Limitations of Load Balancing Mechanisms for N-Tier Systems in the Presence of Millibottlenecks

Zhu, Tao; Li, Jack; Kimball, Josh; Park, Jun‐Hee; Lai, Chien-An; Pu, Calton; Wang, Qingyang

doi:10.1109/icdcs.2017.188

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Microservices complicate cluster management, because dependencies between tiers can introduce backpessure effects, leading to system-wide hotspots [56,59,82,85,87]. Backpressure can additionally trick the cluster manager into penalizing or upsizing a saturated microservice, even though its saturation is the result of backpressure from another, potentially not-saturated service.…”

Section: Cluster Management Implicationsmentioning

confidence: 99%

An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems

Gan

Zhang

Cheng

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

379

215

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show abstract

Section: Cluster Management Implicationsmentioning

confidence: 99%

An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems

Gan

Zhang

Cheng

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

379

215

View full text Add to dashboard Cite

show abstract

“…Microservices complicate cluster management, because dependencies between tiers can introduce backpessure effects, leading to system-wide hotspots [57,59,83,86,88]. Backpressure can additionally trick the cluster manager into penalizing or upsizing a saturated microservice, even though its saturation is the result of backpressure from another, potentially not-saturated service.…”

Section: Cluster Management Implicationsmentioning

confidence: 99%

An Open-Source Benchmark Suite for Cloud and IoT Microservices

Yu¹,

Zhang²,

Cheng³

et al. 2019

Preprint

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Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and utilization.In this paper we explore the implications microservices have across the cloud system stack. We first present Death-StarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to cluster management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability.

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Systematic Construction, Execution, and Reproduction of Complex Performance Benchmarks

Lima

Kimball

Ferreira

et al. 2019

Cloud Computing – CLOUD 2019

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Limitations of Load Balancing Mechanisms for N-Tier Systems in the Presence of Millibottlenecks

Cited by 10 publications

References 16 publications

An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems

An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems

An Open-Source Benchmark Suite for Cloud and IoT Microservices

Systematic Construction, Execution, and Reproduction of Complex Performance Benchmarks

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