Jul 2026· Fundamental Scientific Reports in Multidisciplinary Areas· Vol 2, pp. 235-249· 0 citations
TL;DR
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.
Abstract
Modern enterprise cloud applications increasingly rely on microservice architectures and container orchestration platforms, where Layer-7 (L7) load balancing plays a critical role in routing application-level traffic among service instances. Conventional L7 routing strategies—such as round-robin or least-connection—are primarily designed to improve performance and availability, but they rarely consider the impact of routing decisions on cloud resource consumption and operational cost. In large-scale SaaS platforms, e-commerce systems, and financial cloud services, inefficient traffic distribution may trigger unnecessary container scaling, bandwidth overhead, and suboptimal resource utilization, leading to significant increases in cloud service costs. To address this challenge, this paper proposes L7-COLO (Layer-7 Cost-Optimized Load Optimization), a cost-aware intelligent load balancing framework that integrates deep traffic prediction with dynamic routing optimization for enterprise cloud environments. The proposed approach constructs a Traffic–Resource–Cost (TRC) mapping model that captures the relationship between predicted application traffic, infrastructure resource usage, and cloud operational cost. A CNN-BiLSTM-Attention model is first employed to forecast future API request traffic, followed by a resource estimation module that predicts CPU, memory, bandwidth, and container scaling requirements under different routing strategies. Based on the predicted resource usage, a cloud cost model evaluates the expected operational cost, while a reinforcement learning policy optimizer dynamically adjusts L7 routing weights to minimize cost under service-level agreement constraints. Experiments conducted on real-world microservice workload traces demonstrate that the proposed L7-COLO framework significantly improves cloud resource efficiency and cost control. Compared with traditional Round-Robin routing, L7-COLO reduces overall cloud operational cost from 18.9 USD/min to 12.9 USD/min, achieving approximately 31.7% cost reduction, while also improving resource utilization to 78.4%. Meanwhile, the framework maintains stable service performance with an average latency of 155 ms and reduced SLA violation rates, demonstrating its effectiveness for enterprise cloud cost optimization.
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.
S. Alanazi· Journal of Advances in Infor...· 0 citations
A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.
Anshul Atre, K. Singh, B. Chaurasia et al.· Journal of Circuits, Systems...· 0 citations
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions.
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations
Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.
Bekzat Kobei, N. Seilova, Zarina A. Kashaganova· AI@DTESI· 0 citations
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.
Eram Fatma, Nidhi Mishra, Mohammed Abdul Bari· Journal of Intelligent Decis...· 0 citations
Managing containerized workloads in cloud-native infrastructures poses complex challenges due to the need to simultaneously balance performance, efficiency, and sustainability. This work proposes an adaptive resource allocation framework that leverages Digital Twins for real-time system monitoring and integrates Large Language Models to support context-aware decision-making under multi-objective constraints. The proposed approach dynamically optimizes latency, bandwidth utilization, and energy consumption, enabling intelligent workload orchestration across heterogeneous data center environments. A flexible utility function is introduced to allow system operators to adjust trade-offs between responsiveness and environmental impact. Experimental results demonstrate that the framework consistently outperforms traditional heuristic and learning-based baselines, achieving higher allocation accuracy, improved network utilization, and faster workload completion, while reducing overall energy consumption by more than 20% in sustainability-oriented scenarios. These findings highlight the potential of combining digital twins-driven observability with large language model-based reasoning to enable interpretable, adaptive, and energy-efficient resource management in next-generation cloud computing environments.
Pedro Henrique Sachete Garcia, A. Lorenzon, M. Luizelli et al.· SN Computer Science· 0 citations
