Google Compute Engine

Krishnan, S. P. T.; Gonzalez, Jose L. Ugia

doi:10.1007/978-1-4842-1004-8_4

Cited by 43 publications

(16 citation statements)

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“…We estimate the search cost based on GCP's [15] per-second pricing. The total cost for tuning the four workloads is $379, $354, $288 using Opentuner, Gunther and Random Search, respectively.…”

Section: Zero-knowledge Tuningmentioning

confidence: 99%

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To Tune or Not to Tune?

Fekry

Carata

Pasquier

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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This experimental study presents a number of issues that pose a challenge for practical configuration tuning and its deployment in data analytics frameworks. These issues include: 1) the assumption of a static workload or environment, ignoring the dynamic characteristics of the analytics environment (e.g., increase in input data size, changes in allocation of resources). 2) the amortization of tuning costs and how this influences what workloads can be tuned in practice in a cost-effective manner. 3) the need for a comprehensive incremental tuning solution for a diverse set of workloads. We adapt different ML techniques in order to obtain efficient incremental tuning in our problem domain, and propose Tuneful, a configuration tuning framework. We show how it is designed to overcome the above issues and illustrate its applicability by running a wide array of experiments in cloud environments provided by two different service providers. CCS CONCEPTS • Theory of computation → Online learning algorithms; Gaussian processes; Non-parametric optimization.

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Section: Zero-knowledge Tuningmentioning

confidence: 99%

“…To better understand this, we perform experiments in two cloud environments, Amazon Web Services (AWS) [3] and Google Cloud (GCP) [15] -7429 hours of runtime in total, and use the lessons learned to define a practical automated optimization framework.…”

Section: Introductionmentioning

confidence: 99%

To Tune or Not to Tune?

Fekry

Carata

Pasquier

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…Cloud computing can be informally defined as a set of services that enable access to distributed and shared computing resources over the Internet, including information processing, information storage and massive datasets. There are several commercial cloud computing service providers at present like Amazon Web Services [66], Elastic Compute Cloud commonly referred as EC2 [67], Google Compute Engine [68] and Microsoft Azure [69]. Transferring robotic applications to the cloud requires special frameworks or service models that support this transition [3].…”

Section: Cloud Computing and Roboticsmentioning

confidence: 99%

A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

2018

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Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.

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“…This is one of the main reasons why several users are porting their application frameworks to support Docker containers. Cloud Providers have started to provide services for deploying containers such as the Google Container Engine [39] and cloud management stacks have implemented drivers to support Docker containers as another type of VM, such as the Nova-Docker-driver [20] for OpenStack or OneDoc [21], a Docker driver for Open Nebula. Apart from Docker, different container platforms have appeared recently.…”

Section: Related Workmentioning

confidence: 99%

Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

et al. 2018

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This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a usertransparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.

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Google Compute Engine

Cited by 43 publications

References 0 publications

To Tune or Not to Tune?

To Tune or Not to Tune?

A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

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