HotEdge '19 Workshop Program

As smart home environments get more complex and denser, they are becoming harder to manage. We present our ongoing work on the design and implementation of ``SafeHome'', a system for management and coordination inside a smart home. SafeHome offers users and programmers the flexibility to specify safety properties in a declarative way, and to specify routines of commands in an imperative way. SafeHome includes mechanisms which ensure that under concurrent routines and device failures, the smart home behavior is consistent (e.g., serializable) and safety properties are always guaranteed. SafeHome is intended to run on edge machines co-located with the smart home. Our design space opens the opportunity to borrow and adapt rich ideas and mechanisms from related areas such as databases and compilers.

Deploying machine learning on edge devices is becoming increasingly important, driven by new applications such as smart homes, smart cities, and autonomous vehicles. Unfortunately, it is challenging to deploy deep neural networks (DNNs) on resource-constrained devices. These workloads are computationally intensive and often require cloud-like resources. Prior solutions attempted to address these challenges by either sacrificing accuracy or by relying on cloud resources for assistance.

In this paper, we propose a containerized partition-based runtime adaptive convolutional neural network (CNN) acceleration framework for Internet of Things (IoT) environments. The framework leverages spatial partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. By containerizing each partition, we simplify the model update and deployment with Docker and Kubernetes to efficiently handle runtime resource management and scheduling of containers.

Prior research has proposed using peer-to-peer (P2P) content delivery to serve Internet video at lower costs. Yet, such methods have not witnessed widespread adoption. An important challenge is incentivization: what tangible benefits does P2P content delivery offer users who bring resources to the table? In this paper, we ask whether monetary incentives can help attract peers in P2P content delivery systems. We first propose Gringotts, a system to enable secure monetary incentives for P2P content delivery systems. Gringotts provides a novel Proof of Delivery mechanism that allows content providers to verify correct delivery of their files, and shows how to use cryptocurrency to pay peers while guarding against liars and Sybil attacks. We then present results from an 876-person professional survey we commissioned to understand users’ willingness to participate in Gringotts, and what challenges remain. Our survey revealed that 51% would participate for suitable financial incentives, and motivated the need for alternate payment forms, device security, and peer anonymity.

The rise of edge computing as a new storage and compute model has already motivated numerous studies within the systems community, focusing on the choices and mechanisms of task offloading from end devices to the edge infrastructure, pricing, consistency, indexing and caching. However, it is not yet entirely clear how the edge infrastructure itself will be deployed, and, more importantly, managed. A common point of view considers the edge as an extension of traditional content distribution networks (CDN), due to its hierarchical layout, centralized ownership, and cloud back-end.

In this paper, we consider a different view of the edge, as a "reincarnation" of the well-known peer-to-peer (P2P) model. We show how the edge is similar to P2P systems in many aspects, including the number, heterogeneity and limited availability and resources of its nodes, their central role in performing the system's storage and computation, and the vulnerabilities related to tight interoperability with user end devices. We describe the similarities of the edge to both CDNs and P2P systems, the challenges that arise from these similarities, and the previous approaches to address them in both contexts. We show that the challenges that remain in applying these approaches may be addressed by viewing the edge as a larger and smarter reincarnation of P2P systems.

Edge computing and the Internet of Things (IoT) are irrevocably intertwined and much work has proposed enhancing the IoT through the use of edge computing. These solutions have typically focused on using the edge to increase the locality of cloud applications, achieving benefits mainly in terms of lower network latency. In this work, we argue that IoT systems can benefit much more from semantic properties which are best recognized and exploited in situ, at the edge of the network where the data streams and actuators exist. We outline the idea of a real-time semantic operating system, hosted on the edge, which can provide higher performance in energy consumption, latency, accuracy, and improve not only individual application performance but that of an entire IoT infrastructure. We have implemented a prototype system and show some initial results demonstrating the efficacy of our proposed optimizations, and provide insights into how to handle some of the most critical issues faced in such a system.

High demand for low latency services and local data processing has given rise for edge computing. As opposed to cloud computing, in this new paradigm computational facilities are located close to the end-users and data producers, on the edge of the network, hence the name. The critical issue for the proliferation of edge computing is the availability of local computational resources. Major cloud providers are already addressing the problem by establishing facilities in the proximity of end-users. However, there is an alternative trend, namely, developing open infrastructure as a set of standards, technologies, and practices to enable any motivated parties to offer their computational capacity for the needs of edge computing. Open infrastructure can give an additional boost to this new promising paradigm and, moreover, help to avoid problems for which cloud computing has been long criticized for, such as vendor lock-in or privacy. In this paper, we discuss the challenges related to creating such an open infrastructure, in particular focusing on the applicability of distributed ledgers for contractual agreement and payment. Solving the challenge of contracting is central to realizing an open infrastructure for edge computing, and in this paper, we highlight the potential and shortcomings of distributed ledger technologies in the context of our use case.

