Modeling and Optimization of Performance and Cost of Serverless Applications

Lin, Changyuan; Khazaei, Hamzeh

doi:10.1109/tpds.2020.3028841

Cited by 94 publications

(62 citation statements)

References 24 publications

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“…These performance and cost models enable application developers to plan and budget their deployments in the most cost efective manner. Lin et al propose an analytical model to predict response time and cost for worklow execution in serverless platforms [81]. They consider variations in response time and cost with the allocated memory in building these models.…”

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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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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“…In addition, using YAWL to analyze the properties of the model requires constructing the model with YAWL and using the YAWL engine [18]. In [19], the serverless application workflow is defined as a weighted directed graph. The performance and cost of the serverless application can be predicted through the analysis of the workflow model.…”

Section: Related Workmentioning

confidence: 99%

“…Reachability graph is a main tool to analyse the property of Petri net [19]. Each point is a marking in the reachability graph.…”

Section: The Soundness Of the System Frameworkmentioning

confidence: 99%

DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

Wang

Pang

Ding

et al. 2022

IEEE Trans. Ind. Inf.

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“…In this way, a value such as execution time or cost of a conditional branch is calculated as an expected value. Lin et al [21] used this method to model performance and the cost of a workflow that contained branches.…”

Section: Conditional Branchmentioning

confidence: 99%

“…Many optimization methods for complex workflows have been proposed in recent studies. In [21], authors define common patterns for workflows (i.e., self-loop, cycle, parallel, branch) as probabilistic direct acyclic graphs to model various types of workflows. Their method optimizes the performance of the cost of a workflow using a greedy heuristic that adjusts memory configuration based on the critical path.…”

Section: Workflows Management and Optimizations For Serverless Computingmentioning

confidence: 99%

Mitigating Cold Start Problem in Serverless Computing with Function Fusion

Lee

Yoon

Yeo

et al. 2021

Sensors

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[sangyoon]As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution[sangyoon] that is encountered due to resource provisioning. [sangyoon]In serverless computing, functions can be composed as workflows to process a complex task, and the cold start problem has a significant influence on workflow response time because the cold start can occur in each function.The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.

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Modeling and Optimization of Performance and Cost of Serverless Applications

Cited by 94 publications

References 24 publications

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

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

DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

Mitigating Cold Start Problem in Serverless Computing with Function Fusion

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