6 research outputs found
Designing Reconfigurable Intelligent Systems with Markov Blankets
Compute Continuum (CC) systems comprise a vast number of devices distributed
over computational tiers. Evaluating business requirements, i.e., Service Level
Objectives (SLOs), requires collecting data from all those devices; if SLOs are
violated, devices must be reconfigured to ensure correct operation. If done
centrally, this dramatically increases the number of devices and variables that
must be considered, while creating an enormous communication overhead. To
address this, we (1) introduce a causality filter based on Markov blankets (MB)
that limits the number of variables that each device must track, (2) evaluate
SLOs decentralized on a device basis, and (3) infer optimal device
configuration for fulfilling SLOs. We evaluated our methodology by analyzing
video stream transformations and providing device configurations that ensure
the Quality of Service (QoS). The devices thus perceived their environment and
acted accordingly -- a form of decentralized intelligence
Active Inference on the Edge: A Design Study
Machine Learning (ML) is a common tool to interpret and predict the behavior
of distributed computing systems, e.g., to optimize the task distribution
between devices. As more and more data is created by Internet of Things (IoT)
devices, data processing and ML training are carried out by edge devices in
close proximity. To ensure Quality of Service (QoS) throughout these
operations, systems are supervised and dynamically adapted with the help of ML.
However, as long as ML models are not retrained, they fail to capture gradual
shifts in the variable distribution, leading to an inaccurate view of the
system state. Moreover, as the prediction accuracy decreases, the reporting
device should actively resolve uncertainties to improve the model's precision.
Such a level of self-determination could be provided by Active Inference (ACI)
-- a concept from neuroscience that describes how the brain constantly predicts
and evaluates sensory information to decrease long-term surprise. We
encompassed these concepts in a single action-perception cycle, which we
implemented for distributed agents in a smart manufacturing use case. As a
result, we showed how our ACI agent was able to quickly and traceably solve an
optimization problem while fulfilling QoS requirements
Equilibrium in the Computing Continuum through Active Inference
Computing Continuum (CC) systems are challenged to ensure the intricate
requirements of each computational tier. Given the system's scale, the Service
Level Objectives (SLOs) which are expressed as these requirements, must be
broken down into smaller parts that can be decentralized. We present our
framework for collaborative edge intelligence enabling individual edge devices
to (1) develop a causal understanding of how to enforce their SLOs, and (2)
transfer knowledge to speed up the onboarding of heterogeneous devices. Through
collaboration, they (3) increase the scope of SLO fulfillment. We implemented
the framework and evaluated a use case in which a CC system is responsible for
ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video
streaming. Our results showed that edge devices required only ten training
rounds to ensure four SLOs; furthermore, the underlying causal structures were
also rationally explainable. The addition of new types of devices can be done a
posteriori, the framework allowed them to reuse existing models, even though
the device type had been unknown. Finally, rebalancing the load within a device
cluster allowed individual edge devices to recover their SLO compliance after a
network failure from 22% to 89%
Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions
Zero-touch network is anticipated to inaugurate the generation of intelligent
and highly flexible resource provisioning strategies where multiple service
providers collaboratively offer computation and storage resources. This
transformation presents substantial challenges to network administration and
service providers regarding sustainability and scalability. This article
combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning
(ZTP) for edge networks. This combination helps to manage network devices
seamlessly and intelligently by minimizing human intervention. In addition,
several advantages are also highlighted that come with incorporating
Distributed AI into ZTP in the context of edge networks. Further, we draw
potential research directions to foster novel studies in this field and
overcome the current limitations
Modern computing: vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
Development of an RFID Inventory Robot (AdvanRobot)
AdvanRobot proposes a new robot for inventorying and locating all the products inside a retail store without the need of installing any fixed infrastructure. The patent pending robot combines a laser-guided autonomous robotic base with a Radio Frequency Identification (RFID) payload composed of several RFID readers and antennas, as well as a 3D camera.
AdvanRobot is able not only to replace human operators, but to dramatically increase the efficiency and accuracy in providing inventory, while also adding the capacity to produce store maps and product location.
Some important benefit of the inventory capabilities of AdvanRobot are the reduction in stock-outs, which can cause a drop in sales and are the most important source of frustration for customers; the reduction
of the number of items per reference maximizing the number of references per square meter; and reducing the cost of capital due to over-stocking [1, 7]. Another important economic benefit expected from the inventorying and location capabilities of the robot is the ability to efficiently prepare on-line orders from the closest store to the customer,
allowing retailers to compete with the likes of Amazon (a.k.a. omnichannel retail). Additionally, the robot enables to: produce a 3D model of the store; detect misplaced items; and assist customers and staff in finding
products (wayfinding)