A fault-tolerance shim for serverless computing

Sreekanti, Vikram; Wu, Chenggang; Chhatrapati, Saurav; Gonzalez, Joseph E.; Hellerstein, Joseph M.; Faleiro, Jose M.

doi:10.1145/3342195.3387535

Cited by 42 publications

(26 citation statements)

References 24 publications

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“…For example, a platform would automatically resend the request in case of failure of a function execution. Sreekanti et al show that under certain circumstances, a retry based approach to fault tolerance may always not give accurate results, specially where state management is involved via external storage services [124]. They emphasize that the accuracy may be afected in case a parallel execution of a function, views results of a partially executed failed attempt of the same execution.…”

Section: Qos Goalmentioning

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: Qos Goalmentioning

confidence: 99%

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

2022

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“…The component m i requires a stationary resource r j if MOB(m i ) = 0, and accepts any mobile device if MOB(m i ) = 1. The application owner can define higher MOB (m i ) values if it implements fault tolerance recovery mechanisms, such as alternative execution strategies [33]. This is essential, as the mobility prediction model does not implement any recovery strategy in case of incorrect prediction.…”

Section: Problem Definitionmentioning

confidence: 99%

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Kimovski

Mehran

Kerth

et al. 2022

IEEE Trans. Serv. Comput.

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The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities. Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices' mobility and resource constraints. To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud -Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives. mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network. We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications. Compared to related work, mMAPO reduces the economic cost by 28% and decreases the completion time by 80% while maintaining a stable energy consumption.

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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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A fault-tolerance shim for serverless computing

Cited by 42 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

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Kappa

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