2026· Journal of Advances in Information Technology· 0 citations· 133 references
TL;DR
A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.
Abstract
—As cloud computing and data centers become integral to global Information Technology (IT) infrastructure, optimizing routing and resource management in these networks is critical for maintaining performance, scalability, and energy efficiency. This paper presents a structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025. Key advancements include adopting Software-Defined Networking (SDN), machine learning-based routing algorithms, and energy-efficient resource allocation strategies. This paper critically analyzes the challenges posed by scalability, latency, energy consumption, security, and interoperability, alongside the opportunities presented by Artificial Intelligence (AI)-driven autonomous networks, edge computing, and the integration of 5G technologies. Comparative evaluation highlights key trade-offs between performance gains and real-world feasibility, particularly for machine learning and deep reinforcement learning approaches. Furthermore, the study examines emerging trends, including cloud-edge collaboration and multi-objective optimization frameworks. The findings reveal that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations. Overall, this review provides a consolidated perspective on current approaches, open challenges, and promising research directions for next-generation cloud and data center network optimization.
It is concluded that AI is not merely an enabler but a cornerstone for realizing sustainable and intelligent 6G networks, paving the way for an eco-friendly digital future.
Joshna M, R. K.· Journal of Artificial Intell...· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
The rapid expansion of cloud computing, big data analytics, and artificial intelligence has positioned data centers as the backbone infrastructure of modern computing. Data center networks (DCNs) play a critical role in determining overall system performance and reliability. Existing DCN architectures face limitations, such as difficulties in balancing throughput and latency, insufficient fault-tolerance, and high expansion costs. To address these challenges, we propose ACDC (Augmented Cube-based Data Center), a novel server-centric DCN topology based on augmented cubes that achieves superior performance while maintaining cost-effectiveness through exclusive use of dual-port servers and low-port commodity switches. Firstly, we analyze the key features and properties of ACDC, with a focus on scalability and network diameter, establishing rigorous theoretical foundations for the proposed architecture. Secondly, we present comprehensive routing algorithms including ARouting for fault-free scenarios and AFR for fault-tolerant communication. Finally, extensive experimental evaluations demonstrate ACDC’s superior performance compared to state-of-the-art DCN architectures. Experimental results show that ACDC achieves a network diameter approximately 75% smaller than HSDC and 50% smaller than AQDN. Furthermore, ACDC maintains comparable throughput to the Fat-Tree under random traffic scenarios, while demonstrating substantial advantages under high-density all-to-all communication patterns, achieving at least 69.1% improvement in average throughput and at least 40.7% reduction in flow completion time compared to AQDN, DCell and FiConn. These confirm that ACDC strikes a good balance among performance, cost-efficiency, scalability, and fault-tolerance in contrast to the state-of-the-art DCN architectures.
Xuanli Liu, Weibei Fan, Zhenjiang Dong et al.· IEEE Transactions on Network...· 0 citations
Experiments conducted on real-world microservice workload traces demonstrate that the proposed L7-COLO framework significantly improves cloud resource efficiency and cost control, demonstrating its effectiveness for enterprise cloud cost optimization.
Energy remains the most critical and limiting resource in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) systems, directly constraining network lifetime, scalability, and real-world deployability. Although multi-hop routing is widely adopted to reduce transmission energy and balance traffic load, recent solutions increasingly rely on metaheuristic optimization and machine learning techniques whose computational, control, and learning overhead is rarely accounted for. This leads to a fundamental energy–intelligence trade-off that challenges the sustainability of intelligent routing in resource-constrained environments. This paper presents a critical, energy-centric review of multi-hop routing approaches for IoT and WSNs proposed between 2018 and 2025. Heuristic, metaheuristic, dynamic and Heterogeneous routing, reinforcement learning, deep reinforcement learning, and explainable AI-based protocols are systematically analyzed with an emphasis on net energy efficiency, scalability, feasibility on constrained devices, and model realism, rather than reported performance gains alone. The analysis reveals that energy is predominantly treated as a secondary optimization objective rather than as a governing system constraint. To address this limitation, we propose a hybrid and explainable routing framework governed by energy awareness, in which intelligence activation is explicitly conditioned on its net energy benefit. This perspective provides a principled foundation for sustainable and trustworthy intelligent routing in next-generation IoT and WSN systems.
Moez Elarfaoui, Hamdi Ouechtati, Nadia Ben Azzouna· International Conference on...· 0 citations
Today, with the rapid growth of the Internet of Things (IoT), the volume of data generated and processed has increased significantly, and there is an urgent need to handle tasks quickly and efficiently in real‐time applications. Existing cloud‐based models are not capable of meeting these demands due to their deployment model that adds latency. The separation of the control and data planes has the potential to provide a solution to Software‐Defined Networking (SDN) that offers centralised control, programmability, and dynamic network management. However, efficient scheduling of activities and workloads among the different edge servers is one of the imperative issues since an inefficient allocation of the resources may lead to server congestion, high latency rates, and resource wastages. To overcome this, the article presents the Average‐Based Load Balancing and Resource Allocation Mechanism (ALBRAM), which identifies suitable nodes and dynamically allocates resources to maintain the load balance among all the servers. It recommends a three‐layer SDN‐based edge computing architecture providing a bridge between the IoT devices, middle nodes, and the edge servers, considering both the communication and computation time. The strategy uses a least‐load server selection mechanism to provide fairness, optimal resource utilisation, and balanced workloads. The results of the evaluation indicate that ALBRAM decreases the Makespan, total completion time and latency and increases resource utilization, and the efficiency of load‐balancing in comparison to existing methods.
Ajay Nain, Rohit Malik, Sophiya Sheikh et al.· Concurrency and Computation· 0 citations