Jul 2026· Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering· 0 citations· 20 references
TL;DR
The primary innovation of this research is the development of an adaptive switching framework that integrates Clouded Leopard Optimization for robust global exploration with Cock-hen-chicken Optimization for hierarchical local refinement.
Abstract
Traditional Kubernetes microservice deployment methods often suffer from static resource allocation, leading to inefficient resource usage, high operational costs, and an inability to handle dynamic service dependencies in real-time. To address these challenges, this framework proposes a Hybridized Clouded leopard and Cock-hen-chicken Optimization (HCCO) model. The primary innovation of this research is the development of an adaptive switching framework that integrates Clouded Leopard Optimization (CLO) for robust global exploration with Cock-hen-chicken Optimization (CHCO) for hierarchical local refinement. The rationale for selecting this hybrid approach over other meta-heuristics, such as the Black Hole Algorithm, is its superior ability to avoid premature convergence in high-dimensional search spaces; while the Black Hole Algorithm often struggles with maintaining diversity during rapid resource shifts, HCCO utilizes a dynamic switching variable to ensure a balanced and faster search process. Experimental validation shows that the developed work achieves significant numerical improvements, including a throughput of 91.62%, a resource usage cost reduction of 9.61%, and a minimized execution time of 17.9 min. Ultimately, the HCCO framework provides a highly efficient and scalable solution for optimizing cost and performance in dynamic, real-time cloud environments.
The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources, confirming the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.
H. Merouani, S. Bendib, H. Moumen et al.· Revista Internacional de Mét...· 0 citations
A hybrid scheduling framework that integrates Hybrid Wild Goose Optimization (HWGO) with Deep Reinforcement Learning (DRL) is investigated, indicating that intelligent hybrid optimization techniques can provide adaptive and efficient task scheduling solutions for modern cloud computing environments.
Annaiah H, A. Rajesh· International journal of com...· 0 citations
The convergence of microservice architectures and serverless computing promises an elastic and cost-efficient model for modern cloud applications that often span multiple geo-distributed regions. However, prevailing serverless orchestrators that prioritize resource utilization or simple cold-start mitigation often prove suboptimal concerning SLO compliance and cost-efficiency in this emerging use case. In this paper, we present Krysha, an adaptive orchestration framework that jointly optimizes function scheduling and resource allocation for geo-distributed serverless microservices. Krysha employs a novel bi-level scheduling strategy: global-level early-binding to regions for fast function dispersion, coupled with regional-level late-binding to compute nodes for optimized resource use and cost. Moreover, Krysha achieves fine-grained resource allocation by decoupling CPU and memory provisioning and applying in-place vertical scaling on individual function instances. These capabilities are guided by a comprehensive cost model and practical online optimization techniques. Our extensive evaluation shows that Krysha can achieve up to 74.7% cost savings in scaled deployments compared to state-of-the-art alternatives while maintaining SLO requirements.
Yuqiu Zhang, Hans-Arno Jacobsen· IEEE International Symposium...· 0 citations
This paper proposes the Adaptive Memetic-Guided Slime Evolution Algorithm (AMGSEA) for multi-objective task scheduling in large-scale Infrastructure-as-a-Service (IaaS) cloud environments. Unlike conventional hybrid metaheuristics that rely on static operator integration, AMGSEA introduces a feedback-driven adaptive framework that dynamically balances exploration and exploitation. The proposed method combines oscillatory global search from the Slime Mould Algorithm, Differential Evolution-based adaptive guidance, and a selective memetic local search applied only to elite non-dominated solutions. The key novelty lies in (i) adaptive activation of memetic refinement based on Pareto dominance, (ii) feedback-controlled evolutionary guidance to prevent premature convergence, and (iii) an elite re-injection strategy for diversity preservation. Extensive experiments using CloudSim with PlanetLab traces and synthetic workloads of up to 5,000 tasks demonstrate that AMGSEA achieves up to
14.6% reduction in makespan
,
11.2% reduction in execution cost
, and improved energy efficiency compared to seven state-of-the-art schedulers. Additionally, the method improves deadline satisfaction to
97% compliance
and increases hypervolume by an average of
8–12%
. Statistical validation using Wilcoxon signed-rank tests confirms the significance of the improvements. These results establish AMGSEA as a scalable, QoS-aware, and energy-efficient scheduling framework suitable for dynamic and heterogeneous cloud environments.
R. Nithiavathy, P. Mansingh, R. Nallakumar et al.· International Journal of Com...· 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
: The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates a greater than 240% relative improvement ( + 42.2 percentage points) in virtual machine utilization over load-scattering metaheuristics and avoids the premature policy convergence observed in Deep-DQN baselines. For large-population offline optimization configurations ( P ≥ 5000 individuals), the distributed architecture achieves a 5.85 times sub-linear Amdahl speedup; below this population threshold, including the P = 20 configuration used for online, per-task scheduling, thread-pool context-switching overhead dominates and distributed partitioning does not improve wall-clock latency. The results empirically quantify the necessary tradeoff between aggressive hardware consolidation and Service Level Agreement preservation, establishing Fuzzy-SLW as a scalable solution for power-constrained hyper-scale environments.
Nidhi Chauhan, Navneet Kaur, Jawad Khan et al.· Computers, Materials & C...· 0 citations