Jul 2026· International Journal of Advanced Research in Science, Communication and Technology· 0 citations· 10 references
TL;DR
The survey finds that the need of the hour is to incorporate resource optimization along with security measures into next-generation multi-cloud computing environments.
Abstract
Multi-cloud computing has evolved into a sophisticated model that enables organizations to leverage services from multiple cloud providers to enhance scalability, flexibility, reliability, and continuity. While the technology offers many benefits, there are numerous challenges related to resource optimization and security in heterogeneous cloud infrastructure, given its dynamic nature, diverse service models, varied pricing schemes, and increasing cyberattacks. A comprehensive overview of the resource optimization and security approaches in multi-cloud computing. First, it provides a brief discussion of the basics of multi-cloud computing and its architecture and deployment models. Then, it analyzes some of the most popular resource optimization approaches, which include resource allocation, workload scheduling, load balancing, dynamic resource provisioning, auto-scaling, virtual machine and container optimization, cost-aware optimization, energy-aware resource management, AI-driven optimization, and metaheuristic approaches. Moreover, it provides an overview of the popular security techniques, namely identity and access management, encryption, Zero Trust Architecture, intrusion detection and prevention systems, secure data replication, network security, blockchain-based security, and continuous monitoring. The study of recent literature on the topic brings out the issues, both pros and cons, involved in the area of resource optimization and security. The survey finds that the need of the hour is to incorporate resource optimization along with security measures into next-generation multi-cloud computing environments.
Multi-cloud computing is becoming a prominent paradigm to improve scalability, flexibility, reliability and costeffectiveness by leveraging services from multiple cloud providers. But distributed resource management with strong security is a big challenge in multi-cloud scenarios, which are heterogeneous and dynamic. This review paper provides an all inclusive overview on various multi-cloud architectures, deployment models,resource allocation techniques, optimization methods, and security assurance mechanisms. It covers the major resource allocation strategies such as provisioning, scheduling, load balancing, resource scaling and intelligent optimization through machine learning and metaheuristicalgorithms to optimize resource utilization and Quality of Service (QoS). Additionally, the article delves into significant security methods for protecting decentralized cloud systems, including authentication, authorization, encryption, intrusion detection, trust management, and zero-trust designs. Also, through the comparison of the most recent literature, the current research trends, challenges and limitations for optimizing resources while keeping security in mind are pointed out. According to the review, combining AI-powered optimisation with sophisticated security frameworks has the potential to enhance the performance, resilience and reliability of multi-cloud environments. Last but not least, the paper outlines future research avenues for explainable AI, federated learning, blockchain-based trust management, energy-efficient resource allocation, and autonomous cloud orchestration to enable secure, scalable, and sustainable next-generation multi cloud computing environments.
A. Jain· Journal of Global Research i...· 0 citations
The design and development of DynamiCloud is presented, a scalable and computationally efficient multi-objective dynamic resource allocation model for cloud computing that can simultaneously optimize multiple conflicting objectives such as throughput, Service Level Agreement compliance, and power efficiency.
Onwuegbuchulem Gift., Bennett, E.O., Matthias D. et al.· Journal of Artificial Intell...· 0 citations
This paper explores sophisticated Virtual Machine (VM) scheduling approaches in cloud computing and their significance to enhance resource distribution, improve system efficiency, and cost reduction. It provides a recent overviews of key scheduling algorithms, including heuristic, metaheuristic, advanced machine learning-based and hybrid approaches, while assessing their respective strengths, weaknesses and practical applications. The discussion encompasses their applications in managing workloads, optimizing costs, enhancing energy efficiency, improving Quality of Service (QoS) and with a particular focus on scalability and real-time scheduling in cloud settings. Furthermore, the paper analyzes scheduling strategies adopted by major cloud providers through real-world case studies. Ultimately, our analysis identifies critical VM scheduling trade-offs, provides optimization guidelines, and validates the efficacy of hybrid adaptive methods via a new proposed heuristic-machine learning model for dynamic cloud environments.
Chaimae Bahij, Mohamed El Ghmary, Hassan Echoukairi· EPJ Web of Conferences· 0 citations
This study presents an efficient algorithm for large-scale resource management in a multi-tenant cloud environment that integrates intelligent resource scheduling, workload balancing and adaptive virtual machine allocation to optimize resource utilization while satisfying multiple performance objectives.
Onwuegbuchulem Gift., Bennett E.O., M. D. et al.· International journal of re...· 0 citations
Cloud computing has become a fundamental paradigm for delivering scalable, flexible, and cost-effective computing resources through Internet-based platforms. Its rapid adoption across various sectors has enhanced operational efficiency, resource utilization, and service delivery. Despite these advantages, cloud environments continue to face significant security challenges that threaten the confidentiality, integrity, and availability of data and services. This review examines the major security threats affecting cloud computing environments, including data breaches, account hijacking, insider threats, insecure application programming interfaces (APIs), cloud misconfigurations, distributed denial-of-service (DDoS) attacks, multi-tenancy risks, and regulatory compliance issues. The study also analyzes the security implications associated with different cloud services and deployment models. Furthermore, it reviews contemporary security mechanisms and mitigation strategies, including encryption techniques, Identity and Access Management (IAM), Multi-Factor Authentication (MFA), Zero Trust Architecture (ZTA), Cloud Security Posture Management (CSPM), Artificial Intelligence-based security systems, DevSecOps practices, and Confidential Computing. In addition, the strengths and limitations of existing approaches are critically discussed, while emerging trends, research gaps, and future research directions are highlighted. By synthesizing recent developments in cloud security, this review provides researchers, practitioners, and decision-makers with a comprehensive understanding of current challenges and promising solutions for securing modern cloud computing environments.
Amat AL-latif H. Abo-Torkhoma, A. A. H. Abo-torkhoma, G. Ali· Emirates International Unive...· 0 citations
With the rapid development of edge computing, cloud computing, big data, artificial intelligence and the Internet of Things, traditional centralized cloud computing faces increasing challenges in latency, bandwidth pressure, resource utilization and data privacy protection. Edge-cloud collaboration provides a new computing paradigm by extending computing, storage and network resources from centralized cloud data centers to edge nodes closer to users and data sources. This architecture can improve service response efficiency and reduce data transmission pressure, but it also introduces heterogeneous resources, dynamic task requests, unclear security boundaries and privacy leakage risks. This paper reviews existing studies on edge-cloud collaboration, resource scheduling, intelligent optimization and privacy protection, and then conducts an analytical discussion of the research gaps rather than an experimental evaluation. The review shows that existing studies have achieved valuable progress in task offloading, resource allocation, deep reinforcement learning, access control, encryption and federated learning. However, research on integrated frameworks that combine intelligent resource scheduling with privacy-aware security management remains limited. To address this limitation, this paper proposes a formalized privacy-aware scheduling perspective that incorporates latency, energy consumption, cost, node trustworthiness, data sensitivity, privacy leakage risk, reliability and auditability into a unified decision model. The analysis indicates that future edge-cloud systems should evolve from efficiency-centric scheduling toward secure, trustworthy and sustainable collaborative governance.
Tian-Yun Luo· Frontiers in Computing and I...· 0 citations
