Load Balancing Optimization of In-Memory Database for Massive Information Processing of Internet of Things (IoTs)

Abstract

Based on the analysis of the key technologies of the Internet of Things service platform architecture, a load balancing optimization scheme of in-memory database based on the massive information processing of the Internet of Things service platform is proposed. This scheme firstly proposes a system model that can satisfy the mass sensor information processing under the open platform environment and designs several functional unit modules of the system. By combining these functional units, the service can be configured for thousands of services and tenants. This paper presents an adaptive strategy selection method, which can automatically select the optimization strategy by dividing the position and querying the selection rate to improve the efficiency of the adaptive index algorithm. The index structure is initialized by parallel sorting algorithm, and the query statement is executed and the index structure is optimized by thread level parallel and radix sort methods. An elastic pipeline technique is proposed, which includes an elastic iterator model and a dynamic scheduler. The elastic iterator model is an upgrade of the traditional iterator model, adding the characteristics of dynamic multicore execution. In the process of query processing, dynamic scheduler monitors the load of each node in real time and dynamically adjusts the parallelism, so as to realize the load balance of in-memory database and maximize the utilization of hardware resources. The elastic pipeline realizes the isolation of parallelism from query compilation to avoid inappropriate parallelism allocation caused by missing and insufficient information during query compilation.

Enhancement of Quality of Service (QoS) in Internet of Things based on big data environment using the Harris Hawks algorithm

“…Wei [5] proposed a IoT service platform for load balancing in the memory of database using the position and querying selection. In order to achieve load balancing in-memory database and maximize the use of hardware resources, the dynamic scheduler continuously tracks the load on each node while query processing is underway.…”

“…While the IoT network mainly gathers the data from various sensors, big data would authorize more rapid and efficient storage and processing. The five elements that have been used to characterize big data are, (1) the volume, respectively the size of data handled;(2) the variety, that implies the different types of data gathered from multiple sources; (3) the acceleration, namely the quantity of data collected in real-time; (4) the veracity, that shows the uncertainty of data; (5) the value, that is used in various industrial and academic fields. Big data may be presented as a considerable number of structured, semi-structured, and unstructured elements.…”

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

“…In the Internet of Things, a capillary computing architecture for orchestrating microservices from edge devices to cloud computing providers is suggested in reference [11]. An Edge-Fog-Cloud environment is described as a distributed cloud for IoT computing in reference [12]. The authors also review a cloud computing offloading strategy for simultaneous localization and mapping of indoor mobile robots in reference [13].…”

Retracted: Load Balancing Optimization of In‐Memory Database for Massive Information Processing of Internet of Things (IoTs)

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”