A Load and Distance Aware Cloudlet Selection Strategy in Multi-Cloudlet Environment

A Load and Distance Aware Cloudlet Selection Strategy in Multi-Cloudlet Environment

Ramasubbareddy Somula (VIT University, Vellore, India) and Sasikala R. (Vit University, Vellore, India)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJGHPC.2019040105
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Day to day the usage of mobile devices (MD) is growing in people's lives. But still the MD is limited in terms of memory, battery life time, processing capacity. In order to overcome these issues, the new emerging technology named mobile cloud computing (MCC) has been introduced. The offloading mechanism execute the resource intensive application on the remote cloud to save both the battery utilization and execution time. But still the high latency challenges in MCC need to be addressed by executing resource intensive task at nearby resource cloud server. The key challenge is to find optimal cloudlet to execute task to save computation time. In this article, the authors propose a Round Robin algorithm based on cloudlet selection in heterogeneous MCC system. This article considers both load and distance of server to find optimal cloudlet and minimize waiting time of the user request at server queue. Additionally, the authors provide mathematical evaluation of the algorithm and compare with existing load balancing algorithms.
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Nowadays, mobile devices (MD) are increasingly popular in everyday life. The data traffic rate has been increased to 69% according to CISCO index in 2014 (Index, 2015). The mobile devices are increasing traffic rate more than non-mobile devices. The mobile applications have become complicated in terms of performance and resource consumption in mobile devices. The mobile devices are powerful for processing complicated application.

However, mobile devices are having limited resources such as battery life time, storage capacity, processing capabilities. In order to fulfill the gap between complicated mobile applications and limited resources of mobile devices, the emerging cloud computing technology provides enough resources to fill the gap between mobile devices and resource hungry mobile applications.

Cloud computing (CC) provides various services such as platforms (e.g. operating system), infrastructure (storage, network and server) and applications by cloud service providers (Amazon, Google) the concept of migrating intensive resource application (or) part of application to Remote Server in order to improve response time and battery lifetime. The Cloud computing(CC) is a scalable nature and provides automatic provisioning and deprovisioning for scaling performance of user application (Veni & Bhanu, 2016). The combination of two technologies mobile computing, Cloud computing can be referred as mobile cloud computing (MCC) (Dinh, Lee, Niyato, & Wang, 2013). The MCC allows the mobile users to leverage resources of CC (storage, computing) through wide area network (WAN) as shown in Figure 1. The offloading resource intensive task into remote cloud is sufficient when network status is high for sharing cloud resources on demand basis (Yang, Ou, & Chen, 2008). The authors in (Satish & Reddy, 2018) proposed a genetic algorithm for scheduling recourse by analyzing recourse-cost between service provider and cloud user through action module. The reliability enhancement issues are addressed in (Zhou et al., 2016) to mitigate network and storage recourses while deploying applications on cloud environment.

Figure 1.

Architecture of Mobile Cloud Computing (MCC)


The cloudlet is new technology in MCC that was proposed in (Satyanarayanan et al., 2014). The basic architecture of cloud can be extended from two or three tier hierarchy. The new architecture is composed with three components: mobile device, cloudlet, and cloud. The main objective of cloud is to address resource-intensive and real-time application by offloading into nearby cloudlet with low latency. The cloudlet can also be considered as small-scale data centre (or) data centre in box which is available at the edge of the network. The cloudlet can be accessed by nearby users to process latency-sensitive application in order to reduce execution time (or) waiting time.

The mobile device has to obtain and associate with the optimization cloud in geographical distributed area. The cloudlet can reduce high latency issues but sustains processing of task coming from mobile users in MCC. However, the load balancing mechanisms receiving much attention for researchers in MCC, each cloudlet capability differ and the pattern of arriving jobs cannot be predicted. Distributing load equally among cloudlets in public cloud improves performance of the system and maintain stability. Generally, the load balancing can be categorized into two parts: static and dynamic. The static load balancing mechanism does not require system information. Therefore, it is less complex where as the dynamic mechanism perform load sharing among nodes by using current nodes information. Therefore, it brings additional cost as well as it changes behavior as node status.

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