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Open access Aug 2026

Hierarchical Scheduler with Adaptive Time-Budget Reallocation for Time-Triggered Edge-Fog-Cloud Architectures

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. · 0 citations
Open access Jul 2026

SPES: A Stochastic Predictive Energy-Aware Scheduling Approach for Efficient Multi-Region Cloud Computing

Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.

M. Yacoub, A. Raouf, Walaa K. Gad et al. · 0 citations
Open access Aug 2026

CELLServe: An SLO-Aware and Cost Efficient LLMs Serving System for Serverless Computing Environments

CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.

Zejian Wang, Nan Lin, Zinuo Cai et al. · 0 citations
Preprint Aug 2026

Serverless platform driven CPU loadbalancing

Serverless platforms maintain a global view of function invocations and resource utilization, yet existing systems largely restrict CPU scheduling decisions to the operating system scheduler. This paper presents a serverless platform-driven CPU load balancing framework that enables the control plane to directly influence CPU scheduling through a custom Linux scheduler built on SchedExt(SCX). The proposed scheduler introduces configurable scheduling domains and a shared interface that allows the control plane to assign functions to domains based on their historical inter-arrival times. Within each domain, a single-queue load-balancing strategy combined with a virtual-time prioritization policy improves task placement while reducing interference from busy-polling tasks. Results show that an eight-domain configuration achieves the best trade-off, reducing system energy consumption by approximately 15% while increasing invocation cost by only 5%. Under heavily loaded workloads, the proposed scheduler also reduces request latency by up to 50% compared to the default Linux Completely Fair Scheduler (CFS). These results demonstrate that exposing CPU scheduling decisions to the serverless control plane can improve both energy efficiency and workload performance while preserving scheduling flexibility.

Abdul Rehman · 0 citations
Open access Jun 2026

Performance modeling in FaaS workloads at the edge using machine learning

The Function as a Service (FaaS) paradigm has emerged as a compelling architectural model for both cloud and edge computing environments, enabling the execution of self-contained functions triggered by specific events while abstracting from developers infrastructure management complexities such as load balancing and auto-scaling. In FaaS-enabled clusters, particularly within resource-constrained edge environments, precise resource consumption estimation becomes critical to optimize resource utilization, minimize latency, prevent system overloads, and ensure scalability. This paper addresses performance modeling challenges in FaaS-enabled distributed and decentralized edge computing systems, operating at the granularity level of both nodes and individual functions. We propose a Machine Learning-based framework designed to predict key performance indicators, including CPU utilization, memory, and energy consumption, based on incoming workload patterns, while simultaneously forecasting potential system overload conditions. Moreover, our approach introduces a profiling methodology that characterizes serverless functions according to their resource consumption profiles, thereby enabling accurate prediction of node-level resource demands without requiring detailed knowledge of individual deployed functions. Experimental validation demonstrates that our predictive models achieve 97% accuracy in anticipating node overload scenarios, providing a robust foundation for proactive resource management in edge-based FaaS deployments. Moreover, while our best individual, function-based regression models predict node-level CPU, RAM and power consumption with a Mean Absolute Percentage Error below 9% on average, our experiments highlight the effectiveness of function profiling and cluster-based modeling. On one hand a novel multi-target regressor based on a permutation-invariant neural architecture is proved to generalize effectively across previously unseen workload compositions in the tested scenarios, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document} scores ranging from 0.94 to 0.98, i.e., aligned with the performance of specialized single-target regression models. On the other hand, cluster-level models generalize effectively to previously unseen functions within the same usage class, maintaining prediction errors within practically acceptable ranges and often in the single-digit percentage range.

Federica Filippini, Marco Savi, Michele Ciavotta · 0 citations
Open access Aug 2026

EMC+: An Opportunistic Elasticity Method for Improving System Throughput and CPU Utilization in Cloud Data Centers

The new EMC+ proposal is an OS‐driven elasticity manager for container‐based environments that continuously estimates idle core cycles left by regular (inelastic) applications, and reallocates idle cores to elastic ones, even during short time intervals, and has minimal impact on the performance and QoS of colocated inelastic applications.

J. C. Saez, Carlos Bilbao, Manuel Prieto-Matías · 0 citations