Dynamic CPU Resource Allocation in Containerized Cloud Environments

Monsalve, José; Landwehr, Aaron; Taufer, Michela

doi:10.1109/cluster.2015.99

Cited by 30 publications

(12 citation statements)

References 4 publications

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“…The problem of containers resource allocation has been investigated using time-slicing [25], Fuzzy [29], linear programming models [32], reinforcement learning [24]. Others authors also employ nonstandard techniques like vertical elasticity [5].…”

Section: Resource Allocation For Container-based Ecmentioning

confidence: 99%

“…Others authors also employ nonstandard techniques like vertical elasticity [5]. Most work considers only a single type of resource to allocate, i.e., CPU [25], with few exceptions [24,29]. Some work considers all resources aggregated in one single pool [24], while others [5,29] consider that they are distributed across different nodes, which complicates the allocation problem.…”

Section: Resource Allocation For Container-based Ecmentioning

confidence: 99%

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Resource allocation for edge computing with multiple tenant configurations

Araldo

Stefano

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Edge Computing (EC) consists in deploying computational resources, e.g., memory, CPUs, at the Edge of the network, e.g., base stations, access points, and run there a part of the computation currently running on the Cloud. This approach promises to reduce latency, inter-domain traffic and enhance user experience. Since resources at the Edge are scarce, resource allocation is crucial for EC. While most of the studies assume users interact directly with the Edge submitting a sequence of tasks, we instead consider that users will interact with different Service Providers (SPs), as they currently do in the Web. We therefore consider the case of a Network Operator (NO) that owns the resources at the Edge and must decide how much resource to allocate to the different tenants (SPs). We propose MORA, a polynomial time strategy which allows the NO to maximize its utility, which can be inter-domain traffic savings, improved users' QoE or other metrics of interest. The core of MORA is that (i) it exploits service elasticity, i.e., the fact that services can adapt to the resources allocated by the NO and rely on a remote Cloud for the excess of computation, (ii) it is suitable for micro-services architecture, which decomposes a single service in a set of components, which MORA places in the different computational nodes of the Edge and (iii) it copes with multi-dimensional resources, e.g., memory and CPUs. After analyzing the properties of the algorithm, we show numerically that it performs close to the optimum. To guarantee reproducibility, the numerical evaluation is performed on publicly available traces from Google and Alibaba clusters and in synthetic scenarios and our code is open source. CCS CONCEPTS • Networks → Cloud computing; Network management; Programmable networks; • Computer systems organization → Cloud computing; n-tier architectures;

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Section: Resource Allocation For Container-based Ecmentioning

confidence: 99%

Section: Resource Allocation For Container-based Ecmentioning

confidence: 99%

Resource allocation for edge computing with multiple tenant configurations

Araldo

Stefano

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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“…In DoCloud, a hybrid elasticity controller is proposed that uses proactive and reactive model to scale out and proactive model to scale down. Monsalve et al [24] proposed an approach that controls CPU shares of a container, this approach uses CFS scheduling mode. Nowadays, Docker can use all the CPU shares if there is not concurrency by other containers.…”

Section: Related Workmentioning

confidence: 99%

Autonomic Vertical Elasticity of Docker Containers with ELASTICDOCKER

Al-Dhuraibi¹,

Paraïso

Djarallah³

et al. 2017

2017 IEEE 10th International Conference on Cloud Computing (CLOUD)

129

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Elasticity is the key feature of cloud computing to scale computing resources according to application workloads timely. In the literature as well as in industrial products, much attention was given to the elasticity of virtual machines, but much less to the elasticity of containers. However, containers are the new trend for packaging and deploying microservices-based applications. Moreover, most of approaches focus on horizontal elasticity, fewer works address vertical elasticity. In this paper, we propose ELASTICDOCKER, the first system powering vertical elasticity of Docker containers autonomously. Based on the well-known IBM's autonomic computing MAPE-K principles, ELASTICDOCKER scales up and down both CPU and memory assigned to each container according to the application workload. As vertical elasticity is limited to the host machine capacity, ELASTICDOCKER does container live migration when there is no enough resources on the hosting machine. Our experiments show that ELASTICDOCKER helps to reduce expenses for container customers, make better resource utilization for container providers, and improve Quality of Experience for application end-users. In addition, based on the observed migration performance metrics, the experiments reveal a high efficient live migration technique. As compared to horizontal elasticity, ELASTICDOCKER outperforms Kubernetes elasticity by 37.63%.

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“…For the VM vertical elasticity, there are some works which focus on CPU resizing, e.g, (Lakew et al, 2014) and (Dawoud et al, 2012), while others concentrate on memory resizing, e.g., (Baruchi and Midorikawa, 2011) as well as combination of both such as the work of (Farokhi et al, 2015). (Monsalve et al, 2015) proposed an approach that controls CPU shares of a container, this approach uses CFS scheduling mode. Nowadays, Docker can use all the CPU shares if there is no concurrency by other containers.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, this paper addresses the combination of vertical elasticity of containers and vertical elasticity of VMs. Many works (Baruchi and Midorikawa, 2011), (Dawoud et al, 2012), (Farokhi et al, 2015) handle the vertical elasticity of VMs, other works (Monsalve et al, 2015), (Paraiso et al, 2016) manage the resources of containers. However, no attention was given to the coordination of both elasticities.…”

Section: Introductionmentioning

confidence: 99%

Coordinating Vertical Elasticity of both Containers and Virtual Machines

Al-Dhuraibi

Zalila

Djarallah³

et al. 2018

Proceedings of the 8th International Conference on Cloud Computing and Services Science

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Abstract:Elasticity is a key feature in cloud computing as it enables the automatic and timely provisioning and deprovisioning of computing resources. To achieve elasticity, clouds rely on virtualization techniques including Virtual Machines (VMs) and containers. While many studies address the vertical elasticity of VMs and other few works handle vertical elasticity of containers, no work manages the coordination between these two vertical elasticities. In this paper, we present the first approach to coordinate vertical elasticity of both VMs and containers. We propose an auto-scaling technique that allows containerized applications to adjust their resources at both container and VM levels. This work has been evaluated and validated using the RUBiS benchmark application. The results show that our approach reacts quickly and improves application performance. Our coordinated elastic controller outperforms container vertical elasticity controller by 18.34% and VM vertical elasticity controller by 70%. It also outperforms container horizontal elasticity by 39.6%.

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Dynamic CPU Resource Allocation in Containerized Cloud Environments

Cited by 30 publications

References 4 publications

Resource allocation for edge computing with multiple tenant configurations

Resource allocation for edge computing with multiple tenant configurations

Autonomic Vertical Elasticity of Docker Containers with ELASTICDOCKER

Coordinating Vertical Elasticity of both Containers and Virtual Machines

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