Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.@en

Not all research leads to fruitful results; trying new ways or methods may surpass state of the art, but sometimes the hypothesis is not proven, the improvement is insignificant, or the system fails because of a design error done years ago in previous works. In a systems discipline like pervasive computing, there are many sources of errors, from hardware issues over communication channels to heterogeneous software environments. However, failure to succeed is not a failure to progress. It is essential to create platforms for sharing insights, experiences, and lessons learned when conducting research in pervasive computing so that the same mistakes are not repeated. And sometimes, a problem is a symptom of discovering new research challenges. Based on the collective input of the First International Workshop on Negative Results in Pervasive Computing (PerFail 2022), co-located with the 20th International Conference on Pervasive Computing and Communications (PerCom 2022), this article presents a comprehensive discussion on perspectives on publishing negative results, useful failures, and lessons learned in pervasive computing.@en

In the early days of cloud computing, datacenters were sparsely deployed at distant locations far from end-users with high end-to-end communication latency. However, today’s cloud datacenters have become more geographically spread, the bandwidth of the networks keeps increasing, pushing the end-users latency down. In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. We leverage the well-known measurement platform RIPE Atlas, involving up to 8500 probes deployed in heterogeneous environments, e.g., home and offices. Our goal is to evaluate the suitability of modern cloud environments for various current and predicted applications. We achieve this by comparing our latency measurements against known human perception thresholds and are able to draw inferences on the suitability of current clouds for novel applications, such as augmented reality. Our results indicate that the current cloud coverage can easily support several latency-critical applications, like cloud gaming, for the majority of the world’s population.@en

Edge computing promises to bring computation close to the end-users to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive - there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most - at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions.@en

Edge computing has gained attention from both academia and industry by pursuing two significant challenges: 1) moving latency critical services closer to the users, 2) saving network bandwidth by aggregating large flows before sending them to the cloud. While the rationale appeared sound at its inception almost a decade ago, several current trends are impacting it. Clouds have spread geographically reducing end-user latency, mobile phones? computing capabilities are improving, and network bandwidth at the core keeps increasing. In this paper, we scrutinize edge computing, examining its outlook and future in the context of these trends. We perform extensive client-to-cloud measurements using RIPE Atlas, and show that latency reduction as motivation for edge is not as persuasive as once believed; for most applications the cloud is already 'close enough' for majority of the world's population. This implies that edge computing may only be applicable for certain application niches, as opposed to a general-purpose solution.@en

The sharing economy has made great inroads with services like Uber or Airbnb enabling people to share their unused resources with those needing them. The computing world, however, despite its abundance of excess computational resources has remained largely unaffected by this trend, save for few examples like SETI@home. We present DeCloud, a decentralized market framework bringing the sharing economy to on-demand computing where the offering of pay-as-you-go services will not be limited to large companies, but ad hoc clouds can be spontaneously formed on the edge of the network. We design incentive compatible double auction mechanism targeted specifically for distributed ledger trust model instead of relying on third-party auctioneer. DeCloud incorporates innovative matching heuristic capable of coping with the level of heterogeneity inherent for large-scale open systems. Evaluating DeCloud on Google cluster-usage data, we demonstrate that the system has a near-optimal performance from an economic point of view, additionally enhanced by the flexibility of matching.

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Edge computing (EC) extends the centralized cloud computing paradigm by bringing computation into close proximity to the end-users, to the edge of the network, and is a key enabler for applications requiring low latency such as augmented reality or content delivery. To make EC pervasive, the following challenges must be tackled: how to satisfy the growing demand for edge computing facilities, how to discover the nearby edge servers, and how to securely access them? In this paper, we present ExEC, an open framework where edge providers can offer their capacity and be discovered by application providers and end-users. ExEC aims at the unification of interaction between edge and cloud providers so that cloud providers can utilize services of third-party edge providers, and any willing entity can easily become an edge provider. In ExEC, the unfolding of initially cloud-deployed application towards edge happens without administrative intervention, since ExEC discovers available edge providers on the fly and monitors incoming end-user traffic, determining the near-optimal placement of edge services. ExEC is a set of loosely coupled components and common practices, allowing for custom implementations needed to embrace the diverse needs of specific EC scenarios. ExEC leverages only existing protocols and requires no modifications to the deployed infrastructure. Using real-world topology data and experiments on cloud platforms, we demonstrate the feasibility of ExEC and present results on its expected performance.@en

The Internet is largely a self-organizing system that adapts to changes in its operating environment. In this work, we extend these principles to service infrastructure and introduce ICON, standing for intelligent container. Technically, ICON is a container encapsulating a service that is consumed either directly by end-clients or other services. The novelty of ICON is in the ability of containers to adapt to their environment, targeting near-optimal service delivery and requiring only high-level guidance from the application management. Once deployed, containers form an overlay, observe their setting, and migrate or replicate themselves as needed, to the locations e.g., closest to service consumers. ICON captures our long-term vision for self-organizing service overlays that have the potential for global outreach. Bringing intelligence and adaptation to the level of individual containers renders a decentralized solution that has desirable properties, such as scalability, resilience, reliability, and adaptability to volatile environments. We hope that technology like ICON can open the way for more democratized service provisioning, disintermediating service providers from centralized brokers and optimizing orchestrators.@en