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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%.
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Edge-to-cloud Virtualized Cyberinfrastructure for Near Real-time Water Quality Forecasting in Lakes and Reservoirs
The management of drinking water quality is critical to public health and can benefit from techniques and technologies that support near real-time forecasting of lake and reservoir conditions. The cyberinfrastructure (CI) needed to support forecasting has to overcome multiple challenges, which include: 1) deploying sensors at the reservoir requires the CI to extend to the network’s edge and accommodate devices with constrained network and power; 2) different lakes need different sensor modalities, deployments, and calibrations; hence, the CI needs to be flexible and customizable to accommodate various deployments; and 3) the CI requires to be accessible and usable to various stakeholders (water managers, reservoir operators, and researchers) without barriers to entry. This paper describes the CI underlying FLARE (Forecasting Lake And Reservoir Ecosystems), a novel system co-designed in an interdisciplinary manner between CI and domain scientists to address the above challenges. FLARE integrates R packages that implement the core numerical forecasting (including lake process modeling and data assimilation) with containers, overlay virtual networks, object storage, versioned storage, and event-driven Function-as-a-Service (FaaS) serverless execution. It is a flexible forecasting system that can be deployed in different modalities, including the Manual Mode suitable for end-users’ personal computers and the Workflow Mode ideal for cloud deployment. The paper reports on experimental data and lessons learned from the operational deployment of FLARE in a drinking water supply (Falling Creek Reservoir in Vinton, Virginia, USA). Experiments with a FLARE deployment quantify its edge-to-cloud virtual network performance and serverless execution in OpenWhisk deployments on both XSEDE-Jetstream and the IBM Cloud Functions FaaS system.
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- PAR ID:
- 10304372
- Date Published:
- Journal Name:
- 2021 IEEE 17th International Conference on eScience (eScience)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Serverless computing is a promising new event- driven programming model that was designed by cloud vendors to expedite the development and deployment of scalable web services on cloud computing systems. Using the model, developers write applications that consist of simple, independent, stateless functions that the cloud invokes on-demand (i.e. elastically), in response to system-wide events (data arrival, messages, web requests, etc.). In this work, we present STOIC (Serverless TeleOperable HybrId Cloud), an application scheduling and deployment system that extends the serverless model in two ways. First, it uses the model in a distributed setting and schedules application functions across multiple cloud systems. Second, STOIC sup- ports serverless function execution using hardware acceleration (e.g. GPU resources) when available from the underlying cloud system. We overview the design and implementation of STOIC and empirically evaluate it using real-world machine learning applications and multi-tier (e.g. edge-cloud) deployments. We find that STOIC’s combined use of edge and cloud resources is able to outperform using either cloud in isolation for the applications and datasets that we consider.more » « less
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Serverless computing is a rapidly growing cloud application model, popularized by Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function's execution time, branch mispredictions per kilo-instruction are 20x higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profiling platform that we developed for this work.more » « less
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Abstract Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real‐time iterative water temperature forecasting system (FLARE—Forecasting Lake And Reservoir Ecosystems). FLARE is composed of water temperature and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble‐based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475‐day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean square error (RMSE) of daily forecasted water temperatures was 1.13°C at the reservoir's near‐surface (1.0 m) for 7‐day ahead forecasts and 1.62°C for 16‐day ahead forecasts. The RMSE of forecasted water temperatures at the near‐sediments (8.0 m) was 0.87°C for 7‐day forecasts and 1.20°C for 16‐day forecasts. FLARE successfully predicted the onset of fall turnover 4–14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near‐sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open‐source system for lake and reservoir water quality forecasting to improve real‐time management.more » « less
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null (Ed.)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.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/eScience51609.2021.00024
