A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

Abstract

With the rapid development of the Internet of Things (IoT) technology, diversified applications deploy extensive sensors to monitor objects, such as PM2.5 air quality monitoring. The sensors transmit data to the server periodically and continuously. However, a single server cannot provide efficient services for the ever-growing IoT devices and the data they generate. This study bases on the concept of symmetry of architecture and quantities in system design and explores the load balancing issue to improve performance. This study uses the Linux Virtual Server (LVS) and virtualization technology to deploy a virtual machine (VM) cluster. It consists of a front-end server, also a load balancer, to dispatch requests, and several back-end servers to provide services. These receive data from sensors and provide Web services for browsing real-time sensor data. The Hadoop Distributed File System (HDFS) and HBase are used to store the massive amount of received sensor data. Because load-balancing is critical for resource utilization, this study also proposes a new load distribution algorithm for VM-based server clusters that simultaneously provide multiple services, such as sensor services and Web service. It considers the aggregate load of all back-end servers on the same physical server that provides multiple services. It also considers the difference between physical machines and VMs. Algorithms such as those for LVS, which do not consider these factors, can cause load imbalance between physical servers. The experimental results demonstrate that the proposed system is fault tolerant, highly scalable, and offers high availability and high performance.

Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service

“…It can balance software using TCP and UDP protocols. The software can work in three modes: NAT, IP Tunneling, and Direct Routing, and they differ in the way they process and forward segments to backend servers [34][35][36][37].…”

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

“…Through Virtualization, these underused servers will be consolidated into physical servers. The technology changed in the processors, increased the support for VM virtualization and advanced the use of more underused systems and servers [7].…”

“…For example, in recent studies, 128 gigabytes (GB) main memory and 16 processors in commercial servers, Processors only account for 28 percent of the total energy demand and 41 percent of memory. Therefore, the main factor in creating energyefficient servers is to reduce memory energy consumption [7].…”