OVERVIEW OF LOAD FORECASTS IN THE CLOUD ENVIRONMENT

Abstract

In a cloud computing environment, resource scheduling and load prediction are closely related concepts that are closely related to each other. The cloud environment is a virtualized, elastic computing platform that provides various computing resources to users through the network. The cloud environment can provide high scalability and flexibility to meet user demand for computing resources. Load prediction is based on historical data and models to predict and estimate the load in the cloud environment. Load usually refers to the user's demand for computing resources, such as CPU utilization, memory usage, network traffic, etc. The purpose of load prediction is to understand the future load situation in advance in order to plan and schedule resources in the cloud environment reasonably. Resource scheduling is the process of reasonably allocating and managing computing resources in a cloud environment. It is based on the results of load prediction, according to user needs and performance goals, dynamically allocating resources such as computing instances and storage to different tasks and users. The goal of resource scheduling is to 
optimize resource utilization, improve system performance, ensure service quality and meet user needs. Therefore, the cloud environment, resource scheduling and load prediction are interdependent. Load prediction provides information about future load conditions and provides a basis for resource scheduling decisions. Resource scheduling dynamically adjusts resource allocation based on load prediction results to meet the needs of different tasks and users. Through the coordinated work of load prediction and resource scheduling, resource utilization, performance and user experience in the cloud environment can be optimized. It should be noted that load prediction and resource scheduling are dynamic processes. Due to the uncertainty and variability of the load, prediction and scheduling need to be continuously monitored and adjusted to adapt to real-time demand and environmental changes. Therefore, load 
prediction and resource scheduling are important research and technical fields in the cloud computing environment, 
which are essential for improving the efficiency and performance of cloud services.

Keywords: Cloud environment, decision basis, resource scheduling, resource allocation, load prediction