edge Offloading in Smart Grid

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.Comment: to be submitted to journa

Virtual Service Provisioning for Internet of Things Applications in Mobile Edge Computing

The Internet of Things (IoT) paradigm is paving the way for many new emerging technologies, such as smart grid, industry 4.0, connected cars, smart cities, etc. Mobile Edge Computing (MEC) provides promising solutions to reduce service delays for delay-sensitive IoT applications, where cloudlets are co-located with wireless access points in the proximity of IoT devices. Most mobile users have specified Service Function Chain (SFC) requirements, where an SFC is a sequence of Virtual Network Functions (VNFs). Meanwhile, edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed on the local IoT device of the request; and the other part is processed on a cloudlet (server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet in which the request is assigned. In this thesis, we will focus on virtual service provisioning for IoT applications in MEC Environments. Firstly, we consider the user satisfaction problem of using services jointly provided by an MEC network and a remote cloud for delay-sensitive IoT applications, through maximizing the accumulative user satisfaction when different user services have different service delay requirements. A novel metric to measure user satisfaction of using a service is proposed, and efficient approximation and online algorithms for the defined problems under both static and dynamic user service demands are then devised and analyzed. Secondly, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements with the aim of minimizing service provisioning cost, where each IoT application has multiple data streams from different sources to be uploaded to the MEC network for processing and storage, while each data stream must pass through the network functions of the SFC of the IoT application, prior to reaching its destination. A service provisioning framework for such multi-source IoT applications is proposed, through uploading stream data from multiple IoT sources, VNF instance placement and sharing, in-network aggregation of data streams, and workload balancing among cloudlets. Efficient algorithms for service provisioning of multi-source IoT applications in MEC networks, built upon the proposed framework, are also proposed. Thirdly, we investigate a novel DNN inference throughput maximization problem in an MEC network with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and inference parallelism. We devise a constant approximation algorithm for the problem under the offline setting, and an online algorithm with a provable competitive ratio for the problem under the online setting, respectively. Fourthly, we address a robust SFC placement problem with the aim to maximize the expected profit collected by the service provider of an MEC network, under the assumption of both computing resource and data rate demand uncertainties. We start with a special case of the problem where the measurement of the expected demanded resources for each request admission is accurate, under which we propose a near-optimal approximation algorithm for the problem by adopting the Markov approximation technique, which can achieve a provable optimality gap. Then, we extend the proposed approach to the problem of concern, for which we show that the proposed algorithm still is applicable, and the solution delivered has a moderate optimality gap with bounded perturbation errors on the profit measurement. Finally, we summarize the thesis work and explore several potential research topics that are based on the studies in this thesis

Quality of Experience monitoring and management strategies for future smart networks

One of the major driving forces of the service and network's provider market is the user's perceived service quality and expectations, which are referred to as user's Quality of Experience (QoE). It is evident that QoE is particularly critical for network providers, who are challenged with the multimedia engineering problems (e.g. processing, compression) typical of traditional networks. They need to have the right QoE monitoring and management mechanisms to have a significant impact on their budget (e.g. by reducing the users‘ churn). Moreover, due to the rapid growth of mobile networks and multimedia services, it is crucial for Internet Service Providers (ISPs) to accurately monitor and manage the QoE for the delivered services and at the same time keep the computational resources and the power consumption at low levels. The objective of this thesis is to investigate the issue of QoE monitoring and management for future networks. This research, developed during the PhD programme, aims to describe the State-of-the-Art and the concept of Virtual Probes (vProbes). Then, I proposed a QoE monitoring and management solution, two Agent-based solutions for QoE monitoring in LTE-Advanced networks, a QoE monitoring solution for multimedia services in 5G networks and an SDN-based approach for QoE management of multimedia services

Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm

Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities

Edge computing infrastructure for 5G networks: a placement optimization solution

This thesis focuses on how to optimize the placement of the Edge Computing infrastructure for upcoming 5G networks. To this aim, the core contributions of this research are twofold: 1) a novel heuristic called Hybrid Simulated Annealing to tackle the NP-hard nature of the problem and, 2) a framework called EdgeON providing a practical tool for real-life deployment optimization. In more detail, Edge Computing has grown into a key solution to 5G latency, reliability and scalability requirements. By bringing computing, storage and networking resources to the edge of the network, delay-sensitive applications, location-aware systems and upcoming real-time services leverage the benefits of a reduced physical and logical path between the end-user and the data or service host. Nevertheless, the edge node placement problem raises critical concerns regarding deployment and operational expenditures (i.e., mainly due to the number of nodes to be deployed), current backhaul network capabilities and non-technical placement limitations. Common approaches to the placement of edge nodes are based on: Mobile Edge Computing (MEC), where the processing capabilities are deployed at the Radio Access Network nodes and Facility Location Problem variations, where a simplistic cost function is used to determine where to optimally place the infrastructure. However, these methods typically lack the flexibility to be used for edge node placement under the strict technical requirements identified for 5G networks. They fail to place resources at the network edge for 5G ultra-dense networking environments in a network-aware manner. This doctoral thesis focuses on rigorously defining the Edge Node Placement Problem (ENPP) for 5G use cases and proposes a novel framework called EdgeON aiming at reducing the overall expenses when deploying and operating an Edge Computing network, taking into account the usage and characteristics of the in-place backhaul network and the strict requirements of a 5G-EC ecosystem. The developed framework implements several placement and optimization strategies thoroughly assessing its suitability to solve the network-aware ENPP. The core of the framework is an in-house developed heuristic called Hybrid Simulated Annealing (HSA), seeking to address the high complexity of the ENPP while avoiding the non-convergent behavior of other traditional heuristics (i.e., when applied to similar problems). The findings of this work validate our approach to solve the network-aware ENPP, the effectiveness of the heuristic proposed and the overall applicability of EdgeON. Thorough performance evaluations were conducted on the core placement solutions implemented revealing the superiority of HSA when compared to widely used heuristics and common edge placement approaches (i.e., a MEC-based strategy). Furthermore, the practicality of EdgeON was tested through two main case studies placing services and virtual network functions over the previously optimally placed edge nodes. Overall, our proposal is an easy-to-use, effective and fully extensible tool that can be used by operators seeking to optimize the placement of computing, storage and networking infrastructure at the users’ vicinity. Therefore, our main contributions not only set strong foundations towards a cost-effective deployment and operation of an Edge Computing network, but directly impact the feasibility of upcoming 5G services/use cases and the extensive existing research regarding the placement of services and even network service chains at the edge