FaaSDeliver: Cost-Efficient and QoS-Aware Function Delivery in Computing Continuum

Yu, Guangba; Chen, Pengfei; Zheng, Zibin; Zhang, Jingrun; Li, X.H.; He, Zilong

doi:10.1109/tsc.2023.3274769

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…The framework employs three optimization algorithms, including linear, binary, and gradient descent, to find the minimum cost. FaaSDeliver [14] considers the heterogeneity of serverless platforms and makes separate decisions for memory and CPU. It selects the most cost-efficient function delivery policy for different functions and computing devices based on the Treestructured Parzen Estimator (TPE).…”

Section: Related Workmentioning

confidence: 99%

“…The search for the optimal configuration scheme for functions has been proven to be an NP-hard problem [14]. Although traditional Particle Swarm Optimization (PSO) methods have been extensively applied to tackle continuous optimization problems [30], the decision variables are discrete in the problem studied in this paper.…”

Section: Configuration Scheme Based On Apso-gamentioning

confidence: 99%

“…The second category involves determining the optimal memory size for a serverless function through a combination of sparse measurements and interpolation/modeling techniques. This category of solutions first executes functions with the given sampling point configurations selected through traversal [12], Bayesian optimization [13], TPE [14], etc. Next, it aggregates execution data and constructs a performance model to identify an appropriate resource configuration scheme.…”

Section: Introductionmentioning

confidence: 99%

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FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Li,

Zhang,

Lin

et al. 2023

Sensors

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The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.

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Section: Related Workmentioning

confidence: 99%

Section: Configuration Scheme Based On Apso-gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Li,

Zhang,

Lin

et al. 2023

Sensors

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Self-Provisioning Infrastructures for the Next Generation Serverless Computing

Nastic

2024

SN COMPUT. SCI.

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Serverless computing has ushered in a transformative paradigm, with a promise to alleviate developers from the intricacies of infrastructure management. However, current serverless platforms predominantly offer only serverless compute capabilities. As a consequence, the application developers are once again tasked to explicitly provision and manage the backend services (BaaS), such as object stores or API gateways, the infrastructure, and the configuration models. This violates the main promise of serverless computing and erases much of the practical benefits of the serverless paradigm. It also introduces the challenges of managing the application execution environment, which includes maintaining provisioning and deployment scripts, configuring and managing access permissions, and scaling the services during runtime. To address these challenges, in this paper we introduce a novel paradigm for the next generation of serverless computing, called self-provisioning infrastructure. The self-provisioning infrastructure is an infrastructure that is capable to automatically and autonomously (with zero-configuration and zero-touch) provision serverless functions, their infrastructure, and their supporting BaaS services. To achieve this vision, we introduce novel design principles, models, and mechanisms that are formalized via novel programming, function, and system models. With this novel paradigm, we intend to fortify the core design principles of serverless computing but also extend them to the entire application execution environment. By doing so, we aim to enable the next-generation serverless computing in the Edge-Cloud continuum.

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Machine Learning to Predict Risk Management Applications Performance

De Giorgi,

Ardagna

2024

2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

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FaaSDeliver: Cost-Efficient and QoS-Aware Function Delivery in Computing Continuum

Cited by 5 publications

References 47 publications

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Self-Provisioning Infrastructures for the Next Generation Serverless Computing

Machine Learning to Predict Risk Management Applications Performance

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