The Architectural Implications of Cloud Microservices

Gan, Yu; Delimitrou, Christina

doi:10.1109/lca.2018.2839189

Cited by 88 publications

(51 citation statements)

References 15 publications

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“…First, we quantify how effective current datacenter architectures are at running microservices, as well as how datacenter hardware needs to change to better accommodate their performance and resource requirements (Section 4). This includes analyzing the cycle breakdown in modern servers, examining whether big or small cores are preferable [25,35,41,42,[46][47][48], determining the pressure microservices put on instruction caches [37,52], and exploring the potential they have for hardware acceleration [24,27,38,49,71]. We show that despite the small amount of computation per microservice, the latency requirements of each individual tier are much stricter than for typical applications, putting more pressure on predictably high single-thread performance.…”

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

Self Cite

376

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: 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

Self Cite

376

215

View full text Add to dashboard Cite

show abstract

“…Serverless computing and other container-based platforms suffer several performance and utilization barriers. There are several ways to address these problems, including datacenter design [Gan and Delimitrou 2018], resource allocation [Björkqvist et al 2016], programming abstractions [Baldini et al 2017;Rabbah 2017], edge computing [Aske and Zhao 2018], and cloud container design [Shen et al 2019]. λ is designed to elucidate subtle semantic issues (not performance problems) that affect programmers building serverless applications.…”

Section: Related Workmentioning

confidence: 99%

Formal foundations of serverless computing

Jangda

Pinckney

Brun

et al. 2019

Proc. ACM Program. Lang.

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Serverless computing (also known as functions as a service) is a new cloud computing abstraction that makes it easier to write robust, large-scale web services. In serverless computing, programmers write what are called serverless functions, which are programs that respond to external events. When demand for the serverless function spikes, the platform automatically allocates additional hardware and manages load-balancing; when demand falls, the platform silently deallocates idle resources; and when the platform detects a failure, it transparently retries affected requests. In 2014, Amazon Web Services introduced the first serverless platform, AWS Lambda, and similar abstractions are now available on all major cloud computing platforms.Unfortunately, the serverless computing abstraction exposes several low-level operational details that make it hard for programmers to write and reason about their code. This paper sheds light on this problem by presenting λ , an operational semantics of the essence of serverless computing. Despite being a small (half a page) core calculus, λ models all the low-level details that serverless functions can observe. To show that λ is useful, we present three applications. First, to ease reasoning about code, we present a simplified naive semantics of serverless execution and precisely characterize when the naive semantics and λ coincide. Second, we augment λ with a key-value store to allow reasoning about stateful serverless functions. Third, since a handful of serverless platforms support serverless function composition, we show how to extend λ with a composition language and show that our implementation can outperform prior work. 1 let accounts = new Map(); 2 exports.bank = function(req, res) { 3 if (req.body.type === 'deposit') { 4 accounts.set(req.body.name, req.body.value); 5 res.send(true); 6 } else if (req.body.type === 'transfer') { 7 let { from, to, amnt } = req.body; 8 if (accounts.get(from) >= amnt) { 9

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“…Micro services can either be deployed on virtual machines running in the data center or in the cloud infrastructure. Lot of researchers have already worked to identify the bottle necks and implications in the infrastructure design involved in the local data centers [16] [17]. MSA was designed to achieve horizontal scalability and quality of service (QoS).…”

Section: Choice Of the Right Infrastructure Platformrelated Workmentioning

confidence: 99%

A Systematic Study of Micro Service Architecture Evolution and their Deployment Patterns

Rudrabhatla¹

2018

IJCA

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With the advent of Local Area Networks (LAN), the client server architecture gained traction. The ever-growing need for the distributed systems have paved the path for client server architecture to transform in to Service Oriented Architecture (SOA). Due to the reusable and loosely coupled nature of the services, SOA became a successful representation of client server architecture. However, over time SOA fell short of expectations, as it was fully reliant on monolithic system design. Achieving horizontal scalability, faster response times, high availability, infrastructure agility, service and resource isolation was a challenge in SOA frameworks. Micro service architecture (MSA) soon came to the rescue. It offered various solutions to overcome most of the shortfalls of the traditional monolithic SOA architecture. But at the same time, MSA comes with its own set of challenges due to the complex distributed design. Among various design complexities involved in MSA, creating, managing and deploying microservices in a clustered production grade environment is a major challenge. A micro service can be deployed to run on a virtual machine (VM) or on a container which itself runs on a VM. The VM can be in the data center or in the public cloud. The containers can be self-managed or orchestrated. The orchestration can be done by the cloud provider or a third-party software. This research paper illustrates (1) the journey of architectural design patterns from SOA to MSA, by citing the related work and the reasons for evolution. (2) various deployment models available for MSA (3) comparison of the deployment models and a quantitative analysis of the use cases.

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The Architectural Implications of Cloud Microservices

Cited by 88 publications

References 15 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

Formal foundations of serverless computing

A Systematic Study of Micro Service Architecture Evolution and their Deployment Patterns

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