Recent Advancement of Auto-Scaling in LTE M2M Communication.

Abstract

Lately Machine to Machine (M2M) Communication has gathered huge research interest because of its peculiar nature of communication without any or less human intervention. With the increase in wide variety of devices and application, there is huge change in traffic patterns of Machine Type Communication (MTC) system. Existing traditional Long-Term Evolution (LTE) network will not be able to handle these growing demands of the bandwidth and network availability. There are some challenges in the existing network like latency, scalability, reliability, interference and delay, which degrade the Quality of Service (QoS). Hence to address these issues would require some advanced network resource management capabilities such as Network Functions Virtualization (NFV), Software Defined Networking (SDN). These technologies would help the operators to provide efficient services to consumer. In this literature we present survey of auto-scaling the resources required for LTE communication using SDN, NFV and Machine Learning (ML) for facilitating MTC, along with its requirements, existing work and challenges. This paper first describes in brief about SDN/NFV and its limitations. Then review the existing work and their applicability to MTC along with open problems and finally some future research in this area.