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

Ehsan Ahvar,Noel Crespi,Zoltan Adam Mann,Shohreh Ahvar,Joaquin Garcia-Alfaro,Roch Glitho

doi:10.1109/access.2021.3075973

Ehsan Ahvar, Noel Crespi + Show 4 more

Open Access

https://doi.org/10.1109/access.2021.3075973

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Abstract

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.

Highlights

The Internet of Things (IoT) is producing rapidly increasing amounts of data
We describe in the Appendix the benefit of the A* algorithm for application placement in Edge Clouds (ECs) compared to other heuristics
Given a number of ACs with their sizes and traffic matrix as an input, we aim to find a new feasible placement for ACs allocated on under-utilized Compute Nodes (CNs), minimizing: (1) the energy spent to run the ACs, (2) the total network overhead, and (3) the overhead of AC migrations

Summary

INTRODUCTION

The Internet of Things (IoT) is producing rapidly increasing amounts of data. Data analytics applications that process IoT data require significant computational capacity, which IoT devices typically do not possess. There is a variety of resources, energy prices and carbon emission rates to consider. In contrast to most previous works, DECA considers both CNs and network devices because both of them may contribute significantly to energy consumption. DECA performs joint optimization of compute and network resources, considering their associated energy price and carbon emission rate. It can select CNs from multiple ECs to place the components of an application, in order to (i) be able to achieve low overall energy cost and carbon emission and (ii) overcome capacity limitations of a single EC.

RELATED WORK

APPLICATION ALLOCATION PROBLEM

PERFORMANCE EVALUATION

Findings

CONCLUSION