Aug 2026· SN Computer Science· Vol 7· 0 citations· 26 references
TL;DR
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, enabling intelligent workload orchestration across heterogeneous data center environments.
Abstract
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.
The current invention outlines a Java-driven framework for efficient resource management in cloud data centres using the CloudSim simulation environment. The framework presents a predictive auto-scaling mechanism that examines historical traffic patterns to forecast future workload requirements, allowing for predictive Virtual Machine (VM) al- location rather than traditional fixed threshold-based techniques. Prior to VM migration, the system assesses a Service Level Agreement (SLA) risk factor to avoid performance degradation and potential SLA violations through intelligent power management. A specific Green Scheduler Algorithm dynamically consolidates Virtual Machines by allocating workloads to optimally loaded physical machines based on fore- casted workload conditions. Machines with low utilization are automatically migrated across different power-saving states, such as idle, sleep, and deep sleep modes. This comprehensive framework strikes a balance between energy savings and the preservation of service reliability and Quality of Service (QoS). Simulation results demonstrate the effectiveness of energy savings, improved resource utilization, and SLA compliance, making it suitable for scalable and ecofriendly cloud resource management.
S. Divya, P. Venkadesh, G. Vasunthraa et al.· International Conference on...· 0 citations
The rapid expansion of cloud computing and large-scale data centers has significantly increased energy consumption and carbon emissions, creating critical sustainability concerns for modern computing infrastructures. This paper proposes the Adaptive Carbon-Aware Virtualized Energy-efficient Scheduling (ACAVES) framework to improve resource utilization and reduce environmental impact in cloud environments. The framework combines workload monitoring, task classification, virtual machine consolidation, carbon-aware scheduling, and energy optimization within an integrated architecture. An adaptive scheduling mechanism allocates workloads according to utilization patterns, energy requirements, and carbon emission estimates. Experimental evaluation was performed using heterogeneous workloads containing 10,000 tasks executed over 50 physical servers and 200 virtual machines. Results demonstrate that the proposed ACAVES framework reduced energy consumption from 520 kWh to 385 kWh and carbon emissions from 310 kgCO2 to 215 kgCO2. Additionally, server utilization improved from 68% to 87%, while average task completion time decreased from 820 ms to 670 ms, confirming the effectiveness and scalability of the proposed sustainable scheduling framework.
S. K, Kishore Bitra, Usha Desai· 2026 International Conferenc...· 0 citations
This study presents an efficient algorithm for large-scale resource management in a multi-tenant cloud environment that integrates intelligent resource scheduling, workload balancing and adaptive virtual machine allocation to optimize resource utilization while satisfying multiple performance objectives.
Onwuegbuchulem Gift., B. O., M. D. et al.· International journal of re...· 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, Brijesh Kumar Chaurasia et al.· Journal of Circuits, Systems...· 0 citations
Cloud computing has become a dominant paradigm for delivering scalable and flexible on-demand resources; however, efficiently executing high performance computing (HPC) workloads remains challenging, particularly in heterogeneous environments. Conventional static scheduling methods often lead to poor resource utilization and increased makespan, while dynamic approaches improve load distribution but introduce significant overhead due to continuous monitoring and real-time decision-making. To address these challenges, this paper proposes an SLA-aware Dynamic Enhanced Resource-Aware Load Balancing Algorithm (SLADE- RALBA). The algorithm minimizes load imbalance by considering the computational capacities of virtual machines and ensures Service Level Agreement (SLA) compliance through a three-tier priority-based workflow. The proposed approach is evaluated using CloudSim Plus on two benchmark datasets: Heterogeneous Computing Scheduling Problem (HCSP) instances and the Google Cloud Jobs dataset. Results demonstrate that SLA-DE-RALBA consistently outperforms baseline algorithms, including RALBA, DRALBA, DE-RALBA, SLA-RALBA, Dynamic Max- Min, PSSLB, and PSSELB, across key metrics such as makespan, resource utilization, job rejection, throughput, execution time, and cost. Notably, it achieves zero job rejection, reduces energy consumption by up to 85%, improves resource utilization by 11.9%, lowers makespan by 41-45%, and decreases execution time by up to 57%, making it a robust and efficient solution for HPC workload scheduling in cloud environments.
Mohsin Nawaz, Altaf Hussain, Marran Al Qwaid et al.· Computer Science and Informa...· 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
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