Serverless Computing: An Investigation of Factors Influencing Microservice Performance

Lloyd, Wes; Ramesh, Shruti; Chinthalapati, Swetha; Ly, Lan H.; Pallickara, Shrideep

doi:10.1109/ic2e.2018.00039

Cited by 225 publications

(143 citation statements)

References 17 publications

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“…We plan to develop alternatives for overcoming such limitations by exploring the use of scheduling techniques that leverage multiple clouds, and making use of data from previous invocations to predict and optimize cost as well as execution time. As has been explored in [23] and [34], FaaS tasks are also subject to invocation overhead due to latency caused by the initialization process of the container that serves the function. We plan to analyze such invocation overhead of the FaaS and CaaS services used, and explore whether options like preemptive spawning of tasks can be used to obtain more consistent performance.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Sweep

John

Ausmees

Muenzen

et al. 2019

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion

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Scientific and commercial applications are increasingly being executed in the cloud, but the difficulties associated with cluster management render on-demand resources inaccessible or inefficient to many users. Recently, the serverless execution model, in which the provisioning of resources is abstracted from the user, has gained prominence as an alternative to traditional cyberinfrastructure solutions. With its inherent elasticity, the serverless paradigm constitutes a promising computational model for scientific workflows, allowing domain specialists to develop and deploy workflows that are subject to varying workloads and intermittent usage without the overhead of infrastructure maintenance. We present the Serverless Workflow Enablement and Execution Platform (SWEEP), a cloud-agnostic workflow management system with a purely serverless execution model that allows users to define, run and monitor generic cloud-native workflows. We demonstrate the use of SWEEP on workflows from two disparate scientific domains and present an evaluation of performance and scaling.

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Section: Discussion and Future Workmentioning

confidence: 99%

Sweep

John

Ausmees

Muenzen

et al. 2019

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion

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“…latency. The works [5,12] investigate the different factors that influence the performance of AWS lambda, namely the impact of the choice of language of the function, memory footprint of the function, etc. Work [7] evaluates the performance of Fission, Kubeless and OpenFaaS serverless frameworks and characterizes the response time and the ratio of successfully completed requests for different loads.…”

Section: Related Workmentioning

confidence: 99%

Understanding Open Source Serverless Platforms

Kulkarni

Ramakrishnan

et al. 2019

Proceedings of the 5th International Workshop on Serverless Computing

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Serverless computing is increasingly popular because of the promise of lower cost and the convenience it provides to users who do not need to focus on server management. This has resulted in the availability of a number of proprietary and open-source serverless solutions. We seek to understand how the performance of serverless computing depends on a number of design issues using several popular open-source serverless platforms. We identify the idiosyncrasies affecting performance (throughput and latency) for different open-source serverless platforms. Further, we observe that just having either resource-based (CPU and memory) or workload-based (request per second (RPS) or concurrent requests) auto-scaling is inadequate to address the needs of the serverless platforms.

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“…In this paradigm, the responsibility of deployment and management is delegated to another entity, which could be the cloud infrastructure provider itself or a mediating entity. The execution platform leverages container technology to deploy and scale the prediction service components, which helps to minimize idle resource capacity [12]. These features are beneficial to the design and deployment of parallelizable deep learning prediction services.…”

Section: B Serverless Computingmentioning

confidence: 99%

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

Bhattacharjee

Chhokra

Kang

et al. 2019

2019 IEEE International Conference on Cloud Engineering (IC2E)

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Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource management decisions for these services is a hard problem due to the dynamic workloads and diverse set of available resource configurations that have various deployment and management costs. To address these challenges, we present a distributed and scalable deep-learning prediction serving system called Barista and make the following contributions. First, we present a fast and effective methodology for forecasting workloads by identifying various trends. Second, we formulate an optimization problem to minimize the total cost incurred while ensuring bounded prediction latency with reasonable accuracy. Third, we propose an efficient heuristic to identify suitable compute resource configurations. Fourth, we propose an intelligent agent to allocate and manage the compute resources by horizontal and vertical scaling to maintain the required prediction latency. Finally, using representative real-world workloads for an urban transportation service, we demonstrate and validate the capabilities of Barista.

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Serverless Computing: An Investigation of Factors Influencing Microservice Performance

Cited by 225 publications

References 17 publications

Sweep

Sweep

Understanding Open Source Serverless Platforms

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

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