The increase in privacy concerns among the users has led to edge based analytics applications such as federated learning which trains machine learning models in an iterative and collaborative fashion on the edge devices without sending the raw private data to the central cloud. In this paper, we propose a system for enabling iterative collaborative processing (ICP) in resource constrained edge environments. We first identify the unique systems challenges posed by ICP, which are not addressed by the existing distributed machine learning frameworks such as the parameter server. We then propose the system components necessary for ICP to work well in highly distributed edge environments. Based on this, we propose a system design for enabling such applications over the edge. We show the benefits of our proposed system components with a preliminary evaluation.

Emerging edge applications, such as augmented and virtual reality, real-time video analytics and thin-client gaming, are latency-sensitive, resource-intensive, and stateful. Transitioning these applications from cloud deployments to the edge is non-trivial since edge deployments will exhibit variable resource availability, significant user mobility, and high potential for faults and application preemption, requiring considerable developer effort per application to maintain stable quality of experience for the user.

In this paper, we propose deterministic containers, a new abstraction that simplifies the development of complex applications on the edge. Deterministic containers enforce the property that all activity within a container behave deterministically. Determinism provides replication, which in turn provides key benefits for edge computing including resilience to performance jitter, enhanced fault-tolerance, seamless migration, and data provenance.

We are currently building a prototype, Shadow, that aims to provide deterministic containers with minimal performance overhead while requiring few application modifications. For all sources of non-determinism, Shadow either converts the behavior to be deterministic or restricts the allowable application behavior. Preliminary results indicate that using Shadow to reduce performance jitter at the edge for a vehicle caravan application involving video analytics reduces median application response time by up to 25%.

User-generated video content is imposing an increasing burden on live video service architectures such as Facebook Live. These services are responsible for ingesting large amounts of video, transcoding that video into different quality levels (i.e., bitrates), and adaptively streaming it to viewers. These tasks are expensive, both computationally and network-wise, often forcing service providers to sacrifice the “liveness” of delivered video. Given the steady increases in smartphone bandwidth and energy resources, coupled with the fact that commodity smartphones now include hardware-accelerated codecs, we propose that live video services augment their existing infrastructure with edge support for transcoding and transmission. We present measurements to motivate the feasibility of incorporating such edge-support into the live video ecosystem, present the design of a peer-to-peer adaptive live video streaming system, and discuss directions for future work to realize this vision in practice.

Manufacturer Usage Description (MUD) is a proposed IETF standard enabling local area networks (LAN) to automatically configure their access control when adding a new IoT device based on the recommendations provided for that device by the manufacturer. MUD has been proposed as an isolation-based defensive mechanism with a focus on devices in the home, where there is no dedicated network administrator. In this paper, we describe the efficacy of MUD for a generic IoT device under different threat scenarios in the context of the Fog. We propose a method to use rate limiting to prevent end devices from participating in denial of service attacks (DDoS), including against the Fog itself. We illustrate our assumptions by providing a possible real world example and describe the benefits for MUD in the Fog for various stakeholders.

IoT applications are starting to rely heavily on edge computing due to the advent of low-power and high data-rate wireless communication technologies such as 5G and the processing capability of GPU-driven edge platforms. However, the computation and the data communication model for the edge computing applications are quite diverse, which limits their interoperability. An interoperable edge computing architecture with a versatile communication model would lead to the development of innovative and incentive-driven edge computing applications by combining various data sources from a wide array of devices. In this paper, we present an edge computing architecture by extending the publish-subscribe protocol with support for incentives. Our novel publish-pay-subscribe protocol enables the data producers (publishers) to sell their data with data consumers and service providers (subscribers). The proposed architecture not only allows the device owners to gain incentive but also enable the service providers to sell the processed data with one or more data consumers. Our proof-of-concept implementation using AEDES publish-subscribe broker and Ethereum cryptocurrency shows the feasibility of publish-pay-subscribe broker and its support for data-driven and incentive-based edge computing applications.