Prebaking Functions to Warm the Serverless Cold Start

Silva, Paulo; Fireman, Daniel; Pereira, Thiago Emmanuel

doi:10.1145/3423211.3425682

Cited by 61 publications

(15 citation statements)

References 22 publications

(22 reference statements)

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“…Even local deployments of serverless results in service time degradation due to cold starts [9,42]. Overall, prior works confirm the severity of cold start time and keep-alive cost [16,24,50,59,62].…”

Section: Related Workmentioning

confidence: 84%

IceBreaker: warming serverless functions better with heterogeneity

Roy

Patel

Tiwari

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Serverless computing, an emerging computing model, relies on łwarming upž functions prior to its anticipated execution for faster and cost-effective service to users. Unfortunately, warming up functions can be inaccurate and incur prohibitively expensive cost during the warmup period (i.e., keep-alive cost). In this paper, we introduce IceBreaker, a novel technique that reduces the service time and the łkeep-alivež cost by composing a system with heterogeneous nodes (costly and cheaper). IceBreaker does so by dynamically determining the cost-effective node type to warm up a function based on the function's time-varying probability of the next invocation. By employing heterogeneity, IceBreaker allows for more number of nodes under the same cost budget and hence, keeps more number of functions warm and reduces the wait time during high load. Our real-system evaluation confirms that IceBreaker reduces the overall keep-alive cost by 45% and execution time by 27% using representative serverless applications and industry-grade workload trace. IceBreaker is the first technique to employ and leverage the idea of mixing expensive and cheaper nodes to improve both service time and keep-alive cost for serverless functions ś opening up a new research avenue of serverless computing on heterogeneous servers for researchers and practitioners. CCS CONCEPTS• Computer systems organization → Cloud computing; ntier architectures; • General and reference → Performance.

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Section: Related Workmentioning

confidence: 84%

IceBreaker: warming serverless functions better with heterogeneity

Roy

Patel

Tiwari

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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“…This is done by using the concept of pause containers, which are network-ready empty containers which could be attached to other containers. Similarly in [119], Silva et al try to reduce the container startup time by implementing a checkpoint mechanism for restoring snapshots from recently created function processes. Stein et al propose a non-cooperative resource allocation heuristic for serverless environments which aims to predict the number of function instances required to be kept in order to maintain request waiting time below a threshold level [126].…”

Section: Elements Of Resource Managementmentioning

confidence: 99%

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

2022

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Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to automate the entire process of resource provisioning, allocation, scheduling, monitoring and scaling, has resulted in the need for specialized focus on resource management under the serverless model. In this article, we identify the major aspects covering the broader concept of resource management in serverless environments and propose a taxonomy of elements which influence these aspects, encompassing characteristics of system design, workload attributes and stakeholder expectations. We take a holistic view on serverless environments deployed across edge, fog and cloud computing networks. We also analyse existing works discussing aspects of serverless resource management using this taxonomy. This article further identifies gaps in literature and highlights future research directions for improving capabilities of this computing model.

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“…Many FaaS platform implement this mechanism, for example, Amazon AWS Lambda offers the provisioned concurrency feature to keep a number of containers initialized ready to execute lambda functions with minimum delay; similarly, Microsoft Azure offers the Premium Plan, which allow users to have their code pre-warmed on a specified number of instances; Apache OpenWhisk also includes the possibility for users to pre-warm a given number of containers. There are other research proposals for mitigating the cold start problem, for example the prebaking functions proposed in [28] that implements a mechanism that restores snapshots of previously created functions processes, or the reinforcement learning approach proposed in [29], which analyzes some factors, such as function CPU utilization, to determine the functioninvocation patterns and reduce the function cold start frequency by preparing the function instances in advance.…”

Section: State Of the Artmentioning

confidence: 99%

Latency And Resource Consumption Analysis For Serverless Edge Analytics

Moreno-Vozmediano

Huedo

Montero

et al. 2022

Preprint

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The serverless computing model, implemented by Function as a Service (FaaS) platforms, can offer several advantages for the deployment of data analytics solutions in IoT environments, such as agile and on-demand resource provisioning, automatic scaling, high elasticity, infrastructure management abstraction, and a fine-grained cost model. Nonetheless, in case of applications with strict latency requirements, the cold start problem in FaaS platforms can represent an important drawback. The most common techniques to alleviate this problem, mainly based on instance pre-warming and instance reusing mechanisms, are usually not well adapted to different application profiles and, in general, can entail an extra expense of resources. In this work, we analyze the effect of instance pre-warming and instance reusing on both, application latency (response time) and resource consumption, for a typical data analytics use case (a machine learning application for image classification) with different input data patterns. Furthermore, we propose to extend the classical centralized cloud-based serverless FaaS platform to a two-tier distributed edge-cloud platform to bring the platform closer to the data source and reduce network latencies.

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Prebaking Functions to Warm the Serverless Cold Start

Cited by 61 publications

References 22 publications

IceBreaker: warming serverless functions better with heterogeneity

IceBreaker: warming serverless functions better with heterogeneity

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

Latency And Resource Consumption Analysis For Serverless Edge Analytics

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