DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

IEEE Access

Noel Crespi¹  Shohreh Ahvar²  Ehsan Ahvar³  Roch Glitho⁴  Joaquin Garcia-Alfaro⁵  Zoltan Adam Mann⁶ 

[1] &x00C9;ISEP-Institut Sup&x00E9;Learning, Data and Robotics Laboratory, ESIEA, Ivry-sur-Seine, France;lectronique de Paris, Paris, France;paluno-The Ruhr Institute for Software Technology, University of Duisburg-Essen, Duisburg, Germany;rieur d&x2019;

关键词: Edge cloud;    energy consumption;    energy costs;    green computing;    carbon emission;    application placement;   

DOI  :  10.1109/ACCESS.2021.3075973

来源: DOAJ

【 摘 要 】

As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emission-efficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a tradeoff and optimizing energy cost and carbon emission at the same time.

【 授权许可】

Unknown