Modern large-scale computing systems improve job completion and energy efficiency by executing tasks in parallel. However, the Long Tail problem where a few slow-performing tasks, or stragglers, delay overall execution remains a key challenge in cloud computing. This paper introduces an Energy-aware Straggler Task Management Framework for sustainable cloud environments. The framework integrates predictive analytics with an evolutionary Neural Network to identify stragglers based on historical resource usage. A self-adaptive differential evolution algorithm is employed to optimize the neural network weights, enhancing prediction accuracy. Tasks are classified as stragglers or non-stragglers, with stragglers further divided into Resource Hunter and Long-Tail categories. A resource management policy dynamically assigns resources based on task type to enhance parallelism and sustainability. Extensive simulations were performed using the Google Cluster Dataset to analyze the effectiveness of the EaSTM framework. The obtained results highlight the framework's potential for improving efficiency and sustainability in cloud environments.

The multi-tenant resource-sharing environment of cloud computing among multiple users and vulnerabilities of virtual machines (VM)s and physical machines prompt unauthorized access to users’ confidential  data and excess power consumption and resource wastage. This paper presents a comparative survey study of securing cloud resource strategies to address this critical issue. Specifically, this study focuses on Defensive Strategies associated with cloud resource allocation with their pandect comparative summary.

Cloud computing (CC) has become the best way to manage a large-scale computing paradigm. Resource management (RM) in the cloud environment is critical due to the heterogeneity of computing resource types and their inter-dependencies. Recently, significant research has been done on RM techniques focusing on efficiently sharing resources among multiple users. Machine learning techniques manage various resource management functions, such as VM consolidation, resource optimization, energy optimization, workload estimation, and task scheduling. This article contributes a comprehensive and concise review of RM approaches within cloud environments. Moreover, it suggests design goals and research challenges that must be considered while developing novel RM strategies.

Security is always a prime concern of every cloud user. Many pioneer surveys have been done concerning security during data communication among owners, users, and stakeholders. However, there is a lack of literature surveys that address the concern of security during data processing or the workload execution within cloud data centers. In this context, we present a concise review of the existing methods that have addressed the security of workload or user data during load scheduling and execution within cloud environments. These methods have applied load-balancing strategies that helped to minimize the security attacks via co-residency or covert channels. The pandect comparative summary of these methods follows the corresponding review. The paper is concluded with limitations and future direction concerning secure load management.

This article comprehensively studies data security in cloud computing environments and presents ideas for solving everyday computing issues. Cloud Computing (CC) is a shared pool of resources that allocates those computing resources to cloud clients over the internet. CC provides a set of controls that act as controls between cloud users and storage. Storage has played a crucial role in the cloud and has become ubiquitous among organizations and clients. Nevertheless, various issues such as security, information protection, interoperability, and consistent quality should be defined appropriately. However, the underlying problem is data security and how cloud providers ensure it. Safe and secure protection technology is indispensable for exchanging information between clouds. Encryption technology is used to provide data security in cloud environments. Numerous cryptography algorithms exist to transform plain text to cipher text before placing the data in the cloud to ensure security.

A Bio-VMP scheme is presented for allocating user’s $VM$ based on maximum resource utilization, minimum power consumption as well as carbon emission. To the best of the author’s knowledge, this is the first work that combines the ideas of FPO along with NSGA-II to solve the VMP problem.

The exponential growth of cloud computing within the last decade has led to a severe consideration about the power requirement of the cloud data center. However, variations in the computing resource demands and fixed size of virtual machines (VMs) lead to a poor resource utilization, load imbalance, and excessive energy consumption within the data center. Dynamic VM consolidation effectively handles these problems, where VMs are placed on as few physical machines (PMs) as possible. Concurrently, VM allocation must be done strategically by considering various factors of the computing resources for minimal exploitation of them. The high frequency of VM consolidation may lead to the degradation of the system’s reliability by assigning VMs to unreliable PMs. A novel Quantum Machine learning driven Reliable Resource Allocation (QM-RRA) model is proposed to address these problems and enhance the data center’s reliability. We handle the problem by applying an Evolutionary Quantum C-Not Neural Network (QCNN) unit to forecast future resource usage at the cloud data center. In addition, Quantum Evolutionary Adaptive Algorithm (QEAA) is utilized to optimize qubit network weights. It offers energy saving by minimizing the number of active PMs, maximizing resource utilization, and enhancing the reliability of the overall data center. The QM-RRA model is evaluated by performing experiments on the Google Cluster Data set (GCD). The observed results are compared with the state-of-art methods and shows its supremacy in terms of different performance parameters like resource utilization, power consumption, and the number of active PMs. There is a significant reduction in power consumption and the number of active PMs up to 31.38%  and 44.93%, respectively. The enhancement in resource utilization is up to 25.61%, and overall system reliability is 63.35%.