Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

Shi, Tao; Ma, Hui; Chen, Gang

doi:10.1109/cec.2018.8477708

Cited by 38 publications

(13 citation statements)

References 22 publications

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“…Similarly, Shi et al [17] jointly consider the problem of VM selection and VM placement in container placement, and proposes a Two-stage Multi-type Particle Swarm Optimization (TMPSO) algorithm. It optimizes the utilization of each host by selecting the appropriate VM to reduce the number of hosts used, and finally achieve the goal of reducing the energy consumption of cloud data center.…”

Section: A Static Placementmentioning

confidence: 99%

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An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

Zhang

Wang

et al. 2021

IEEE Access

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Section: A Static Placementmentioning

confidence: 99%

“…= { ,1 , … , , , … , ,| | }, , and , represent the maximum and minimum of the nonnegative values in ; similarly, = { ,1 , … , , , … , ,| | }, , and , represent the maximum and minimum of the non-negative values in . For all non-negative , and , , the formulas for standardized transformation are as (17) and (18).…”

Section: ) Vm To Host Placemenmentioning

confidence: 99%

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

Zhang

Wang

et al. 2021

IEEE Access

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“…7b). Others think [60][61][62] that the combination of the two sand-boxing technologies (VM containers) is more efficient (Fig. 7a).…”

Section: Energy-efficient Container Resources Managementmentioning

confidence: 99%

“…So as shown, more focus was just on host level. Again in this study [61], a suitable VM for containers in terms of type and operating system was selected first using first fit (FF) algorithm regarding both CPU and memory. However, PM's selection was more largely performed using PSO.…”

Section: Energy-efficient Container Resources Managementmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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“…[24] assigns the instances to the suitable PM nodes, where the instances could be containers or VMs, and we use the CaaS deployment model that is proposed in [1], i.e., applications are packaged in containers, containers are running on virtual machines (VMs), and VMs are placed on PMs. Tao Shi [37] propose a TMPSO algorithm for container consolidation, but they are only concerned with the energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Be Good Neighbors: A Novel Application Isolation Metric Used to Optimize the Initial Container Placement in CaaS

et al. 2020

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Many cloud providers benefit from fine-grained resource management in Container as a Service (CaaS), e.g., energy savings. However, while optimizing power consumption, cloud providers also need to consider service performance. Few studies in the literature performed a balanced optimization of the two objectives in the initial container placement (ICP). As such, the obtained solutions are often locally optimal. In this work, we model the ICP as a bi-objective optimization problem. Unfortunately, directly applying a conventional performance modeling method in solving the problem is complex and costly, especially for a large-scale application scenario. It also fails to capture the impacts of some non-resource constraints such as high availability constraints. To tackle these issues, we propose a novel application isolation metric to quantify the overall service performance. With regard to the power consumption objective, we opt for a non-linear model since a linear one makes power consumption indistinguishable in a homogeneous setting. With the two building blocks, we introduce an optimization algorithm named First Fit based improving Genetic Algorithm (denoted by FF-based-IGA) to find the best ICP solution. Our experimental results are as follows: 1) the proposed application isolation metric is effective at improving service performance, and 2) FF-based-IGA achieves a desirable balance between power consumption and service performance.

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Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

Cited by 38 publications

References 22 publications

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Be Good Neighbors: A Novel Application Isolation Metric Used to Optimize the Initial Container Placement in CaaS

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