Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 14-19· 0 citations· 15 references
Abstract
Adaptive CPU scheduling that responds to dynamic workload characteristics remains an open challenge for minimal operating systems. Traditional schedulers employ static parameters unsuitable for heterogeneous workloads, while recent machine learning approaches incur overhead inappropriate for resource-constrained environments. This paper presents Load-Aware Adaptive Scheduling (LAAS), a novel approach that integrates real-time system load classification with entropy-inspired process scoring to enable dynamic scheduling decisions. The core innovation—a load-dependent scoring formula where weight values adapt based on system load class—represents the first application of entropy principles to CPU process selection logic in a minimal kernel. LAAS achieves adaptation without machine learning runtime overhead (sub-0.5% CPU vs. 1% for ML-based approaches) and is suitable for minimal monolithic kernels (approximately 1,500 LOC total implementation). Experimental evaluation on synthetic workloads demonstrates 31% reduction in average waiting time, 28% reduction in context switches, and 18% improvement in CPU utilization compared to standard Round-Robin scheduling, while achieving a Jain fairness index of 0.94 in our test scenarios. These results indicate that load-aware, entropy-inspired adaptive scheduling is practically feasible in severely resource-constrained kernel environments.
High Performance Computing (HPC) systems increasingly operate under heterogeneous workloads, dynamic resource availability, and stringent performance and energy constraints. Traditional batch scheduling policies such as First-Come First-Served (FCFS), backfilling, and priority-based heuristics rely on static assumptions about job behavior and system state, often leading to suboptimal utilization and long waiting times in highly variable environments. This paper proposes an integrated theoretical framework for adaptive HPC scheduling that unifies queuing-theoretic models with computational learning techniques. By interpreting job arrivals and service processes through stochastic queues while enabling scheduling decisions to evolve via data-driven learning, we establish a principled basis for adaptive schedulers that can respond to workload uncertainty. We outline the mathematical foundations of this approach, discuss learning-augmented scheduling policies, and present illustrative scenarios demonstrating how adaptive strategies can outperform static heuristics in terms of mean response time, fairness, and system utilization. This work aims to bridge the gap between analytical scheduling theory and practical intelligent resource management in HPC systems.
Rodgers Kimera, Ali Najib, David Kakeeto· Practice and Experience in A...· 0 citations
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).
A dynamic priority scheduling framework at the kernel level that enhances the CPU allocation to latency-sensitive containers running in Kubernetes environments and reveals 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., P. M et al.· Journal of Soft Computing Pa...· 0 citations
Power-state management in high-performance computing (HPC) clusters must reduce idle energy without excessive wake-up delays for rigid parallel jobs. This paper presents SNF-ICON, an event-driven controller combining smallest-need-first (SNF) gang scheduling, predictive wake timing, and adaptive warm-spare control. At each scheduler invocation, recent interarrival and completed-service samples are screened for sufficiency, exponential-like variability, low lag-one autocorrelation, and acceptable Kolmogorov-Smirnov distance. Rejected or data-sparse windows use SNF+IPM (Intelligent Power Manager), whereas accepted windows activate release prediction and an exponential next-event model. Warm-spare optimization is applied only when queue, event, and arrival-recency conditions permit, balancing estimated waiting and non-compute energy over a timeout-capped horizon. We evaluate four DAS2 trace segments and a generated Markovian workload on AOBA-derived 64-node models, plus SDSC Blue on an AOBA-derived 1152-node model. SNF-ICON is compared with SNF+IPM and First Come First Served (FCFS) + backfilling with IPM. It reduces average waiting time relative to the FCFS-based baseline in all six cases and remains close to at least one heuristic energy baseline in five. The generated workload spends substantial time in ICON mode, whereas DAS2 workloads operate mainly in fallback. Furthermore, cross-platform results show strong dependence on node-transition and power models. Thus, no single policy or parameter set works best in every case.
Ultra-low-latency (ULL) SSDs enable cloud service providers to co-locate latency-sensitive services and throughputoriented background jobs on the same machines. However, their microsecond-scale latency creates a scheduling dilemma: conventional I/O schedulers introduce visible overhead, while disabling scheduling removes the kernel's ability to enforce workload-specific Quality of Service (QoS). This paper presents ARDA, an Adaptive Request Dispatching Algorithm for QoSaware I/O scheduling on ULL SSDs. ARDA dynamically assigns dispatch budgets based on workload QoS requirements inspired by real-time CPU scheduling concepts. Unlike conventional schedulers that rely on request types or relative priorities, ARDA regulates the amount of I/O each workload may inject into the device, thereby satisfying latency targets while preserving throughput and avoiding hardware queue overload. Evaluation on an emulated ULL SSD shows that ARDA reduces average latency by $\mathbf{2 2. 9 6 - 4 2. 9 5 \%}$ and maximum latency by $\mathbf{3 6. 2 6 - 4 4. 8 7 \%}$ compared with Kyber under mixed workloads. Under writesaturated conditions, ARDA reduces worst-case latency by up to 59% compared with the none scheduler. These results show that target-aware scheduling can provide predictable QoS for colocated workloads on ULL SSDs without sacrificing throughput.
Ming Wei, Tzu-Chieh Huang, Chieh-Lin Tsai et al.· IEEE International Conferenc...· 0 citations
A hybrid congestion-aware autoscaling framework for serverless platforms inspired by TCP slow-start and congestion control principles is proposed, which significantly improves latency performance, scalability, and resource efficiency in serverless environments.
Mohammad Tari, Mostafa Ghobayee-Arani, Jafar Pouramini· International Journal of Clo...· 0 citations
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