Jul 2026· Fall Joint Computer Conference· pp. 97-104· 0 citations· 28 references
Abstract
As computing resources in cloud environments become increasingly abundant, executing complex scientific workflows on large-scale cloud infrastructure has become a standard practice. However, communication-intensive workflows face two fundamental bottlenecks. First, the lack of physical topology awareness often forces high-frequency interacting microservices to be placed on geographically distant nodes, which generates excessive cross-node communication overhead, leads to network load imbalance, and increases latency. Second, the prohibitive online computation time of conventional iterative scheduling algorithms further degrades response speed, making them unsuitable for real-time scenarios. To address these bottlenecks, this paper proposes AMSche, a framework for microservice deployment and task scheduling that is aware of both position and topology. The framework comprises two core mechanisms. The first mechanism, position-aware service deployment, colocates high-frequency interacting services on the same physical node based on communication affinity, thereby compressing cross-node communication overhead at the physical level. The second mechanism, topology-aware task scheduling, leverages online topology feature similarity mapping to instantly reuse historical scheduling plans, achieving scheduling decisions at the millisecond level. Extensive experiments on real-world scientific workflow datasets demonstrate that AMSche achieves an average improvement of 16.19% to 39.15% over existing baseline methods in comprehensive metrics including response time, total communication volume, and network load balance.
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
A resource-aware optimization framework that dynamically selects the MPI process count and performs node- and NUMA-aware process placement and reduces task-sequence execution time and improves the evaluated resource-utilization metrics by more than 30%.
Wenxiao Wang, Zi-Bo Gao, Guoding Ji et al.· Journal of Intelligent Compu...· 0 citations
QMScaler is proposed, a QoS-driven microservice horizontal scaling framework based on Monte Carlo Tree Search (MCTS) that aims to minimize the number of container instances while meeting the QoS requirements of multiple application functions.
Tianyang Zheng, Pengfei Yang, Zhe Xu et al.· 0 citations
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· De Computis· 0 citations
ADASCALE is an adaptive framework that jointly scales and places microservice replicas under multi-dimensional dynamics that consistently meets SLO targets and improves both latency and throughput.
Ming Chen, Muhammed Tawfiqul Islam, Maria Rodriguez Read et al.· arXiv.org· 0 citations
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded.
This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay.
Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
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