Docker Cluster Management for the Cloud - Survey Results and Own Solution

Peinl, René; Holzschuher, Florian; Pfitzer, Florian

doi:10.1007/s10723-016-9366-y

Cited by 109 publications

(46 citation statements)

References 27 publications

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“…If the next population size is equal to n, it will be returned as the actual next population. However, if the size of combined fronts is greater than n, the first front up to (i − 1)th front are combined, and the remaining individuals are picked from the ith front by selecting the most elite ones as shown in lines [11][12][13][14][15][16][17][18][19]. Selecting the remaining individuals is done after going through three operators: normalization, association, and niching.…”

Section: Constraint Violation Valuementioning

confidence: 99%

“…However, if the size of combined fronts is greater than n, the first front up to (i − 1)th front are combined, and the remaining individuals are picked from the ith front by selecting the most elite ones as shown in lines[11][12][13][14][15][16][17][18][19]. However, if the size of combined fronts is greater than n, the first front up to (i − 1)th front are combined, and the remaining individuals are picked from the ith front by selecting the most elite ones as shown in lines[11][12][13][14][15][16][17][18][19].…”

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confidence: 99%

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Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Imdoukh

Ahmad

Alfailakawi

2019

Concurrency and Computation

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Container virtualization is an emerging technology in cloud computing mainly due to its portability and lightweight features. Scheduling is a key task, performed by container management tool, which indirectly affects the characteristics of distributed software system in terms of availability, realizability, scalability, resources utilization, as well as power consumption. However, current schedulers only focus on some of the aforementioned aspects but not all. In this paper, a Many-Objective Genetic Algorithm Scheduler (MOGAS) is proposed to handle all such objectives to realize solutions with better characteristics. The proposed scheduler is compared with the Ant Colony Optimization (ACO)-based scheduler. Based on the proposed objective functions, simulation results show that MOGAS is better than the ACO scheduler in equally distributing tasks by 50%, assigning unique set of tasks per node by 40%, and reducing power consumption by 7%, on average.

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Section: Constraint Violation Valuementioning

confidence: 99%

mentioning

confidence: 99%

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Imdoukh

Ahmad

Alfailakawi

2019

Concurrency and Computation

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“…Distributed computing paradigms such as the Cloud, is the basis of the IT era. Docker containers offer an efficient option to run applications in the Cloud [23]. The Docker containers may be executed on single host or on multiple hosts.…”

Section: Background and Related Workmentioning

confidence: 99%

Exploring the support for high performance applications in the container runtime environment

Martin

Kandasamy

Chandrasekaran

2018

Hum. Cent. Comput. Inf. Sci.

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IntroductionCloud computing is being used for innumerable applications these days. The end-users vary from naive clients to expertised technicians. Cloud is a pool of resources shared among number of users [1]. Presently, in the world of cloud computing, it is the era of XaaS (Anything-as-a-Service) which means that the providers offer a wide variety of services [2,3]. One of the most recent services provided through the cloud is high performance computing (HPC) environments for the complex applications. Virtualization is the technology which enables users to share a single entity among a group of users. AbstractCloud computing is the driving power behind the current technological era. Virtualization is rightly referred to as the backbone of cloud computing. Impacts of virtualization employed in high performance computing (HPC) has been much reviewed by researchers. The overhead in the virtualization layer was one of the reasons which hindered its application in the HPC environment. Recent developments in virtualization, especially the OS container based virtualization provides a solution that employs a lightweight virtualization layer and promises lesser overhead. Containers are advantageous over virtual machines in terms of performance overhead which is a major concern in the case of both data intensive applications and compute intensive applications. Currently, several industries have adopted container technologies such as Docker. While Docker is widely used, it has certain pitfalls such as security issues. The recently introduced CoreOS Rkt container technology overcomes these shortcomings of Docker. There has not been much research on how the Rkt environment is suited for high performance applications. The differences in the stack of the Rkt containers suggest better support for high performance applications. High performance applications consist of CPU-intensive and data-intensive applications. The High Performance Linpack Library and the Graph500 are the commonly used computation intensive and data-intensive benchmark applications respectively. In this work, we explore the feasibility of this inter-operable Rkt container in high performance applications by running the HPL and Graph500 applications and compare its performance with the commonly used container technologies such as LXC and Docker containers. Martin et al. Hum. Cent. Comput. Inf. Sci. (2018) Hum. Cent. Comput. Inf. Sci. (2018) 8:1 Based on the position of the virtualization layer, virtualization can be of different types like full virtualization, paravirtualization and OS level virtualization. RESEARCHTraditional HPC clusters are composed of many separate dedicated servers called nodes and may be shared among different organizations. The requirements of each user or organization will be different, which demands the creation of customized environments without affecting others. This is not an easy task in traditional HPC systems. As a solution for this, virtualization was adopted for HPC. Virtualization materializes the task by creating s...

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“…The Docker facilitates application executions in a single host. To fabricate computing clusters using containers, higher level tools such as Docker Swarm and Kubernetes are necessary [30,31]. Docker Swarm is a Docker-native container orchestration system for cluster computing.…”

Section: Docker With Kubernetes Orchestration For Clustering Containersmentioning

confidence: 99%

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Huang

Zhang

et al. 2017

IJGI

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Abstract:Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

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Docker Cluster Management for the Cloud - Survey Results and Own Solution

Cited by 109 publications

References 27 publications

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Exploring the support for high performance applications in the container runtime environment

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

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