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Data replication facilitates availability and recovery in a distributed environment. However, concurrent updates to multiple replicas result in divergence of data. Conflict-Free Replicated Data Types (CRDTs) are abstract data types that provide a principled approach to asynchronously reconcile this divergence. We propose a different perspective on the divergence of data, whereby we treat data divergences as versions of the data. That is, instead of treating it only as a problem that needs to be solved, we consider it also to be a feature that provides a way to track versioning and evolution of data. Versioning information is helpful in multiple scenarios, such as provenance tracking and system debugging. Doing so allows us to leverage concepts such as the version tree found in the literature for persistent (versioned) data structures. We show that many techniques used in CRDTs to order elements can be derived from version trees, which predates CRDTs by more than 20 years. Using version trees for maintaining order and append-only logs for storage, we propose a method to ensure convergence of arbitrary data types, while maintaining information related to the evolution of data. 

We introduce Canal, a programmable, topic-based, publish/subscribe system that is designed for multi-tier cloud deployments (e.g. edge-cloud, multi-cloud, IoT-cloud, etc.). Canal implements a triggered computational (i.e. “serverless”) programming model and provides developers with a uniform and portable programming interface. To achieve scalability and reliability, Canal combines the use of a distributed hash table (DHT) and replica consensus protocol to distribute and replicate functions, state, and data. Canal also decouples replica placement from the DHT topology to allow developers to optimize function placement for different objectives. We evaluate Canal using a real-world multi-tier IoT deployment and we use Canal to compare placement strategies, end-to-end performance, and failure recovery using both benchmarks and a real-world IoT-edge application. Our results show that Canal is able to achieve both low latency and reliability in this setting. 

Co-location of processing infrastructure and IoT devices at the edge is used to reduce response latency and long-haul network use for IoT applications. As a result, edge clouds for many applications (e.g. agriculture, ecology, and smart city deployments) must operate in remote, unattended, and environmentally harsh settings, introducing new challenges. One key challenge is heat exposure, which can degrade the performance, reliability, and longevity of electronics. For edge clouds, these problems are exacerbated because they increasingly perform complex workloads, such as machine learning, to affect data-driven actuation and control of devices and systems in the environment. The goal of our work is to protect edge clouds from overheating. To enable this, we develop a heat-budget-based scheduling system, called Sparta, which leverages dynamic voltage and frequency scaling (DVFS) to adaptively control CPU temperature. Sparta takes machine learning applications, datasets, and a temperature threshold as input. It sets the initial frequency of the CPU based on historical data and then dynamically updates it, according to the applications’ execution profile and ambient temperature, to safeguard edge devices. We find that for a suite of machine learning applications and deployment temperatures, Sparta is able to maintain CPU temperature below the threshold 94% of the time while facilitating improvements in execution time by 1.04x − 1.32x over competitive approaches. 

We present CAPLets, an authorization mechanism that extends capability based security to support fine grained access control for multi-scale (sensors, edge, cloud) IoT deployments. To enable this, CAPLets uses a strong cryptographic construction to provide integrity while preserving computational efficiency for resource constrained systems. Moreover, CAPLets augments capabilities with dynamic, user defined constraints to describe arbitrary access control policies. We introduce an application specific, turing complete virtual machine, CapVM, alongside with eBPF and Wasm, to describe constraints. We show that CAPLets is able to express permissions and requirements at a fine grain, facilitating construction of non-trivial access control policies. We empirically evaluate the efficiency and flexibility of CAPLets abstractions using resource constrained devices and end-to-end IoT deployments, and compare it against related mechanisms in wide use today. Our empirical results show that CAPLets is an order of magnitude faster and more energy efficient than current IoT authorization systems. 

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many different types of service offerings, developer productivity enhancement tools, and new application classes as well as the manifestation of cloud functionality closer to the user at the edge. The notion of utility computing, however, has remained constant throughout its evolution, which means that cloud users always seek to save costs of leasing cloud resources while maximizing their use. On the other hand, cloud providers try to maximize their profits while assuring service-level objectives of the cloud-hosted applications and keeping operational costs low. All these outcomes require systematic and sound cloud engineering principles. The aim of this paper is to highlight the importance of cloud engineering, survey the landscape of best practices in cloud engineering and its evolution, discuss many of the existing cloud engineering advances, and identify both the inherent technical challenges and research opportunities for the future of cloud computing in general and cloud engineering in particular. 

Due to the proliferation of IoT and the popularity of smart contracts mediated by blockchain, smart home systems have become capable of providing privacy and security to their occupants. In blockchain-based home automation systems, business logic is handled by smart contracts securely. However, a blockchain-based solution is inherently resource-intensive, making it unsuitable for resource-constrained IoT devices. Moreover, time-sensitive actions are complex to perform in a blockchainbased solution due to the time required to mine a block. In this work, we propose a blockchain-independent smart contract infrastructure suitable for resource-constrained IoT devices. Our proposed method is also capable of executing time-sensitive business logic. As an example of an end-to-end application, we describe a smart camera system using our proposed method, compare this system with an existing blockchain-based solution, and present an empirical evaluation of their performance. 

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting. 

Serverless computing is an emerging event-driven programming model that accelerates the development and deployment of scalable web services on cloud computing systems. Though widely integrated with the public cloud, serverless computing use is nascent for edge-based, IoT deployments. In this work, we design and develop STOIC (Serverless TeleOperable HybrId Cloud), an IoT application deployment and offloading system that extends the serverless model in three ways. First, STOIC adopts a dynamic feedback control mechanism to precisely predict latency and dispatch workloads uniformly across edge and cloud systems using a distributed serverless framework. Second, STOIC leverages hardware acceleration (e.g. GPU resources) for serverless function execution when available from the underlying cloud system. Third, STOIC can be configured in multiple ways to overcome deployment variability associated with public cloud use. Finally, we empirically evaluate STOIC using real-world machine learning applications and multi-tier IoT deployments (edge and cloud). We show that STOIC can be used for training image processing workloads (for object recognition) – once thought too resource intensive for edge deployments. We find that STOIC reduces overall execution time (response latency) and achieves placement accuracy that ranges from 92% to 97%. 

Internet of Things (IoT) devices are becoming increasingly prevalent in our environment, yet the process of programming these devices and processing the data they produce remains difficult. Typically, data is processed on device, involving arduous work in low level languages, or data is moved to the cloud, where abundant resources are available for Functions as a Service (FaaS) or other handlers. FaaS is an emerging category of flexible computing services, where developers deploy self-contained functions to be run in portable and secure containerized environments; however, at the moment, these functions are limited to running in the cloud or in some cases at the "edge" of the network using resource rich, Linux-based systems. In this work, we investigate NanoLambda, a portable platform that brings FaaS, high-level language programming, and familiar cloud service APIs to non-Linux and microcontroller-based IoT devices. To enable this, NanoLambda couples a new, minimal Python runtime system that we have designed for the least capable end of the IoT device spectrum, with API compatibility for AWS Lambda and S3. NanoLambda transfers functions between IoT devices (sensors, edge, cloud), providing power and latency savings while retaining the programmer productivity benefits of high-level languages and FaaS. A key feature of NanoLambda is a scheduler that intelligently places function executions across multi-scale IoT deployments according to resource availability and power constraints. We evaluate a range of applications that use NanoLambda to run on devices as small as the ESP8266 with 64KB of ram and 512KB flash storage.