Aug 2026· Journal of Intelligent Computing and Networking· 0 citations· 25 references
TL;DR
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%.
Abstract
Large-scale high-performance computing workloads in petroleum geophysical exploration commonly use the Message Passing Interface (MPI) as their parallel programming model. However, MPI does not provide native mechanisms for resource management, which complicates the efficient execution of multiple MPI jobs in shared-resource environments. As MPI workloads migrate to cloud-native platforms, their high degrees of parallelism and communication-intensive behavior may lead to scheduling delays and contention for shared resources, resulting in prolonged execution time and inefficient resource utilization. This paper identifies two major limitations of existing cloud-native MPI deployments: cluster-unaware process-count selection and insufficient exploitation of Non-Uniform Memory Access (NUMA) locality. To address these limitations, we propose a resource-aware optimization framework that dynamically selects the MPI process count and performs node- and NUMA-aware process placement. Experimental results show that the proposed parallelism-selection method reduces task-sequence execution time by at least 18% and improves the evaluated resource-utilization metrics by more than 30%. The topology-aware placement method further reduces execution time by at least 10% compared with the evaluated affinity baselines under the tested shared-resource cluster configurations.
The use of containerized cloud platforms has now become inevitable for latency-sensitive applications; but resource contention between different workloads often creates the problem of noisy neighbors, which causes higher response latency and poor service quality. We present SCX_MUS (Scheduler Extensions for Multi-User Scheduling), a dynamic priority scheduling framework at the kernel level that enhances the CPU allocation to latency-sensitive containers running in Kubernetes environments. The proposed framework uses Linux sched_ext architecture along with the extension of eBPF to support dynamic cgroup-based scheduling through runtime awareness. A lightweight user space component continuously observes the workload of Kubernetes and adjusts the scheduling priorities of the containers without any need for recompiling the kernel or restarting containers. The experimental evaluation of the proposed framework was done with the help of Redis and stress-ng workloads in the same benchmarking condition compared to the native Linux Completely Fair Scheduler (CFS). The experimental results reveal a significant improvement in terms of latency reduction, enhanced throughput, efficient utilization of CPU resources, and stable performance of scheduling under resource contention.
T. Rajkumar, Nishanth D., Prabu M. et al.· Journal of Soft Computing Pa...· 0 citations
High Performance Computing (HPC) centers are expanding to encompass resources that extend beyond traditional computing. By extending resources to quantum computing, hybrid quantum-classical workflows tackle complex optimization problems that have never before been possible. However, integrating quantum processing units (QPUs) into cloud-native and scientific workload managers presents a unique orchestration challenge: remote quantum devices introduce a second, external queue -- a two-queue problem -- alongside the queue owned by the traditional scheduler. In this work we present Fluence, a Kubernetes scheduler plugin backed by the Fluxion graph-based scheduler, that enables informed, gang-scheduled placement for quantum-classical workloads and custom resources. We evaluate Fluence across three scenarios using AWS Braket simulators and real QPUs. First, under node contention, Fluence's atomic gang placement all but eliminates the wasted node-time that a default scheduler accrues by partially placing gangs. Second, we introduce a synchronization primitive for the two-queue problem in which a single producer submits a shared quantum task while consumers remain scheduling-gated, reducing worker idle time by roughly 5x under short device queues and by orders of magnitude when a real device queue stretched to hours. Third, cost- and queue-aware backend selection pins the cheapest or shortest-queue device satisfying a workload, cutting mean per-run cost by roughly 70x and time-to-result from hours to under a minute. Together, these results show that quantum-awareness can be added to a cloud-native scheduler without modifying user containers.
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.
Hao Wei, Hailiang Chen, Jia-Nan Sun et al.· Fall Joint Computer Conferen...· 0 citations
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, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
This study assesses the scalability of process-based and thread-based schedulers for many-core shared-memory systems using a memory-intensive row-wise quick-sort workload on large three-dimensional tensors. The process-based evaluation considers bounded prolific, bounded collective, and three pipe-based producer-consumer schedulers: one-to-one, one-to-many, and many-to-many. These pipe schedulers dynamically stream task identifiers to worker processes, exchanging increased inter-process communication overhead for enhanced runtime load balancing and flexible chunk-based task dispatching. The thread-based evaluation examines static, dynamic, guided, chunk-based, chunk-stealing, adaptive chunk, and AIMD adaptive scheduling strategies. The AIMD scheduler employs an additive-increase multiplicative-decrease policy inspired by TCP congestion control, utilizing an exponentially weighted moving average (EWMA) of CPU utilization to regulate a contention window that limits the number of concurrently active chunks. The adaptive chunk scheduler further modifies chunk size based on observed per-thread execution speed. Experimental results on a 24-core x86-64 platform indicate that thread schedulers deliver the highest overall performance, with dynamic and guided scheduling yielding the most favorable practical outcomes. Among process schedulers, pipe-based designs demonstrate the strongest scalability, with one-to-one pipes excelling for smaller workloads and many-to-many pipes preferred for larger workloads. In summary, lightweight thread scheduling is optimal for shared-memory row sorting, while AIMD/adaptive scheduling and pipe-based process scheduling remain valuable for contention-aware execution, explicit inter-process coordination, and distributed-style heterogeneous workload management.
Mejgan Dedaj, Argyro Gailla, T. Ioannou et al.· 0 citations
Current GPU Collective Communication Libraries (CCLs) employ predefined schedules optimized for stable environments. Their supported schedules and selection logic are fixed at communicator initialization, which fails to account for evolving runtime conditions, such as workload characteristics and hardware health status. Consequently, long-running GPU jobs experience suboptimal performance after hours or days of execution, which translates into longer job completion times and wasted GPU cluster resources. To address this problem, we present Theseus, a novel CCL backend that provides schedule-level runtime adaptivity. It admits user-defined schedules and selection policies. As runtime conditions change, Theseus selects suitable schedules using cluster-wide runtime attributes beyond CCL-internal metrics. Moreover, it hot-swaps from the previous schedule consistently across GPUs with low overhead. Theseus acts as a drop-in replacement to facilitate integration. We evaluate Theseus extensively on various GPU workloads with intuitive policies. Compared with NCCL, Theseus achieves up to 1.61X speedup of communication time in stable environments and 2.46X in dynamic environments. It improves end-to-end job completion time by up to 1.84X while incurring comparable or lower overhead.
Rui Ding, Xiandong Lu, Jiajun Wang et al.· Proceedings of the ACM SIGCO...· 0 citations