Pigeon: A Dynamic and Efficient Serverless and FaaS Framework for Private Cloud

Wei, Ling; Ma, Lin; Tian, Chen; Hu, Ziang

doi:10.1109/csci49370.2019.00265

Cited by 27 publications

(23 citation statements)

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“…Schedulers in commercial and open-source serverless platforms also seem to be using simple load balancing mechanisms such as round robin and bin-packing approaches [131], [125]. A considerable set of works exist in literature which study the applicability of various heuristic based A Holistic View on Resource Management in Serverless Computing Environments • 15 approaches for function scheduling in serverless environments [88], [127], [82], [120]. In their work, Pinto et al explore the applicability of Bayesian Upper Conidence Bound (UCB) algorithms for deciding the optimum location for the execution of functions in an edge/cloud network [97].…”

Section: Elements Of Resource Managementmentioning

confidence: 99%

“…Apache OpenWhisk maintains pre-warmed containers and also uses load balancing strategies to direct similar function executions to the same set of workers as much as possible, thus enhancing chances of container reuse [125]. In existing research work, many models are presented to predict the arrival rates of incoming function requests and the demand for particular function executions and thereby proactively setup and maintain a pool of warm containers across VMs [126], [120], [82], [18], [121], [133], [39]. Subsequently, load balancing algorithms are devised to beneit from these existing resource pools.…”

Section: Elements Of Resource Managementmentioning

confidence: 99%

“…In addition to the above, many studies focus on achieving data locality via load balancing methods to direct new function requests to workers with pro-actively spawned containers or containers that could be re-used [120], [82]. Research works have also focused on attaining data locality among functions in terms of the runtime package dependencies of functions, by routing functions requiring similar packages to the same node for execution [8].…”

Section: Workloadmentioning

confidence: 99%

“…One main cause for high application latency in serverless environments is the cold start delay in resource setup, which tends to become signiicant compared to application execution times [88]. Scheduling algorithms along with diferent container pool management techniques, customized sandboxing methods, and low-overhead runtimes to reduce resource setup times are explored in literature to reduce cold start delay [66], [8], [120], [82], [53], [3], [93]. Latency caused by CPU contention is also studied and dynamic resource control methods are proposed for serverless applications [88], [127], [74], [63].…”

Section: Qos Goalmentioning

confidence: 99%

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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%

Section: Elements Of Resource Managementmentioning

confidence: 99%

Section: Workloadmentioning

confidence: 99%

Section: Qos Goalmentioning

confidence: 99%

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

2022

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“…This behavior is possible since both AWS Lambda and Greengrass support the same function languages and constructs. Others have instead focused on improving existing open-source serverless platforms and optimizing them for the network edge [67,77]. AI, as one popular use-case of serverless functions, has also incentivized the development of specialized platforms satisfying its requirements [70,72].…”

Section: Classification Of Existing Literaturementioning

confidence: 99%

IoT Serverless Computing at the Edge: A Systematic Mapping Review

2021

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Serverless computing is a new concept allowing developers to focus on the core functionality of their code, while abstracting away the underlying infrastructure. Even though there are existing commercial serverless cloud providers and open-source solutions, dealing with the explosive growth of new Internet of Things (IoT) devices requires more efficient bandwidth utilization, reduced latency, and data preprocessing closer to the source, thus reducing the overall data volume and meeting privacy regulations. Moving serverless computing to the edge of the network is a topic that is actively being researched with the aim of solving these issues. This study presents a systematic mapping review of current progress made to this effect, analyzing work published between 01.01.2015 and 01.09.2021. Using a document selection methodology which emphasizes the quality of the papers obtained through querying several popular databases with relevant search terms, we have included 64 entries, which we then further categorized into eight main categories. Results show that there is an increasing interest in this area with rapid progress being made to solve the remaining open issues, which have also been summarized in this paper. Special attention is paid to open-source efforts, as well as open-access contributions.

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Container lifecycle‐aware scheduling for serverless computing

Tao

Fan

et al. 2021

Softw Pract Exp

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Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers.We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead.

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Pigeon: A Dynamic and Efficient Serverless and FaaS Framework for Private Cloud

Cited by 27 publications

References 0 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

IoT Serverless Computing at the Edge: A Systematic Mapping Review

Container lifecycle‐aware scheduling for serverless computing

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