GLAP: Distributed Dynamic Workload Consolidation through Gossip-Based Learning

Khelghatdoust, Mansour; Gramoli, Vincent; Sun, Daniel

doi:10.1109/cluster.2016.24

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…The proposed RL-DC does not require any prior information about workload and dynamically achieve online energy and performance management. Another paper [91] combines Q-learning with novel two-phase (learning aggregation) distributed algorithm. Here, each PM computes Q-values and converges to their unified value through a gossip-based protocol.…”

Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

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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Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

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

et al. 2020

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“…VM consolidation considering workload characterization patterns in cloud data center is proposed in [18]. The authors propose a fully distributed and threshold free Dynamic Virtual Machine Consolidation (DVMC) algorithm called GLAP that combines Q-Learning with a gossip-based protocol.…”

Section: Related Workmentioning

confidence: 99%

Dynamic K-Means Clustering of Workload and Cloud Resource Configuration for Cloud Elastic Model

et al. 2020

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“…Moreover, this BFD algorithm is SO and provides a local optimization, so it is fast and light to be used during runtime. This algorithm is referred in many recent publications, and it is also included in the open source version of CloudSim 2.0. The second baseline is an in‐house SA approach.…”

Section: Performance Evaluationmentioning

confidence: 99%

Heuristics and metaheuristics for dynamic management of computing and cooling energy in cloud data centers

et al. 2018

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Data centers handle impressive high figures in terms of energy consumption, and the growing popularity of cloud applications is intensifying their computational demand. Moreover, the cooling needed to keep the servers within reliable thermal operating conditions also has an impact on the thermal distribution of the data room, thus affecting to servers' power leakage. Optimizing the energy consumption of these infrastructures is a major challenge to place data centers on a more scalable scenario. Thus, understanding the relationship between power, temperature, consolidation, and performance is crucial to enable an energy-efficient management at the data center level. In this research, we propose novel power and thermal-aware strategies and models to provide joint cooling and computing optimizations from a local perspective based on the global energy consumption of metaheuristic-based optimizations. Our results show that the combined awareness from both metaheuristic and best fit decreasing algorithms allow us to describe the global energy into faster and lighter optimization strategies that may be used during runtime. This approach allows us to improve the energy efficiency of the data center, considering both computing and cooling infrastructures, in up to a 21.74% while maintaining quality of service.

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GLAP: Distributed Dynamic Workload Consolidation through Gossip-Based Learning

Cited by 9 publications

References 20 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

Dynamic K-Means Clustering of Workload and Cloud Resource Configuration for Cloud Elastic Model

Heuristics and metaheuristics for dynamic management of computing and cooling energy in cloud data centers

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