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

Gan, Yu; Zhang, Yanqi; Cheng, Dailun; Shetty, Ankitha; Rathi, Priyal; Katarki, Nayan; Bruno, Ariana; Hu, Justin; Ritchken, Brian; Jackson, Brendon; Hu, Kelvin; Pancholi, Meghna; He, Yuan; Clancy, Brett; Colen, Chris; Wen, Fukang; Leung, Catherine; Wang, Siyuan; Zaruvinsky, Leon; Espinosa, Mateo; Lin, Rick; Liu, Zhongling; Padilla, Jake; Delimitrou, Christina

doi:10.1145/3297858.3304013

Cited by 376 publications

(249 citation statements)

References 65 publications

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“…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. The observed metrics will have to be constantly analyzed to detect if there are occurrences of any unwanted events.…”

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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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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“…We use two interactive applications to evaluate deflation on real-world web workloads: Wikipedia: We replicate the German Wikipedia on our local testbed. We choose the German Wikipedia as it is the second most popular Wikipedia in terms of number of views-with more than 720000 Figure 15: The micro-service architecture of the social network application used in our evaluation (Courtesy of [17]). page views per hour-, and the fourth most in terms of number of articles-with more 2.25 Million articles [1].…”

Section: Evaluation Environmentmentioning

confidence: 99%

Cloud-scale VM-deflation for Running Interactive Applications On Transient Servers

Fuerst

Ali-Eldin

Shenoy

et al. 2020

Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing

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Transient computing has become popular in public cloud environments for running delay-insensitive batch and data processing applications at low cost. Since transient cloud servers can be revoked at any time by the cloud provider, they are considered unsuitable for running interactive application such as web services. In this paper, we present VM deflation as an alternative mechanism to server preemption for reclaiming resources from transient cloud servers under resource pressure. Using real traces from top-tier cloud providers, we show the feasibility of using VM deflation as a resource reclamation mechanism for interactive applications in public clouds. We show how current hypervisor mechanisms can be used to implement VM deflation and present cluster deflation policies for resource management of transient and on-demand cloud VMs. Experimental evaluation of our deflation system on a Linux cluster shows that microservice-based applications can be deflated by up to 50% with negligible performance overhead. Our clusterlevel deflation policies allow overcommitment levels as high as 50%, with less than a 1% decrease in application throughput, and can enable cloud platforms to increase revenue by 30%.Before presenting our deflation techniques, we examine the efficacy and feasibility of deflating public cloud applications. We use publicly-available resource usage traces from two top-tier cloud providers, Azure [14] and Alibaba [15]. The goal of our analysis is to understand the feasibility of deflating CPU, memory, disk, and network allocations of real cloud applications, and specifically interactive web applications, under time-varying workloads that they exhibit. We seek to answer two key research questions through our feasibility analysis: (1) How much slack is present in cloud VMs and by how much can these VMs be safely deflated without any performance impact? (2) How does workload class and VM size impact the deflatability of VMs? Application Behavior under DeflationWe first present an abstract model to capture the performance behavior of an application under different amounts of resource Performance Slack Knee Linear Deflation %

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“…Services are designed to be representative of frameworks used in production systems, modular, and easily reconfigurable. The end-to-end applications and tracing infrastructure are described in more detail and open-sourced in [45]. Table 1 briefly shows the characteristics of each end-toend application, including its communication protocol, the number of unique microservices it includes, and its breakdown by programming language and framework.…”

Section: End-to-end Applications With Microservicesmentioning

confidence: 99%

“…Violating these requirements worsens the end user experience, leads to loss of availability and reliability, and has severe revenue implications [13,14,28,32,35,36]. In an effort to meet these performance constraints and facilitate frequent application updates, cloud services have recently undergone a major shift from complex monolithic designs, which encompass the entire functionality in a single binary, to graphs of hundreds of loosely-coupled, single-concerned microservices [9,45]. Microservices are appealing for several reasons, including accelerating development and deployment, simplifying correctness debugging, as errors can be isolated in specific tiers, and enabling a rich software ecosystem, as each microservice is written in the language or programming framework that best suits its needs.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer

Gan

Zhang

et al. 2019

SIGOPS Oper. Syst. Rev.

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Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting QoS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services.We present Seer, an online cloud performance debugging system that leverages deep learning and the massive amount of tracing data cloud systems collect to learn spatial and temporal patterns that translate to QoS violations. Seer combines lightweight distributed RPC-level tracing, with detailed low-level hardware monitoring to signal an upcoming QoS violation, and diagnose the source of unpredictable performance. Once an imminent QoS violation is detected, Seer notifies the cluster manager to take action to avoid performance degradation altogether. We evaluate Seer both in local clusters, and in large-scale deployments of end-to-end applications built with microservices with hundreds of users. We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases. Finally, we show that Seer can identify applicationlevel design bugs, and provide insights on how to better architect microservices to achieve predictable performance.

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

Cited by 376 publications

References 65 publications

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

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

Cloud-scale VM-deflation for Running Interactive Applications On Transient Servers

Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer

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