Job-Level Dynamic Cache Allocation for Real-Time Systems under Shared LLC Interference

Beyond the Limits: Flexible and Congestion-Aware Cluster Scheduling for the Cloud

The results show that soft SLO limits reduce corrective rescheduling actions by 49% compared to hard-limit approaches while maintaining acceptable performance guarantees, and resource-aware scheduling decreases node-level congestion and further mitigates SLO violations, demonstrating the effectiveness of incorporating application-level flexibility and hardware-level insights into scheduling and rescheduling decisions.

Critical-Section Granularity for Multi-Resource Systems with Nested Critical Sections

Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions

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.

CacheFlow: Using Maximum Flow to Bound Cache-Based Preemption Delays

CacheFlow is presented, a framework that formulates total CRPD as a maximum-flow problem, and an iterative max-flow formulation is described to more efficiently integrate the max-flow solving in the context of standard fixed-priority response-time analysis.

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.

FlowGuard: Slack-Aware Overload Control for Multi-Agent LLM Serving

Multi-agent applications increasingly rely on shared large language model backends in the public cloud, where bursty workloads cause requests from different agents to contend for the same LLM instances, leading to long queues, memory imbalance, and severe tail-latency inflation. Existing approaches typically prioritize requests using coarse workflow positions or static execution heuristics, which fail to adapt to short-term overload dynamics. We present FlowGuard, a workflow-aware overload controller for multi-agent LLM serving. Its key insight is that under sustained overload, GPU cycles spent on requests whose execution service-level-objectives (SLOs) are already violated are wasted. FlowGuard continuously recomputes per-request slack and prioritizes requests with the greatest remaining time before their deadlines, thereby maximizing on-time completions. In addition, a resource-aware dispatcher jointly accounts for KV-cache memory pressure and in-flight queue depth to reduce preemption across shared instances. Evaluated on a deliberately over-subscribed two-GPU backend, where all policies exhibit high absolute miss rates (i.e., the percentage of workflows that miss their deadlines), FlowGuard reduces the miss rate by 14–28% points over workflow-oblivious and static-priority baselines under BurstGPT-driven load, and by 34–38% points under co-located mixed-agent workloads.