μ Suite: A Benchmark Suite for Microservices

Sriraman, Akshitha; Wenisch, Thomas F.

doi:10.1109/iiswc.2018.8573515

Cited by 66 publications

(45 citation statements)

References 61 publications

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Section: Related Workmentioning

confidence: 99%

“…The emergence of microservices has prompted recent work to study their characteristics and requirements [55,78,79,86]. µSuite for example quantifies the system call, context switch, and other OS overheads in microservices [78], while Ueda et al [79] show the impact of compute resource allocation, application framework, and container configuration on the performance and scalability of several microservices. DeathstarBench differentiates from these studies by focusing on large-scale applications with tens of unique microservices, allowing us to study effects that only emerge at large scale, such as network contention and cascading QoS violations due to dependencies between tiers, as well as by including diverse applications that span social networks, media and e-commerce services, and applications running on swarms of edge devices.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

380

215

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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

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

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

380

215

View full text Add to dashboard Cite

show abstract

“…Aderaldo et al presented the essential characteristics to be possessed by a microservice application to be considered as a benchmark application . μ Suite‐Benchmark Suite provides researchers with a collection of services to analyze the performance impacts on multitiered microservices. DeathStarBench aims to assist researchers in evaluating the performance of microservice‐based applications specific for cloud and edge environments. Performance anomaly detection: In constantly changing environments of the microservices architectures, performance monitoring can be a challenging task.…”

Section: Taxonomy Based On Different Aspects Of Msasmentioning

confidence: 99%

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Joseph

Chandrasekaran

2019

Softw Pract Exp

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Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices-based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain.

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“…Many applications that exhibit a high fan-out pattern use KV stores as caches [13]. These applications can issue tens to hundreds of sub-requests to satisfy a single user request [10,11,19,36,45,57,65]. The slowest response time to these sub-requests determines the overall response time.…”

Section: Elastic Kv Store Requirementsmentioning

confidence: 99%

Peafowl

Asyabi

Bestavros

Sharafzadeh

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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The traffic load sent to key-value (KV) stores varies over long timescales of hours to short timescales of a few microseconds. Long-term variations present the opportunity to save power during low or medium periods of utilization. Several techniques exist to save power in servers, including feedback-based controllers that right-size the number of allocated CPU cores, dynamic voltage and frequency scaling (DVFS), and c-state (idle-state) mechanisms. In this paper, we demonstrate that existing power saving techniques are not effective for KV stores. This is because the high rate of traffic even under low load prevents the system from entering low power states for extended periods of time. To achieve power savings, we must unbalance the load among the CPU cores so that some of them can enter low power states during periods of low load. We accomplish this by introducing the notion of in-application CPU scheduling. Instead of relying on the kernel to schedule threads, we pin threads to bypass the kernel CPU scheduler and then perform the scheduling within the KV store application. Our design, Peafowl, is a KV store that features an in-application CPU scheduler that monitors the load to learn the workload characteristics and then scales the number of active CPU cores when the load drops, leading to notable power savings during low or medium periods of utilization. Our experiments demonstrate that Peafowl uses up to 40-54% lower power than state of the art approaches such as Rubik and DPM.

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μ Suite: A Benchmark Suite for Microservices

Cited by 66 publications

References 61 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

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Peafowl

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