The rapid growth of geographically distributed cloud data centers has intensified the demand for secure, privacy-preserving, and resource-efficient collaboration among mutually untrusted cloud environments. This paper presents BlockFed, a blockchain-empowered federated learning framework designed to enable cyber-threat-resilient collaboration across geographically distributed data centers. In BlockFed, each data center independently trains local models using private workload data and shares only the computed gradients rather than raw data, with a centralized aggregation server through a secure blockchain layer. A Proof-of-Work consensus mechanism is employed to validate gradient transactions and maintain an immutable, tamper-resistant ledger, ensuring trust and integrity among participating entities. The centralized server aggregates blockchain-verified updates to construct a global model, which is iteratively redistributed to support collaborative learning. This integrated learning process enhances task-level resource demand prediction while simultaneously improving system security and operational efficiency across all participating data centers. Extensive simulations on the Google Cluster Dataset show that BlockFed outperforms state-of-the-art baselines, including ISTM, ETP-WE, First-Fit, Best-Fit, and Random-Fit, in resource utilization, power consumption, and active server reduction. BlockFed achieves up to 7-81% higher resource utilization, 41-58% lower power consumption, and 31-65% fewer active servers, while attaining a cyber-threat estimation accuracy of 93.19%.

Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This article provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, this article discusses the tradeoffs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and SRM. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.

Large-scale computing systems in the modern era distribute tasks into smaller units that can be executed simultaneously to speed up job completion and decrease energy usage. However, cloud computing systems encounter a significant challenge called the Long Tail problem, where a small subset of slow-performing tasks hinders the overall progress of parallel job execution. This behavior leads to longer service response times and reduced system efficiency. This paper introduces a novel approach called Stochastic Gradient Descent with Momentum-driven Neural Network to analyze and classify heterogeneous tasks as either stragglers or non-stragglers. The straggler tasks are further categorized into Resource Hunter and Long-Tail stragglers based on their specific resource requirements. A traffic management policy is implemented to schedule and assign resources among user job requests, considering the task category, to achieve parallelism and improve sustainability within the cloud infrastructure. Extensive simulations are conducted using the Google Cluster Dataset (GCD) to assess the effectiveness of the proposed framework. The results obtained from these simulations are then compared to state-of-the-art techniques. The experimental findings demonstrate significant reductions in power consumption, carbon emissions, active servers, conflicting servers, and VM migration up to 55.16%, 49.76%, 35%, 25.7%, and 87.29%, respectively. Moreover, there has been an enhancement in resource utilization by up to 78.31%, accompanied by a decrease in execution time of up to 67.74%.