Centralized Core-granular Scheduling for Serverless Functions

Kaffes, Kostis; Yadwadkar, Neeraja J.; Kozyrakis, Christos

doi:10.1145/3357223.3362709

Cited by 95 publications

(47 citation statements)

References 14 publications

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“…Sharing resources among functions and other serverless components is a challenging task. Therefore, good techniques are required to be investigated to achieve this goal [98,210,283].…”

Section: Resource Sharingmentioning

confidence: 99%

Survey on serverless computing

2021

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Serverless computing has gained importance over the last decade as an exciting new field, owing to its large influence in reducing costs, decreasing latency, improving scalability, and eliminating server-side management, to name a few. However, to date there is a lack of in-depth survey that would help developers and researchers better understand the significance of serverless computing in different contexts. Thus, it is essential to present research evidence that has been published in this area. In this systematic survey, 275 research papers that examined serverless computing from well-known literature databases were extensively reviewed to extract useful data. Then, the obtained data were analyzed to answer several research questions regarding state-of-the-art contributions of serverless computing, its concepts, its platforms, its usage, etc. We moreover discuss the challenges that serverless computing faces nowadays and how future research could enable its implementation and usage.

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“…Sharing resources among functions and other serverless components is a challenging task. Therefore, good techniques are required to be investigated to achieve this goal [98,210,283].…”

Section: Resource Sharingmentioning

confidence: 99%

Survey on serverless computing

2021

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“…Improvements to serverless platforms. Recent works have proposed serverless-optimized storage and caching solutions (e.g., Pocket [44], Savanna [29, § 3], Cloudburst [85], Aft [84], and HydroCache [97]), security enforcement via information flow control [3], and techniques to optimize the performance and resource usage of serverless platforms [1,42,49,61,65,80,82,85,94]. Kappa automatically benefits from transparent platform improvements, and can exploit new storage services by placing checkpoints and large queue elements there ( § 4).…”

Section: Other Related Workmentioning

confidence: 99%

Kappa

Zhang

Fang

Panda³

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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Serverless computing has recently emerged as a new paradigm for running software on the cloud. In this paradigm, programs need to be expressed as a set of short-lived tasks, each of which can complete within a short bounded time (e.g., 15 minutes on AWS Lambda). Serverless computing is beneficial to cloud providers-by allowing them to better utilize resources-and to users-by simplifying management and enabling greater elasticity. However, developing applications to run in this environment is challenging, requiring users to appropriately partition their code, develop new coordination mechanisms, and deal with failure recovery. In this paper, we propose Kappa, a framework that simplifies serverless development. It uses checkpointing to handle lambda function timeouts, and provides concurrency mechanisms that enable parallel computation and coordination. CCS Concepts • Computer systems organization → Cloud computing.

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“…Burst-N sends a burst of N simultaneous requests at once. Some initial studies have shown burst workloads to be common in serverless applications [40,52,56]. Continuous-N sends constant N requests per second.…”

Section: Apache Openwhisk Apachementioning

confidence: 99%

“…FnSched [96] manages invoker resources (VMs/containers that run functions) to ensure unnecessary latency is not incurred, while minimizing cost. Finally, [52] centrally schedules lambdas on CPU cores instead of servers to reduce latency. While some of the above works provide SLAs and function reordering (which Sequoia also provides), none of the works holistically study QoS to the extent our work does.…”

Section: Related Workmentioning

confidence: 99%

Sequoia

Tariq

Pahl

Nimmagadda

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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Serverless computing is a rapidly growing paradigm that easily harnesses the power of the cloud. With serverless computing, developers simply provide an event-driven function to cloud providers, and the provider seamlessly scales function invocations to meet demands as event-triggers occur. As current and future serverless oerings support a wide variety of serverless applications, eective techniques to manage serverless workloads becomes an important issue. This work examines current management and scheduling practices in cloud providers, uncovering many issues including inated application run times, function drops, inecient allocations, and other undocumented and unexpected behavior. To x these issues, a new quality-of-service function scheduling and allocation framework, called Sequoia, is designed. Sequoia allows developers or administrators to easily dene how serverless functions and applications should be deployed, capped, prioritized, or altered based on easily congured, exible policies. Results with controlled and realistic workloads show Sequoia seamlessly adapts to policies, eliminates mid-chain drops, reduces queuing times by up to 6.4⇥, enforces tight chain-level fairness, and improves run-time performance up to 25⇥.

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Centralized Core-granular Scheduling for Serverless Functions

Cited by 95 publications

References 14 publications

Survey on serverless computing

Survey on serverless computing

Kappa

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