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

Amazon Web Services (AWS) offers transient virtual servers at a discounted price as a way to sell unused spare capacity in its data centers. Although transient servers are very appealing as some instances have up to 90% discount, they are not bound to regular availability guarantees as they are opportunistic resources sold on the spot market. In this paper, we present SPA, a framework that remarkably increases the spot instance reliability over time due to insights gained from the analysis of historical data, such as cross-region price variability and intervals between evictions. We implemented the SPA reliability strategy, evaluated them using over one year of historical pricing data from AWS, and found out that we can increase the transient instance lifetime by adding a pricing overhead of 3.5% in the spot price in the best scenario.

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Cloud computing has seen continuous growth over the last decade. The recent rise in popularity of next-generation applications brings forth the question: "Can current cloud infrastructure support the low latency requirements of such apps?" Specifically, the interplay of wireless last-mile and investments of cloud operators in setting up direct peering agreements with ISPs globally to current cloud reachability and latency has remained largely unexplored.This paper investigates the state of end-user to cloud connectivity over wireless media through extensive measurements over six months. We leverage 115,000 wireless probes on the Speed-checker platform and 195 cloud regions from 9 well-established cloud providers. We evaluate the suitability of current cloud infrastructure to meet the needs of emerging applications and highlight various hindering pressure points. We also compare our results to a previous study over RIPE Atlas. Our key findings are: (i) the most impact on latency comes from the geographical distance to the datacenter; (ii) the choice of a measurement platform can significantly influence the results; (iii) wireless last-mile access contributes significantly to the overall latency, almost surpassing the impact of the geographical distance in many cases. We also observe that cloud providers with their own private network backbone and direct peering agreements with serving ISPs offer noticeable improvements in latency, especially in its consistency over longer distances.@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 aims to enable applications with stringent latency requirements, e.g., augmented reality, and tame the overwhelming data streams generated by IoT devices. A core principle of this paradigm is to bring the computation from a distant cloud closer to service consumers and data producers. Consequentially, the issue of edge computing facilities' placement arises. We present a comprehensive analysis suggesting where to place general-purpose edge computing resources on an Internet-wide scale. We base our conclusions on extensive real-world network measurements. We perform extensive traceroute measurements from RIPE Atlas to datacenters in the US, resulting in a graph of 11K routers. We identify the affiliations of the routers to determine the network providers that can act as edge providers. We devise several edge placement strategies and show that they can improve cloud access latency by up to 30%.

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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

AWS offers discounted transient virtual instances as a way to sell unused resources in their data-centers, and users can enjoy up to 90% discount as compared to the regular on-demand pricing. Despite the economic incentives to purchase these transient instances, they do not come with regular availability SLAs, meaning that they can be evicted at any moment. Hence, the user is responsible for managing the instance availability to meet the application requirements. In this paper, we present Bricklayer, a software tool that assists users to better use transient resources in the cloud, reducing costs for the same amount of resources, and increasing the overall instance availability. Bricklayer searches for possible combinations of smaller and cheaper instances to compose the requested amount of resources while deploying them into different spot markets to reduce the risk of eviction. We implemented and evaluated Bricklayer using 3 months of historical data from AWS and found out that it can reduce up 54% of the regular spot price and up to 95% compared to the standard on-demand pricing.

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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

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 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 provides an attractive platform for bringing data and processing closer to users in networked environments. Several edge proposals aim to place the edge servers at a couple hop distance from the client to ensure lowest possible compute and network delay. An attractive edge server placement is to co-locate it with existing (cellular) base stations to avoid additional infrastructure establishment costs. However, determining the exact locations for edge servers is an important question that must be resolved for optimal placement. In this paper, we present Anveshak1, a framework that solves the problem of placing edge servers in a geographical topology and provides the optimal solution for edge providers. Our proposed solution considers both end-user application requirements as well as deployment and operating costs incurred by edge platform providers. The placement optimization metric of Anveshak considers the request pattern of users and existing user-established edge servers. In our evaluation based on real datasets, we show that Anveshak achieves 67% increase in user satisfaction while maintaining high server utilization.@en

Several Internet-of-Things (IoT) based use-cases require an increased amount of computation resources with extremely low latency. Cloud-based services may fail to provide such requirement given the high latency required to access their facilities. Therefore, an increasing number of resources are being deployed at the network "edge" as Edge Clouds.However, as the modeling of edge network is still in its early stages, the existing solutions for service placement and resource utilization are found to be quite inefficient. In this paper, we model a multi-tier edge network and propose a service placement algorithm, Mute. In our evaluation, performed on real network topologies, we show that Mute achieves 660of-the-art solutions.@en

Edge clouds are an attractive platform to support latency-sensitive applications by providing computations on servers deployed close to end-users. These servers aim to employ MPTCP to leverage multiple connections including wireless over a public network. In this paper, we show that the default MPTCP design does not adequately support reliability in these environments, which makes it unfit for use in edge clouds. We propose RAMPTCP, an extension to MPTCP which focuses on adding reliability over network paths.@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

Edge clouds handle data and computations closer to its source and users. Applications like industrial automation, bring new challenges and require solutions tailored for computation-centric edge cloud networks. In this paper we build on existing edge and fog computing models and develop a solution to predict and store data in edge resource caches for upcoming computations. Our solution is based on grouping caches according to the workloads they serve. We further develop methods for populating the caches and ensuring the coherence of the cached data. We evaluate the performance of our grouping mechanisms and show that they bring significant performance gains, both in terms of network traffic and access latency.@en