Energy-Efficient Core Allocation and Deployment for Container-Based Virtualization

Lin, Ching-Chi; Chen, Jian-Jia; Liu, Pangfeng; Wu, Jan‐Jan

doi:10.1109/padsw.2018.8644537

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Researches that address the efficient allocation of resources to containers are scarce and still lead to confusion. Some believes [57][58][59] that the use of container on bare metal is more optimal in terms of energy consumption ( Fig. 7b).…”

Section: Energy-efficient Container Resources Managementmentioning

confidence: 99%

“…Therefore, servers have to be chosen adaptively based on request type. However, as the energy consumption and performance of a container are primarily impacted by the number of cores assigned to it and their frequencies, this author [58] focuses on the energy efficiency of cores allocation problem for containers in a DC and decides the number of cores, core frequencies, and the core deployment per container. This study considers two scenarios: single server and multiple servers; both scenarios take into account DVFS per server and DVFS per container.…”

Section: Energy-efficient Container Resources Managementmentioning

confidence: 99%

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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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Section: Energy-efficient Container Resources Managementmentioning

confidence: 99%

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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“…(1) Task moldability [30,36,47,48], i.e., executing a single task on multiple threads/cores via work-sharing can help to reduce the energy consumption by decreasing resource oversubscription or making use of otherwise idle resources. Moldability helps to exploit the internal parallelism within a single task by supporting 1:M mapping (i.e., a single task to multiple threads/cores) in addition to the traditional 1:1 mapping (i.e., a single task to a single thread-/core).…”

Section: Introductionmentioning

confidence: 99%

ERASE: Energy Efficient Task Mapping and Resource Management for Work Stealing Runtimes

Chen

Manivannan

Abduljabbar

et al. 2022

ACM Trans. Archit. Code Optim.

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Parallel applications often rely on work stealing schedulers in combination with fine-grained tasking to achieve high performance and scalability. However, reducing the total energy consumption in the context of work stealing runtimes is still challenging, particularly when using asymmetric architectures with different types of CPU cores. A common approach for energy savings involves dynamic voltage and frequency scaling (DVFS) wherein throttling is carried out based on factors like task parallelism, stealing relations, and task criticality. This article makes the following observations: (i) leveraging DVFS on a per-task basis is impractical when using fine-grained tasking and in environments with cluster/chip-level DVFS; (ii) task moldability, wherein a single task can execute on multiple threads/cores via work-sharing, can help to reduce energy consumption; and (iii) mismatch between tasks and assigned resources (i.e., core type and number of cores) can detrimentally impact energy consumption. In this article, we propose EneRgy Aware SchedulEr (ERASE), an intra-application task scheduler on top of work stealing runtimes that aims to reduce the total energy consumption of parallel applications. It achieves energy savings by guiding scheduling decisions based on per-task energy consumption predictions of different resource configurations. In addition, ERASE is capable of adapting to both given static frequency settings and externally controlled DVFS. Overall, ERASE achieves up to 31% energy savings and improves performance by 44% on average, compared to the state-of-the-art DVFS-based schedulers.

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“…Some recent works analyzed the performance evaluation of microservices architectures using simulations through the CloudSim tool [14] [15]. Other studies have used the elasticity metric to assess energy management [16] [17]. Through modeling, some other works verified the reliability of the [18] [19].…”

mentioning

confidence: 99%

Energy Consumption in Microservices Architectures: A Systematic Literature Review

Araújo,

Sabino,

Lima

et al. 2023

Preprint

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Cloud computing is a paradigm that delivers technology resources on demand through service virtualization. Virtualization allows sharing of hardware by applications compartmentalized into multiple independent modules. Microservices architecture uses the ability of virtualization to adopt a software architecture approach based on building applications composed of several interdependent and loosely coupled modules. However, microservices architectures can incur large energy costs to achieve the required levels of performance and availability. Several research topics related to energy consumption in microservice architectures have already been explored, such as elasticity, reliability, performance, and availability. However, given the variety of problems and solutions, identifying research trends and gaps related to microservices is complex. We aim to systematically identify, rank, and compare existing research trends and gaps around energy consumption in microservices. This paper presents a systematic review of energy consumption in microservices architectures. This work also provides a list of references to find the mapped works, thus being able to guide searches more safely. paper selection started with 3625 papers, followed by three refinement processes in which 37 papers were selected for a full review. The remaining 37 papers were cataloged and classified according to some criteria, such as metrics, evaluation method and type of architecture. Thus, the review revealed research gaps and trends in energy consumption in microservices architectures. In addition, significant research challenges and directions were identified. The research challenges are presented based on the metrics that guide each work in this systematic review: performance, elasticity, scalability, reliability, sustainability, and availability.

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Energy-Efficient Core Allocation and Deployment for Container-Based Virtualization

Cited by 10 publications

References 13 publications

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

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

ERASE: Energy Efficient Task Mapping and Resource Management for Work Stealing Runtimes

Energy Consumption in Microservices Architectures: A Systematic Literature Review

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