# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 1 Introduction  
In the past decade, traditional cloud computing technology, which relies heavily on the centralized cloud data center (CDC), has been widely used. Cloud services have also been offered in the form of centralized CDCs that can be remote from mobile users. This sometimes entails network delay due to the centralized CDC structure. Recently, significant progress has been made in the deployment of cloud services nearer to users, which provides higher reliability and faster response time. In particular, in distributed cloud services, a small cloud, such as a distributed cloud, can be connected to provide services to the device users in order to provide the low latency and rapid response time of mobile devices. By placing and migrating to the cloud services that have a higher resource near to the mobile users, the distributed cloud offers faster response time, along with higher scalability and reliability than centralized CDCs [1; 2; 3]. One of the important issues in distributed cloud data centers (DDCs) is service placement (SP) with mobility. The limited coverage of a single server to support mobile users will result in service-level agreement (SLA) violation, which further leads to a decrease in performance and quality of service (QoS). Therefore, it is difficult to ensure seamless service [4; 5]. Unlike conventional CDCs, DDCs are characterized by a relatively low computing resource and storage capacity. These DDCs consist of computing devices such as personal computers (PC), tablets, and laptops. To support distributed cloud services efficiently, there is a need to manage and place various tasks in order to meet various SLAs parameters such as resource utilization, response time, service availability, latency, and costs. As many different end devices need to connect to the distributed cloud for SP, a decision should be made, when a service needs migration [6]. For this reason, various approaches for placement and migration have been studied and proposed for DDCs. To take advantage of nearby resources and resource management, SP and migration techniques have been playing an important role [7; 8]. However, existing SP and migration schemes have some challenges, which are covered below. First, placement and migration techniques in cloud computing have been developed to find various objectives to maximize energy efficiency [9], minimize costs [10], and maximize performance. However, in most of the previous approaches, objectives of services are not considered. For example, References [11; 12] mainly focus on energy efficiency, while Reference [11] focuses on performance. Due to the high uncertainty of user mobility and requirements, it is very difficult to conduct efficient SP. Thus, in order to meet various requirements, we should also consider various metrics based on the objective. Second, the cloud service provider (CSP) needs to balance conflicting objectives such as minimizing resources wastage, reducing overall operation costs, and minimizing service latency [13]. Specifically, multi-objective optimization investigates the search methods that are used to find solutions based on several conflicting performance objectives, such as waiting time minimization, resource utilization maximization, and profitability maximization [10; 11; 12; 13]. It is difficult for the CSP to manage the trade-off between objectives. For example, energy efficiency is typically optimized at the expense of QoS. From the perspective of the cloud user, the only priority is the fulfillment of their application resource demands. The resource demand is typically specified as SLA. Although this approach can provide continuous service with a minimal delay, there are disadvantages, such as degraded virtual machine (VM) execution performance and increased SP cost. In addition, some SP algorithms provide a well-balanced SLA efficiently. However, they need to be avoided since they increase network traffic and energy costs [7; 8].