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1. Edge-Adaptable Serverless Acceleration for Machine Learning IoT Applications

   https://doi.org/DOI 10.1002/spe.2944  

   Zhang, M; Krintz, C.; Wolski, R. (December 2020, Software practice experience)

   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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2. Supporting IoT Applications with Serverless Edge Clouds

   https://doi.org/10.1109/CloudNet51028.2020.9335805  

   Wang, I.; Liri, E.; Ramakrishnan, K. K. (November 2020, 2020 IEEE 9th International Conference on Cloud Networking (CloudNet))

   null (Ed.)

   Cloud computing has grown because of lowered costs due to economies of scale and multiplexing. Serverless computing exploits multiplexing in cloud computing however, for low latency required by IoT applications, the cloud should be moved nearer to the IoT device and the cold start problem should be addressed. Using a real-world dataset, we showed through implementation in an open-source cloud environment based on Knative that a serverless approach to manage IoT traffic is feasible, uses less resources than a serverfull approach and traffic prediction with prefetching can mitigate the cold start delay penalty. However applying the Knative framework directly to IoT traffic without considering the execution context gives unnecessary overhead. 

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3. Tracking Causal Order in AWS Lambda Applications

   https://doi.org/10.1109/IC2E.2018.00027  

   Lin, Wei-Tsung; Krintz, Chandra; Wolski, Rich; Zhang, Michael; Cai, Xiaogang; Li, Tongjun; Xu, Weijin (April 2018, IEEE International Conference on Cloud Engineering (IC2E))

   Serverless computing is a new cloud programming and deployment paradigm that is receiving wide-spread uptake. Serverless offerings such as Amazon Web Services (AWS) Lambda, Google Functions, and Azure Functions automatically execute simple functions uploaded by developers, in response to cloud-based event triggers. The serverless abstraction greatly simplifies integration of concurrency and parallelism into cloud applications, and enables deployment of scalable distributed systems and services at very low cost. Although a significant first step, the serverless abstraction requires tools that software engineers can use to reason about, debug, and optimize their increasingly complex, asynchronous applications. Toward this end, we investigate the design and implementation of GammaRay, a cloud service that extracts causal dependencies across functions and through cloud services, without programmer intervention. We implement GammaRay for AWS Lambda and evaluate the overheads that it introduces for serverless micro-benchmarks and applications written in Python. 

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4. STOIC: Serverless TeleOperable Hybrid Cloud for Machine Learning Applications on Edge Device

   Zhang, M; Krintz, C; Wolski, R. (March 2020, International Workshop on Smart Edge Computing and Networking)

   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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5. Deserv: Decentralized Serverless Computing

   Christie, Samuel H.; Chopra, Amit K.; Singh, Munindar P. (January 2021, Proceedings of the 19th IEEE International Conference on Web Services)

   null (Ed.)

   A decentralized application involves multiple autonomous principals, e.g., humans and organizations. Autonomy motivates (1) specifying a decentralized application via a protocol that captures the interactions between the principals, and (2) a programming model that enables each principal to independently (from other principals) construct its own protocol-compliant agent. An agent encodes its principal's decision making and represents it in the application. We contribute Deserv, the first protocol-based programming model for decentralized applications that is suited to the cloud. Specifically, Deserv demonstrates how to leverage function-as-a-service (FaaS), a popular serverless programming model, to implement agents. A notable feature of Deserv is the use declarative protocols to specify interactions. Declarative protocols support implementing stateful agents in a manner that naturally exploits the concurrency and autoscaling benefits offered by serverless computing. 

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