Modeling the Green Cloud Continuum: integrating energy considerations into Cloud-Edge models

被引:3
作者
Patel, Yashwant Singh [1 ,3 ]
Townend, Paul [1 ]
Singh, Anil [1 ,3 ]
Ostberg, Per-Olov [2 ]
机构
[1] Umea Univ, Dept Comp Sci, S-90187 Umea, Sweden
[2] Umea Univ, Biti Innovat & Dept Comp Sci, S-90187 Umea, Sweden
[3] Thapar Inst Engn & Technol, Dept Comp Sci & Engn, Patiala 147004, Punjab, India
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2024年 / 27卷 / 04期
关键词
Models; Green; Cloud-Edge; Renewable energy; Resource management; Continuum; WORKLOAD PREDICTION; PLACEMENT METHOD; COST; CONSUMPTION; MANAGEMENT; ALLOCATION; RESOURCES; FRAMEWORK; SERVICES; NETWORK;
D O I
10.1007/s10586-024-04383-w
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The energy consumption of Cloud-Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud-Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud-Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud-Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud-Edge models together.
引用
收藏
页码:4095 / 4125
页数:31
相关论文
共 137 条
  • [1] Scheduling Internet of Things requests to minimize latency in hybrid Fog-Cloud computing
    Aburukba, Raafat O.
    AliKarrar, Mazin
    Landolsi, Taha
    El-Fakih, Khaled
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 111 (539-551): : 539 - 551
  • [2] DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds
    Ahvar, Ehsan
    Ahvar, Shohreh
    Mann, Zoltan Adam
    Crespi, Noel
    Glitho, Roch
    Garcia-Alfaro, Joaquin
    [J]. IEEE ACCESS, 2021, 9 : 70192 - 70213
  • [3] Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures
    Ahvar, Ehsan
    Orgerie, Anne-Cecile
    Lebre, Adrien
    [J]. IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING, 2022, 7 (02): : 277 - 288
  • [4] amazon, AWS Lambda
  • [5] Andrae ASG., 2015, Challenges, V6, P117, DOI [10.3390/challe6010117, DOI 10.3390/CHALLE6010117]
  • [6] Towards a Multi-objective Scheduling Policy for Serverless-based Edge-Cloud Continuum
    Angelelli, Luc
    Da Silva, Anderson Andrei
    Georgiou, Yiannis
    Mercier, Michael
    Mounie, Gregory
    Trystram, Denis
    [J]. 2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING, CCGRID, 2023, : 485 - 497
  • [7] [Anonymous], 2017, RENEWABLE ENERGY WOR
  • [8] [Anonymous], 2021, MICROSOFTS NEW DATAC
  • [9] [Anonymous], 2024, Electricity Maps
  • [10] An ensemble learning framework for anomaly detection in building energy consumption
    Araya, Daniel B.
    Grolinger, Katarina
    ElYamany, Hany F.
    Capretz, Miriam A. M.
    Bitsuamlak, Girma
    [J]. ENERGY AND BUILDINGS, 2017, 144 : 191 - 206