Data Center Resource Allocation Strategy Based on Edge Computing

Abstract

In recent years, Mobile Edge Computing (MEC) technology can support resource-intensive applications in edge networks. With the expansion of cloud computing service models in edge networks, it provides users with real-time services, which solves traditional cloud computing the high latency barrier when the center provides services is a new technology with very broad application prospects. This article aims to study the data center resource allocation strategy of edge computing. This paper designs an edge server experiment to make full use of the edge computing model to mine the computing power of the edge terminal in the network, perform some or all calculations at the edge terminal, process private data, and reduce the computing, transmission bandwidth load and energy consumption of the cloud computing center. The concept of edge computing was proposed only after the development of cloud computing technology for a period of time. At present, edge computing and cloud computing technology are complementary to each other. As the technical basis for building an interconnected environment of all things, this paper studies in detail the use of mobile edge computing technology to provide users with real-time application services The architecture of this service model analyzes the challenges faced by the service model and proposes an effective solution, which improves the efficiency of resource allocation by nearly 45.73%.

Enabling Robust Sensor Network Design with Data Processing and Optimization Making use of Local Beehive Image and Video Files

“…Wi-Fi sensor network topologies that are enhanced by edge computing show promise [3,4]. Edge computing overcomes the drawbacks of traditional cloud-based solutions by decreasing latency and boosting bandwidth efficiency; this is especially helpful in environments with limited resources.…”

“…In order to lessen the computing load of data processing and analysis, sampling algorithms choose a subset of data points from a larger dataset. However, there is limited research on the application of edge computing [3,4] to data optimization in various robust sensor networks for resource-constrained environments.…”

“…These limitations impede the development of Internet of Things (IoT) applications. A new technology that arises to address these issues is edge computing [3,4].…”

“…As a result, data transmission and storage efficiency are increased, which is important in places with limited resources. Edge computing [1,3] has the transformative potential to improve decision-making and promote efficiency across diverse IoT applications by enabling practitioners to collect and analyze more extensive data. By monitoring environmental indicators like temperature, humidity, and activity, practitioners can use edge computing to detect abnormalities and problems early on.…”