Application Oriented Dynamic Resource Allocation for Data Centers Using Docker Containers

Guan, Xiaodong; Wan, Xili; Choi, Baek‐Young; Song, Sejun; Zhu, Jiafeng

doi:10.1109/lcomm.2016.2644658

Cited by 86 publications

(35 citation statements)

References 10 publications

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“…In 2017, Guan et al . [4] have designed a resource allocation technique using application‐oriented Docker container (AODC) framework for supporting the automatic scaling functions. It also minimises the application costs in DCs, along with the varying workload applications of the cloud.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Anyhow, the interdependence among the services has to be concerned more. Likewise, linear programming (LP) was exploited in [4], which offers optimal acceptance ratio and it also reduced application cost. However, it involves more consideration of optimisation issues.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It could provide numerous functionalities based on the needs of an organisation, namely virtualisation of desktop, server virtualisation and network virtualisation, in addition to storage virtualisation and application. Resource allocation [4,5] and management in the cloud was found to be extensively analysed for saving the cost and for improving the efficiency of resource utilities. At present, there are certain approaches that are used for distributing resources for handling the workloads in the cluster [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Particle swarm optimisation with grey wolf optimisation for optimal container resource allocation in cloud

Vhatkar¹,

Bhole²

2020

IET Networks

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In the cloud sector, as the applications used by users are exploited via micro-service pattern, the container allocation seems to be the most vital process. This has further been concentrated with more care for its beneficiary acts like easier employment, limited overheads and higher portability. For the past few decades, various contributions have been made under the container management and allocation as well. Under these circumstances, this study intends to design an optimal resource allocation and management model by incorporating the concept of optimisation, which guarantees optimal container allocation. To make this possible, this study establishes a novel hybrid algorithm, namely velocity-updated grey wolf optimisation (VU-GWO), which is the hybridisation of two renowned algorithms particle swarm optimisation and grey wolf optimization, respectively. More importantly, the solution of optimised resource allocation is influenced by the designing of a novel objective function, which concerns the constraints like balanced cluster use, threshold distance, system failure, and total network distance as well. At last, the performance of the presented scheme is evaluated over other traditional schemes, and the betterment of the proposed model is validated.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Particle swarm optimisation with grey wolf optimisation for optimal container resource allocation in cloud

Vhatkar¹,

Bhole²

2020

IET Networks

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“…The ACO algorithm performance is compared with the greedy scheduling algorithm in DockerSwarm. A framework for Application Oriented Docker container (AODC) resource allocation to minimize the application deployment cost in datacenters is presented in [16]. AODC considers deployment of container on PM and is compared against optimal VM placement algorithms.…”

Section: Auto-scalingmentioning

confidence: 99%

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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“…Reference [28] employed an ant colony optimization (ACO)based algorithm to optimize the distribution and scheduling of Docker containers. Reference [29] developed a linear programming based framework that can optimize resource allocation for application-oriented Docker containers and support automatic resource expansion when demand changes. Reference [30] proposed a resource allocation mechanism for container cloud based on combined double auction and simulated annealing algorithms.…”

Section: Related Workmentioning

confidence: 99%

Improving Resource Usages of Containers Through Auto-Tuning Container Resource Parameters

Cai

Wei

et al. 2019

IEEE Access

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Recently, the container-based virtualization has gained increasing attention and been widely used in cloud computing. In container products such as Docker, there are a number of parameters that can control container resource usages, to avoid the resource contention occurred when running too many containers concurrently. However, it is difficult to set parameter values accurately only based on experience while tuning the parameters manually is too time-consuming to be impractical. Therefore, it becomes a challenge to set appropriate resource parameter values automatically and quickly to optimize the resource usages of container. In this paper, we present an adaptive tuning framework, conTuner, to optimize the resource configuration of container online for a new application. conTuner contains two components: an optimized configuration pool that offers candidate resource configurations, as well as a configuration optimizer that gets the appropriate optimized configuration from the pool. We have deployed conTuner in a Docker cluster. The experimental results demonstrated that, for a new application, compared to the pre-set upper limit of container resource usages, the container performance is equal or better when using conTuner, and the set resource usage constraint is more accurate. Besides, conTuner can also forecast whether resource contention among multiple containers occurs before running them concurrently. The evaluation results indicate that the prediction accuracy is 87%.

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Application Oriented Dynamic Resource Allocation for Data Centers Using Docker Containers

Cited by 86 publications

References 10 publications

Particle swarm optimisation with grey wolf optimisation for optimal container resource allocation in cloud

Particle swarm optimisation with grey wolf optimisation for optimal container resource allocation in cloud

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

Improving Resource Usages of Containers Through Auto-Tuning Container Resource Parameters

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