Autonomic Approach based on Semantics and Checkpointing for IoT System Management

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Scheduling Tasks on Intermittently-Powered Real-Time Systems

Batteryless systems go through sporadic power on and off phases due to intermittently available energy; thus, they are called intermittent systems. Unfortunately, this intermittence in power supply hinders the timely execution of tasks and limits such devices’ potential in certain application domains, e.g., healthcare, live-stock tracking. Unlike prior work on time-aware intermittent systems that focuses on timekeeping [1, 2, 3] and discarding expired data [4], this dissertation concentrates on finishing task execution on time. I leverage the data processing and control layer of batteryless systems by developing frameworks that (1) integrate energy harvesting and real-time systems, (2) rethink machine learning algorithms for an energy-aware imprecise task scheduling framework, (3) develop scheduling algorithms that, along with deciding what to compute, answers when to compute and when to harvest, and (4) utilize distributed systems that collaboratively emulate a persistently powered system. Scheduling Framework for Intermittently Powered Computing Systems. Batteryless systems rely on sporadically available harvestable energy. For example, kinetic-powered motion detector sensors on the impalas can only harvest energy when the impalas are moving, which cannot be ascertained in advance. This uncertainty poses a unique real-time scheduling problem where existing real-time algorithms fail due to the interruption in execution time. This dissertation proposes a unified scheduling framework that includes both harvesting and computing. Imprecise Deep Neural Network Inference in Deadline-Aware Intermittent Systems. This dissertation proposes Zygarde- an energy-aware and outcome-aware soft-real-time imprecise deep neural network (DNN) task scheduling framework for intermittent systems. Zygarde leverages the semantic diversity of input data and layer-dependent expressiveness of deep features and infers only the necessary DNN layers based on available time and energy. Zygarde proposes a novel technique to determine the imprecise boundary at the runtime by exploiting the clustering classifiers and specialized offline training of the DNNs to minimize the loss of accuracy due to partial execution. It also proposes a single metric, η to represent a system’s predictability that measures how close a harvesterâs harvesting pattern is to a constant energy source. Besides, Zygarde consists of a scheduling algorithm that takes available time, available energy, impreciseness, and the classifier's performance into account. Scheduling Mutually Exclusive Computing and Harvesting Tasks in Deadline-Aware Intermittent Systems. The lack of sufficient ambient energy to directly power the intermittent systems introduces mutually exclusive computing and charging cycles of intermittently powered systems. This introduces a challenging real-time scheduling problem where the existing real-time algorithms fail due to the lack of interruption in execution time. To address this, this dissertation proposes Celebi, which considers the dynamics of the available energy and schedules when to harvest and when to compute in batteryless systems. Using data-driven simulation and real-world experiments, this dissertation shows that Celebi significantly increases the number of tasks that complete execution before their deadline when power was only available intermittently. Persistent System Emulation with Distributed Intermittent System. Intermittently-powered sensing and computing systems go through sporadic power-on and off periods due to the uncertain availability of energy sources. Despite the recent efforts to advance time-sensitive intermittent systems, such systems fail to capture important target events when the energy is absent for a prolonged time. This event miss limits the potential usage of intermittent systems in fault- intolerant and safety-critical applications. To address this problem, this dissertation proposes Falinks, a framework that allows a swarm of distributed intermittently powered nodes to collaboratively imitate the sensing and computing capabilities of a persistently powered system. This framework provides power-on and off schedules for the swamp of intermittent nodes which has no communication capability with each other.Doctor of Philosoph

SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph

Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics

Compilación de los short papers presentados en las 10mas Jornadas de Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET2022), llevadas a cabo en modalidad híbrida durante junio de 2021 y organizadas por el Instituto de Investigación en Informática LIDI (III-LIDI) y la Secretaría de Posgrado de la Facultad de Informática de la UNLP, en colaboración con universidades de Argentina y del exterior.Facultad de Informátic

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

Enabling Cyber-Physical Communication in 5G Cellular Networks: Challenges, Solutions and Applications

Cyber-physical systems (CPS) are expected to revolutionize the world through a myriad of applications in health-care, disaster event applications, environmental management, vehicular networks, industrial automation, and so on. The continuous explosive increase in wireless data traffic, driven by the global rise of smartphones, tablets, video streaming, and online social networking applications along with the anticipated wide massive sensors deployments, will create a set of challenges to network providers, especially that future fifth generation (5G) cellular networks will help facilitate the enabling of CPS communications over current network infrastructure. In this dissertation, we first provide an overview of CPS taxonomy along with its challenges from energy efficiency, security, and reliability. Then we present different tractable analytical solutions through different 5G technologies, such as device-to-device (D2D) communications, cell shrinking and offloading, in order to enable CPS traffic over cellular networks. These technologies also provide CPS with several benefits such as ubiquitous coverage, global connectivity, reliability and security. By tuning specific network parameters, the proposed solutions allow the achievement of balance and fairness in spectral efficiency and minimum achievable throughout among cellular users and CPS devices. To conclude, we present a CPS mobile-health application as a case study where security of the medical health cyber-physical space is discussed in details