Abstract
With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.
Highlights
Under the background of the explosive growth of mobile data traffic and the emergence of various new business scenarios, the fifth-generation (5G) mobile communication network is proposed and becoming a hot topic in academical and industrial field
As a new generation of wireless mobile communication network, 5G is mainly used to meet the demand of mobile communication after 2020; driven by the rapid development of mobile Internet and growing demand for Internet of Things (IoT) services, 5G is required to have the features of low cost, low power consumption, and being safe and reliable [1, 2]; 5G will enable information and communication to exceed the time and space constraints, greatly shorten the distance between people and things, and quickly realize the interoperability of human and all things [3]
Mobile cloud computing is a new model of delivery and usage of IT resources or information services; it is a product of cloud computing in the mobile Internet; mobile smart terminals in mobile networks are connected to remote service providers in an on-demand and scalable way to obtain the necessary resources, mainly including infrastructure, computing, storage capacity, and application resources [5, 6]
Summary
Received 25 January 2018; Revised 5 April 2018; Accepted 17 April 2018; Published 17 May 2018. With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly
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