COSCO: container orchestration using co-simulation and gradient based optimization for fog computing environments

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms

Dynamic Assignment Control of a Closed Queueing Network under Complete Resource Pooling

We study the design of dynamic assignment control in networks with a fixed number of circulating resources (supply units). Each time a demand arises, the controller has (limited) flexibility in choosing the node from which to assign a supply unit. If no supply units are available at any compatible node, the demand is lost. If the demand is served, this causes to the supply unit to relocate to the "destination" of the demand. We study how to minimize the proportion of lost requests in steady state (or over a finite horizon) via a large deviations analysis. We propose a family of simple state-dependent policies called Scaled MaxWeight (SMW) policies that dynamically manage the distribution of supply in the network. We prove that under a complete resource pooling condition (analogous to the condition in Hall's marriage theorem), any SMW policy leads to exponential decay of demand-loss probability as the number of supply units scales to infinity. Further, there is an SMW policy that achieves the $\textbf{optimal}$ loss exponent among all assignment policies, and we analytically specify this policy in terms of the demand arrival rates for all origin-destination pairs. The optimal SMW policy maintains high supply levels adjacent to structurally under-supplied nodes. We discuss two applications: (i) Shared transportation platforms (like ride-hailing and bikesharing): We incorporate travel delays in our model and show that SMW policies for assignment control continue to have exponentially small loss. Simulations of ride-hailing based on the NYC taxi dataset demonstrate excellent performance. (ii) Service provider selection in scrip systems (like for babysitting or for kidney exchange): With only cosmetic modifications to the setup, our results translate fully to a model of scrip systems and lead to strong performance guarantees for a "Scaled Minimum Scrip" service provider selection rule

Stability of spatial wireless systems with random admissible-set scheduling

We examine the stability of wireless networks whose users are distributed over a compact space. Users arrive at spatially uniform locations with intensity \lambda and each user has a random number of packets to transmit with mean \beta. In each time slot, an admissible subset of users is selected uniformly at random to transmit one packet. A subset of users is called admissible when their simultaneous activity obeys the prevailing interference constraints. We consider a wide class of interference constraints, including the SINR model and the protocol model. Denote by \mu the maximum number of users in an admissible subset for the model under consideration. We will show that the necessary condition \lamba \beta <\mu is also sufficient for random admissible-set scheduling to achieve stability. Thus random admissible-set scheduling achieves stability, if feasible to do so at all, for a broad class of interference scenarios. The proof relies on a description of the system as a measure-valued process and the identi??cation of a Lyapunov function. Keywords: Wireless networks, stability, Foster-Lyapunov, Harris recurrent, measure-valued process, interference constraints, SINR requirements, protocol mode

AI augmented Edge and Fog computing: trends and challenges

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems

Opportunistic device-to-device communication in cellular networks: from theory to practice

Mención Internacional en el título de doctorCellular service providers have been struggling with users’ demand since the emergence of mobile Internet. As a result, each generation of cellular network prevailed over its predecessors mainly in terms of connection speed. However, the fifth generation (5G) of cellular network promises to go beyond this trend by revolutionizing the network architecture. Device-to-Device (D2D) communication is one of the revolutionary changes that enables mobile users to communicate directly without traversing a base station. This feature is being actively studied in 3GPP with special focus on public safety as it allows mobiles to operate in adhoc mode. Although under the (partial) control of the network, D2D communications open the door to many other use-cases. This dissertation studies different aspects of D2D communications and its impact on the key performance indicators of the network. We design an architecture for the collaboration of cellular users by means of timely exploited D2D opportunities. We begin by presenting the analytical study on opportunistic outband D2D communications. The study reveals the great potential of opportunistic outband D2D communications for enhancing energy efficiency, fairness, and capacity of cellular networks when groups of D2D users can be form and managed in the cellular network. Then we introduce a protocol that is compatible with the latest release of IEEE and 3GPP standards and allows for implementation of our proposal in a today’s cellular network. To validate our analytical findings, we use our experimental Software Defined Radio (SDR)-based testbed to further study our proposal in a real world scenario. The experimental results confirm the outstanding potential of opportunistic outband D2D communications. Finally, we investigate the performance merits and disadvantages of different D2D “modes”. Our investigation reveals, despite the common belief, that all D2D modes are complementary and their merits are scenario based.This work has been supported by IMDEA Networks Institute.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Douglas Leith.- Secretario: Albert Banchs Roca.- Vocal: Carla Fabiana Chiasserin

Scaling limit of a limit order book model via the regenerative characterization of L\'evy trees

We consider the following Markovian dynamic on point processes: at constant rate and with equal probability, either the rightmost atom of the current configuration is removed, or a new atom is added at a random distance from the rightmost atom. Interpreting atoms as limit buy orders, this process was introduced by Lakner et al. to model a one-sided limit order book. We consider this model in the regime where the total number of orders converges to a reflected Brownian motion, and complement the results of Lakner et al. by showing that, in the case where the mean displacement at which a new order is added is positive, the measure-valued process describing the whole limit order book converges to a simple functional of this reflected Brownian motion. Our results make it possible to derive useful and explicit approximations on various quantities of interest such as the depth or the total value of the book. Our approach leverages an unexpected connection with L\'evy trees. More precisely, the cornerstone of our approach is the regenerative characterization of L\'evy trees due to Weill, which provides an elegant proof strategy which we unfold.Comment: Accepted for publication in stochastic system