Measuring Docker Performance

Casalicchio, Emiliano; Perciballi, Vanessa

doi:10.1145/3053600.3053605

Cited by 60 publications

(35 citation statements)

References 15 publications

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“…Then, the correlation model is used to take appropriate auto-scaling decisions. This work confirms also the importance of monitoring the appropriate performance counters, as suggested in [11].…”

Section: Performancesupporting

confidence: 86%

“…Performance monitoring is a topic of increasing interest for the containers' research community. In [11] the authors assess the different measurement methodology used to collect performance counters for CPU and disk I/O intensive Docker workload. The importance of using information about the performance of all the stack components to take effective deployment and adaptation decisions is addressed in [20] where the authors propose Elascale, a cloud service for monitoring performance metrics at all the layers of the computational stack.…”

Section: Performancementioning

confidence: 99%

“…With containers, a microservice or an application can be executed on any platform running a container engine [23]. Containers are lightweight and introduce lower overhead compared to Virtual Machines (VMs) [11,14,22,25]. Those are some of the reasons that make the cloud computing industry to adopt container technologies and to contribute to their evolution [7,8,33].…”

Section: Introductionmentioning

confidence: 99%

“…First, the correlation between absolute and relative usage measures [11] and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values [11]. The former account for the actual utilization of resources in the host system (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A study on performance measures for auto-scaling CPU-intensive containerized applications

Casalicchio

2019

Cluster Comput

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Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes' auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented.

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Section: Performancesupporting

confidence: 86%

Section: Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A study on performance measures for auto-scaling CPU-intensive containerized applications

Casalicchio

2019

Cluster Comput

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“…Container-level runtime metrics are pulled from containers by the Kubernetes kubelet process using the cAdvisor software, which has low overhead. 19,20 Prometheus pulls this data from the kubelet every 15 seconds, which should have a negligible performance impact on the overall system when amortized over experiments lasting 20 to 30 minutes. Results for the entire experiment are queried from Prometheus only after the experiment is finished.…”

Section: Data Collectionmentioning

confidence: 99%

Impact of etcd deployment on Kubernetes, Istio, and application performance

et al. 2020

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Summary This experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P < .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

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The state‐of‐the‐art in container technologies: Application, orchestration and security

Casalicchio

Iannucci

2020

Concurrency and Computation

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SummaryContainerization is a lightweight virtualization technology enabling the deployment and execution of distributed applications on cloud, edge/fog, and Internet‐of‐Things platforms. Container technologies are evolving at the speed of light, and there are many open research challenges. In this paper, an extensive literature review is presented that identifies the challenges related to the adoption of container technologies in High Performance Computing, Big Data analytics, and geo‐distributed (Edge, Fog, Internet‐of‐Things) applications. From our study, it emerges that performance, orchestration, and cyber‐security are the main issues. For each challenge, the state‐of‐the‐art solutions are then analyzed. Performance is related to the assessment of the performance footprint of containers and comparison with the footprint of virtual machines and bare metal deployments, the monitoring, the performance prediction, the I/O throughput improvement. Orchestration is related to the selection, the deployment, and the dynamic control of the configuration of multi‐container packaged applications on distributed platforms. The focus of this work is on run‐time adaptation. Cyber‐security is about container isolation, confidentiality of containerized data, and network security. From the analysis of 97 papers, it came out that the state‐of‐the‐art is more mature in the area of performance evaluation and run‐time adaptation rather than in security solutions. However, the main unsolved challenges are I/O throughput optimization, performance prediction, multilayer monitoring, isolation, and data confidentiality (at rest and in transit).

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Measuring Docker Performance

Cited by 60 publications

References 15 publications

A study on performance measures for auto-scaling CPU-intensive containerized applications

A study on performance measures for auto-scaling CPU-intensive containerized applications

Impact of etcd deployment on Kubernetes, Istio, and application performance

The state‐of‐the‐art in container technologies: Application, orchestration and security

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