Fine-Grained Performance and Cost Modeling and Optimization for FaaS Applications

Lin, Changyuan; Mahmoudi, Nima; Fan, Caixiang; Khazaei, Hamzeh

doi:10.1109/tpds.2022.3214783

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…Throughout the entire DDD process, the domain models and technology models are tightly integrated to ensure their clarity, completeness, and strong consistency throughout the software development lifecycle [50]. These inherent characteristics of DDD align well with the principles of microservice architecture, making DDD a key determinant of success in microservice applications [51].…”

Section: Domain-driven Designmentioning

confidence: 98%

A Microservice and Serverless Architecture for Secure IoT System

Ouyang

Wang

et al. 2023

Sensors

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In cross-border transactions, the transmission and processing of logistics information directly affect the trading experience and efficiency. The use of Internet of Things (IoT) technology can make this process more intelligent, efficient, and secure. However, most traditional IoT logistics systems are provided by a single logistics company. These independent systems need to withstand high computing loads and network bandwidth when processing large-scale data. Additionally, due to the complex network environment of cross-border transactions, the platform’s information security and system security are difficult to guarantee. To address these challenges, this paper designs and implements an intelligent cross-border logistics system platform that combines serverless architecture and microservice technology. This system can uniformly distribute the services of all logistics companies and divide microservices based on actual business needs. It also studies and designs corresponding Application Programming Interface (API) gateways to solve the interface exposure problem of microservices, thereby ensuring the system’s security. Furthermore, asymmetric encryption technology is used in the serverless architecture to ensure the security of cross-border logistics data. The experiments show that this research solution validates the advantages of combining serverless architecture and microservices, which can significantly reduce the operating costs and system complexity of the platform in cross-border logistics scenarios. It allows for resource expansion and billing based on application program requirements at runtime. The platform can effectively improve the security of cross-border logistics service processes and meet cross-border transaction needs in terms of data security, throughput, and latency.

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Section: Domain-driven Designmentioning

confidence: 98%

A Microservice and Serverless Architecture for Secure IoT System

Ouyang

Wang

et al. 2023

Sensors

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“…For example, Eismann et al introduce Sizeless for predicting memory‐related configurations to cut client costs and improve provider resource efficiency 74 . Later, Lin et al propose formally modeling FaaS applications to aid the implementation of an accurate cost model 73 …”

Section: Related Workmentioning

confidence: 99%

“…Some simulators were implemented exclusively for evaluating a new adaptation technique. 73 Similarly, particular simulations aim at near-identical outcomes as the ground truth for specific system components. For example, Eismann et al…”

Section: Specialized High Fidelity Simulatorsmentioning

confidence: 99%

“…74 Later, Lin et al propose formally modeling FaaS applications to aid the implementation of an accurate cost model. 73 The downside is that specialized simulators are costly and more dependent on their intended platforms and applications. Worse, in work where the simulator is not the contribution and is only a necessary side product, it may not generalize well outside its intended scope.…”

Section: Specialized High Fidelity Simulatorsmentioning

confidence: 99%

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faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms

Raith,

Rausch,

Furutanpey

et al. 2023

Softw Pract Exp

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This paper presents faas‐sim, a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim. By running experiments on both real‐world test beds and replicating them using faas‐sim, we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems.

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“…4 To our knowledge, only a few studies have been proposed to tackle the abovementioned challenges. [5][6][7] For example, Lin and Khazaei 5 proposed an analytical model to predict the average response time and cost of a workflow, and devised a probability refined critical path (PRCP) algorithm with greedy strategies for optimization. However, there are following shortcomings in these studies: (1) the analytical model is very complex and built on the basis of some strong assumptions (e.g., in a cycle structure, a larger iteration number leads to a less execution probability); (2) the optimization algorithm is implemented based on greedy strategies, which may work well on simple problems but underperform on those with huge search space.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic modeling and evolutionary optimization approach for serverless workflow configuration

Wang,

Wu,

Zhang

et al. 2023

Softw Pract Exp

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Serverless computing has nowadays become a mainstream paradigm to develop cloud‐native applications owing to its high scalability, ease of usage and cost‐effectiveness. Nevertheless, because of its poor infrastructure transparency, two main challenges emerge when users migrate their applications to a serverless platform: the lack of an effective analytical model for performance and billing, and the trade‐off problem between them. In this paper, we formally define a serverless workflow and introduce the concept of execution instances. Based on them, a probabilistic performance and cost evaluation model is built to obtain their expected values for an input serverless workflow. Then, we design a tailored evolutionary optimization algorithm called EASW to tackle budget‐constrained performance optimization and performance‐constrained cost optimization problems. Extensive experiments were carried out to test the proposed model and optimization algorithm on AWS Lambda. Results reveal that our model can achieve an accuracy over 98% and EASW can yield a better memory configuration solution than existing methods for constrained optimization.

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Fine-Grained Performance and Cost Modeling and Optimization for FaaS Applications

Cited by 12 publications

References 35 publications

A Microservice and Serverless Architecture for Secure IoT System

A Microservice and Serverless Architecture for Secure IoT System

faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms

A probabilistic modeling and evolutionary optimization approach for serverless workflow configuration

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