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Edge computing is a distributed computing paradigm that moves data-intensive applications and services (e.g., AI) closer to the data source. The rapid growth of edge endpoints connected to the Internet today poses several challenges in scalable application life cycle management. That is, managing data and workloads on several thousand, up to millions of edge endpoints, challenged by limited connectivity, resource constraints, network and edge endpoint failures. In this work, we present EdgeRDV, a new edge abstraction that builds on the idea of rendezvous nodes to manage edge workloads at scale. The EdgeRDV architecture is comprised of a central cloud management endpoint (or cloud hub), a central gateway for each edge site (or edge hub), redundant gateways (or rendezvous nodes), and edge endpoints. Beyond its scalable architecture, EdgeRDV presents new techniques and algorithms that address single points of failures and provide adjustable levels of resilience and cost-effectiveness in edge network deployments. We conducted preliminary experiments to evaluate EdgeRDV, through simulations, and our results show that EdgeRDV requires one to three orders of magnitude fewer intermediate nodes compared to relay structures, can gracefully adapt to failures, and requires a constant number of messages during failure recovery in edge sites with up to 667K+ edge endpoints. 

This work is an experience with a deployed networked system for digital agriculture (or DA). Digital agriculture is the use of data-driven techniques towards a sustainable increase in farm productivity and efficiency. DA systems are expected to be overlaid on existing rural infrastructures, which are known to be less robust. While existing DA approaches partially address several infrastructure issues, challenges related to data aggregation, data analytics, and fault tolerance remain open. In this work, we present the design of Comosum, an extensible, reconfigurable, and fault-tolerant architecture of hardware, software, and distributed cloud abstractions to sense, analyze, and actuate on different farm types. FarmBIOS is an implementation of the Comosum architecture. We analyze FarmBIOS by leveraging various applications, deployment experiences, and network differences between urban and rural farms. This includes, for instance, an edge analytics application achieving 86% accuracy in vineyard disease detection. An eighteen-month deployment of FarmBIOS highlights Comosum’s fault tolerance. It was fault tolerant to intermittent network outages that lasted for several days during many periods of the deployment. We introduce active digital twins to cope with the unreliability of the underlying base systems. 

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity. To increase farm efficiency and understand the tradeoffs of these new DA innovations we developed ROAM, which is a decision support system developed to find a Pareto optimal architectural design to build DA systems. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined metrics and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and to visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability. 

Rural infrastructure is known to be more prone to breakdown than urban infrastructure. This paper explores how the fragility of rural infrastructure is reproduced through the process of engineering design. Building on values in design, we examine how eventual use is anticipated by engineering researchers building on emerging infrastructure for digital agriculture (DA). Our approach combines critically reflective technical systems-building with interviews with other practitioners to understand and address moments early in the design process where the eventual effects of DA systems may be being built-in. Our findings contrast researchers’ visions of seamless farming technologies with the seamful realities of their work to produce them. We trace how, when anticipating future use, the seams that researchers themselves experience disappear, other seams are hidden from view by institutional support, and seams end users may face are too distant to be in sight. We develop suggestions for the design of these technologies grounded in a more artful management of seamfulness and seamlessness during the process of design and development. 

Oblivious routing has a long history in both the theory and practice of networking. In this work we initiate the formal study of oblivious routing in the context of reconfigurable networks, a new architecture that has recently come to the fore in datacenter networking. These networks allow a rapidly changing bounded-degree pattern of interconnections between nodes, but the network topology and the selection of routing paths must both be oblivious to the traffic demand matrix. Our focus is on the trade-off between maximizing throughput and minimizing latency in these networks. For every constant throughput rate, we characterize (up to a constant factor) the minimum latency achievable by an oblivious reconfigurable network design that satisfies the given throughput guarantee. The trade-off between these two objectives turns out to be surprisingly subtle: the curve depicting it has an unexpected scalloped shape reflecting the fact that load-balancing becomes more difficult when the average length of routing paths is not an integer because equalizing all the path lengths is not possible. The proof of our lower bound uses LP duality to verify that Valiant load balancing is the most efficient oblivious routing scheme when used in combination with an optimally-designed reconfigurable network topology. The proof of our upper bound uses an algebraic construction in which the network nodes are identified with vectors over a finite field, the network topology is described by either the elementary basis or a sequence of Vandermonde matrices, and routing paths are constructed by selecting columns of these matrices to yield the appropriate mixture of path lengths within the shortest possible time interval. 

Infrastructure-as-a-Service cloud providers sell virtual machines that are only specified in terms of number of CPU cores, amount of memory, and I/O throughput. Performance-critical aspects such as cache sizes and memory latency are missing or reported in ways that make them hard to compare across cloud providers. It is difficult for users to adapt their application’s behavior to the available resources. In this work, we aim to increase the visibility that cloud users have into shared resources on public clouds. Specifically, we present CacheInspector , a lightweight runtime that determines the performance and allocated capacity of shared caches on multi-tenant public clouds. We validate CacheInspector ’s accuracy in a controlled environment, and use it to study the characteristics and variability of cache resources in the cloud, across time, instances, availability regions, and cloud providers. We show that CacheInspector ’s output allows cloud users to tailor their application’s behavior, including their output quality, to avoid suboptimal performance when resources are scarce.