Reinforcement Learning Based Service Provisioning for a Greener Cloud

Ravi, Vaishnavi; Hamead, H. Shahul

doi:10.1109/eco-friendly.2014.92

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another 20 papers were removed in the full-text level screening. Out of these, 15 did not present a VM consolidation approach [1,9,19,[26][27][28][29]32,45,47,52,54,58,60,63], 2 presented a centralized VM consolidation approach [15,23], 2 were published in several papers [20,37], and 1 had a duplicate record [49]. Finally, from the remaining 21 papers, 2 papers were removed in the quality assessment stage because their aggregate scores were below the cutoff point [7,34].…”

Section: Resultsmentioning

confidence: 99%

Distributed virtual machine consolidation: A systematic mapping study

Ashraf

Byholm

Porres

2018

Computer Science Review

View full text Add to dashboard Cite

Background: Virtual Machine (VM) consolidation is an effective technique to improve resource utilization and reduce energy footprint in cloud data centers. It can be implemented in a centralized or a distributed fashion. Distributed VM consolidation approaches are currently gaining popularity because they are often more scalable than their centralized counterparts and they avoid a single point of failure. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on distributed VM consolidation approaches. Method: A Systematic Mapping Study (SMS) of the existing distributed VM consolidation approaches. Results: 19 papers on distributed VM consolidation categorized in a variety of ways. The results show that the existing distributed VM consolidation approaches use four types of algorithms, optimize a number of different objectives, and are often evaluated with experiments involving simulations. Conclusion: There is currently an increasing amount of interest on developing and evaluating novel distributed VM consolidation approaches. A number of research gaps exist where the focus of future research may be directed.

show abstract

Section: Resultsmentioning

confidence: 99%

Distributed virtual machine consolidation: A systematic mapping study

Ashraf

Byholm

Porres

2018

Computer Science Review

View full text Add to dashboard Cite

show abstract

“…This technique reduces the combined state and action space considerably. The authors of [24] propose an RL-based technique which causes the migration of VMs from servers with low utilization and subsequently shuts the servers down.…”

Section: Proactive Methodsmentioning

confidence: 99%

Driving under influence: Robust controller migration for MEC-enabled platooning

Ayimba

Segata

Casari

et al. 2022

Computer Communications

View full text Add to dashboard Cite

“…Ravi and Hamead [34] developed an energy-efficient solution for Green Computing. The proposed approach leverages live migration of the VMs to reduce energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility

Addad

Dutra

Taleb

et al. 2021

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.

show abstract

Reinforcement Learning Based Service Provisioning for a Greener Cloud

Cited by 6 publications

References 7 publications

Distributed virtual machine consolidation: A systematic mapping study

Distributed virtual machine consolidation: A systematic mapping study

Driving under influence: Robust controller migration for MEC-enabled platooning

Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility

Contact Info

Product

Resources

About