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%.
A Programmable and Reliable Publish/Subscribe System for Multi-Tier IoT
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.
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- International Conference on Internet of Things: Systems, Management and Security
- Page Range or eLocation-ID:
- 1 to 8
- 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.
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