Rademics Research Institute

Abstract

Cloud-based predictive systems are increasingly relied upon for efficient decision-making in a variety of domains, including security analytics, resource management, and data-driven optimization. The scalability and cost-efficiency of these systems depend heavily on effective resource allocation, performance optimization, and data management. This chapter explores advanced strategies for optimizing cloud resources in predictive systems, with a focus on leveraging machine learning, AIOps, and self-optimizing architectures. It delves into the challenges and solutions associated with automating cloud resource management, particularly in the context of security analytics, where real-time processing and high availability are paramount. Techniques such as caching, data preprocessing, and predictive scaling are examined as key enablers of resource efficiency, reducing costs while enhancing system performance. Additionally, the integration of AIOps for dynamic resource optimization was discussed, illustrating how intelligent systems can proactively scale and adjust cloud infrastructure in response to fluctuating demands. The chapter provides a comprehensive overview of these cutting-edge approaches, highlighting their application in cloud-based security analytics and offering practical insights into achieving both operational efficiency and cost-effectiveness.

Introduction

Cloud-based predictive systems have become an indispensable part of modern technological infrastructures [1]. These systems leverage advanced algorithms and massive computational power to perform real-time analysis, generate insights, and enable data-driven decision-making [2]. With the exponential growth in data generation across industries, traditional computing models are no longer sufficient to manage and process such volumes of information [3]. Cloud platforms, with their scalability and flexibility, provide the ideal environment for deploying these predictive models, offering high availability and computational resources on demand [4]. A key application of these systems was in security analytics, where real-time data processing was crucial for

detecting threats, identifying vulnerabilities, and responding to security breaches beforeescalate [5]. As organizations increasingly rely on cloud-based solutions for predictive analytics, optimizing resource management becomes a critical concern [6]. Efficient resource allocation not only ensures that cloud systems perform optimally but also contributes significantly to reducing operational costs, thereby improving the overall sustainability of the infrastructure [7]. One of the most pressing challenges in cloud-based predictive systems lies in managing resources effectively [8]. Traditional resource management techniques, such as static allocation and manual adjustments, are often ill-suited for dynamic and data-intensive applications like predictive analytics [9]. These methods fail to respond in real-time to fluctuations in demand, leading to issues such as resource overprovisioning or underprovisioning, both of which can severely affect system performance and cost efficiency [10]. In the context of security analytics, where data streams can be unpredictable and variable, the ability to scale resources on-demand and adapt to ever-changing workloads was crucial [11]. Security events like DDoS attacks or sudden data influxes can place considerable strain on computational resources, necessitating an agile system that can adjust dynamically to meet these demands [12]. In highly competitive industries, the need for continuous service availability and low-latency processing amplifies the importance of optimizing cloud resources to avoid delays and disruptions in critical services [13]. To address these challenges, cloud-based predictive systems require more sophisticated, automated approaches to resource management [14].