Addressing Application Latency Requirements through Edge Scheduling

Abstract Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications

Sl-EDGE: Network Slicing at the Edge

Network slicing of multi-access edge computing (MEC) resources is expected to be a pivotal technology to the success of 5G networks and beyond. The key challenge that sets MEC slicing apart from traditional resource allocation problems is that edge nodes depend on tightly-intertwined and strictly-constrained networking, computation and storage resources. Therefore, instantiating MEC slices without incurring in resource over-provisioning is hardly addressable with existing slicing algorithms. The main innovation of this paper is Sl-EDGE, a unified MEC slicing framework that allows network operators to instantiate heterogeneous slice services (e.g., video streaming, caching, 5G network access) on edge devices. We first describe the architecture and operations of Sl-EDGE, and then show that the problem of optimally instantiating joint network-MEC slices is NP-hard. Thus, we propose near-optimal algorithms that leverage key similarities among edge nodes and resource virtualization to instantiate heterogeneous slices 7.5x faster and within 0.25 of the optimum. We first assess the performance of our algorithms through extensive numerical analysis, and show that Sl-EDGE instantiates slices 6x more efficiently then state-of-the-art MEC slicing algorithms. Furthermore, experimental results on a 24-radio testbed with 9 smartphones demonstrate that Sl-EDGE provides at once highly-efficient slicing of joint LTE connectivity, video streaming over WiFi, and ffmpeg video transcoding

Machine Learning Algorithms for Provisioning Cloud/Edge Applications

Mención Internacional en el título de doctorReinforcement Learning (RL), in which an agent is trained to make the most favourable decisions in the long run, is an established technique in artificial intelligence. Its popularity has increased in the recent past, largely due to the development of deep neural networks spawning deep reinforcement learning algorithms such as Deep Q-Learning. The latter have been used to solve previously insurmountable problems, such as playing the famed game of “Go” that previous algorithms could not. Many such problems suffer the curse of dimensionality, in which the sheer number of possible states is so overwhelming that it is impractical to explore every possible option. While these recent techniques have been successful, they may not be strictly necessary or practical for some applications such as cloud provisioning. In these situations, the action space is not as vast and workload data required to train such systems is not as widely shared, as it is considered commercialy sensitive by the Application Service Provider (ASP). Given that provisioning decisions evolve over time in sympathy to incident workloads, they fit into the sequential decision process problem that legacy RL was designed to solve. However because of the high correlation of time series data, states are not independent of each other and the legacy Markov Decision Processes (MDPs) have to be cleverly adapted to create robust provisioning algorithms. As the first contribution of this thesis, we exploit the knowledge of both the application and configuration to create an adaptive provisioning system leveraging stationary Markov distributions. We then develop algorithms that, with neither application nor configuration knowledge, solve the underlying Markov Decision Process (MDP) to create provisioning systems. Our Q-Learning algorithms factor in the correlation between states and the consequent transitions between them to create provisioning systems that do not only adapt to workloads, but can also exploit similarities between them, thereby reducing the retraining overhead. Our algorithms also exhibit convergence in fewer learning steps given that we restructure the state and action spaces to avoid the curse of dimensionality without the need for the function approximation approach taken by deep Q-Learning systems. A crucial use-case of future networks will be the support of low-latency applications involving highly mobile users. With these in mind, the European Telecommunications Standards Institute (ETSI) has proposed the Multi-access Edge Computing (MEC) architecture, in which computing capabilities can be located close to the network edge, where the data is generated. Provisioning for such applications therefore entails migrating them to the most suitable location on the network edge as the users move. In this thesis, we also tackle this type of provisioning by considering vehicle platooning or Cooperative Adaptive Cruise Control (CACC) on the edge. We show that our Q-Learning algorithm can be adapted to minimize the number of migrations required to effectively run such an application on MEC hosts, which may also be subject to traffic from other competing applications.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Anta.- Secretario: Diego Perino.- Vocal: Ilenia Tinnirell