# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 5 Conclusions  
The development of huge CDCs and a variety of DDCs has led to the enormous degradation of performance and EC in DDCs [11]. In addition, users move frequently. Thus, it is essential for CDCs to conduct SP considering a variety of elements for energy efficiency and performance efficiency with resource management.  
Figure 16: EC according to policy Fuzzy (sc1): Energy, Fuzzy (sc2): Performance, Fuzzy (sc3): Cost.  
Figure 15: Energy consumption by policy.  
Therefore, various approaches have been studied for the DDCs. In particular, to take advantage of nearby resources and resource management, SP and migration techniques have been playing an important role. In addition, user requirements are increasing with the various service types.  
We proposed an efficient SP scheme to solve these limitations and satisfy user requirements. In this paper, we focused on performance and energy efficiency. Moreover, the proposed system allowed efficient placement for a variety of requirements as well as tested cases such as performance and energy efficiency. Through the results of the test cases, we proved that the ET of the proposed scheme was about 39% faster than that of the threshold scheme. In addition, the proposed system reduced EC by up to around 32% in comparison with the threshold scheme. Using the results of the evaluation, our proposed scheme was verified to be more energy-efficient and performance-efficient than the general scheme.  
Our proposed scheme can efficiently manage resources and improve user satisfaction according to services of various objectives. We hope this algorithm can contribute to distributed cloud environments when services are placed and migrated.  
However, this study has a few limitations. More research and evaluation are needed to consider various environments and user requirements. In addition, the difficulty in making an optimal decision about a dynamically changed service state needs to be addressed. Therefore, in future studies, we will run our scheme with various services and compare it with diverse approaches to advance the scheme.  
Conceptualization, E.-N.H. Software, A-Y.S. Supervision, E.-N.H. Validation, A-Y.S. Visualization, A-Y.S. Writing--original draft, A-Y.S. Writing--review and editing, E.-N.H. This research received no external funding. This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program(IITP-2019-2015-0-00742) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and This work was supported by Institute for Information & communications Technology Planning&Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2017-0-00294, Service mobility support distributed cloud technology) The authors declare no conflict of interest. The following abbreviations are used in this manuscript.  
Distributed Data Center  
Cloud Data Center  
Service Placement  
Service Level Agreement  
Analytical Hierarchical Process  
Internet of Things  
Quality of Service  
Cloud Service Provider  
Fuzzy-AHP system  
Virtual Machine  
Physical Machine  
Multi objective optimization  
Membership Function  
Resource Utilization  
Number of Migrations  
Waiting Time