Open Access
Survey of Secure Workload Scheduling and Management in Cloud Computing Systems
4
System Analyst, MITS Deemed University, India
Abstract
Cloud computing provides scalable, on-demand computing resources for a wide range of applications, making efficient workload scheduling and management essential for optimizing resource utilization, reducing execution time, and ensuring Quality of Service (QoS) and Service Level Agreement (SLA) compliance. However, the distributed and multi-tenant nature of cloud environments introduces security challenges such as unauthorized access, data breaches, malicious workloads, and resource misuse. This survey presents a comprehensive review of secure workload scheduling and management techniques in cloud computing systems. It discusses the fundamentals of workload scheduling, including cloud architectures, service models, deployment models, and workload management concepts. The survey reviews secure scheduling approaches, including static and dynamic scheduling, heuristic and metaheuristic algorithms, Artificial Intelligence (AI)- and Machine Learning (ML)-based techniques, and load balancing strategies. It also examines key security mechanisms such as authentication, authorization, encryption, trust management, workload isolation, and intrusion detection for protecting cloud workloads. Furthermore, the survey summarizes recent research contributions, identifies existing limitations, and highlights open challenges, including scalability, multi-cloud security, real-time scheduling, edge–cloud integration, green cloud computing, and Zero-Trust security. Finally, it outlines future research directions for developing intelligent, secure, and efficient workload scheduling frameworks in modern cloud computing environments.
Keywords
Cloud Computing,
Secure Workload Scheduling,
Management
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