An Architecture of Thin Client-Edge Computing
Collaboration for Data Distribution and Resource Allocation in Cloud
Aymen Alsaffar, Pham Hung,
and Eui-Nam Huh
Department of Computer Science and Engineering, Kyung Hee
University, South Korea
Abstract: These days, Thin-client devices are
continuously accessing the Internet to perform/receive diversity of services in
the cloud. However these devices might either has lack in their capacity (e.g.,
processing, CPU, memory, storage, battery, resource allocation, etc) or in
their network resources which is not sufficient to meet users satisfaction in
using Thin-client services. Furthermore, transferring big size of Big Data over
the network to centralized server might burden the network, cause poor quality
of services, cause long respond delay, and inefficient use of network
resources. To solve this issue, Thin-client devices such as smart mobile device
should be connected to edge computing which is a localized near to user
location and more powerful to perform computing or network resources. In this
paper, we introduce a new method that constructs its architecture on
Thin-client -edge computing collaboration. Furthermore, present our new
strategy for optimizing big data distribution in cloud computing. Moreover, we
propose algorithm to allocate resources to meet Service Level Agreement (SLA)
and Quality of Service (QoS) requirements. Our simulation result shows that our
proposed approach can improve resource allocation efficiently and shows better
performance than other existing methods.
Keywords: Cloud computing, data distribution, edge
computing, resource allocation, and thin client.
Received January 19, 2015; accepted August 12,
2015