TL;DR
Experimental results on SPEC CPU benchmarks show that MPORA delivers accurate predictions under unseen inputs and distribution shifts with low overhead, while improving schedulability and response times over existing methods.
PRISM, a prediction-guided runtime framework that jointly selects model variants and CPU allocations for containerized edge microservices, and adapts each pipeline stage in place and minimizes predicted CPU-package energy under deadline, resource, and offline model-level Quality of Result constraints is presented.
This paper introduces a job-level dynamic cache allocation strategy for multicore systems in which critical real-time tasks execute on a dedicated core while sharing the LLC with best-effort workloads executing on the remaining cores.
A wave-quantized analytical model and launch-time resource manager for dependency-coupled overlap pipelines that selects the communication-CTA count and resource partition before each launch without candidate execution, per-workload profiling, or kernel recompilation is presented.
SNF-ICON is presented, an event-driven controller combining smallest-need-first (SNF) gang scheduling, predictive wake timing, and adaptive warm-spare control that 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.
This work presents the first deterministic approach for modeling Spark application execution time under dynamic executor allocation, and the proposed models are lightweight, interpretable, and require minimal input data, offering a practical alternative for performance prediction and resource optimization in distribute...
DAN-Scheduler is presented, a deterministic offline scheduling and compiler optimization framework for intra-core NPU execution that co-optimizes these decisions in three stages that show that deterministic stage-wise co-optimization is an effective and broadly applicable compiler optimization strategy for memory-const...
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