# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 5 Conclusions  
This survey has traced four algorithmic lineages--greedy/heuristic packing, exact optimization, queue-theoretic policies, and learning-augmented hybrids--and highlighted that the most effective GPU schedulers emerge when insights from all four converge. Yet the map is not the territory: each lineage relies on workload models and benchmarks that are increasingly outdated for today's hyperscale, multi-tenant clusters. Real progress now depends on better measurements. To that end, we outline five actionable open problems, each linked to a specific quantitative gap in existing public benchmarks, as summarized in Table 8.  
Releasing the five datasets outlined above would shift the field from qualitative debates to quantitative \"horse races,\" enabling the community to report _regression-style \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Open Challenge** & **LLM Training** & **LLM Inference** & **Mixed-Tenant Clusters** & **Edge/IoT** & **Solution Maturity** \\ \hline Coflow-aware scheduling & High & Medium & High & Low & Low \\ \hline Secure multi-tenant isolation & Medium & High & High & Medium & Medium \\ \hline Energy-aware DVFS scheduling & Medium & Medium & High & High & High \\ \hline Topology-aware placement for trillion-parameter LLMs & High & Medium & Medium & Low & Medium \\ \hline Cross-objective (carbon/cost/fairness) schedulers & Medium & Low & High & Low & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Contextual relevance of open GPU-scheduling challenges across deployment scenarios. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Open Problems** & **Missing Benchmark Signal** & **Why It Matters** \\ \hline \multirow{4}{*}{Coflow-aware scheduling for ultra-fast fabrics} & No public trace records per-flow bytes and timing on NVSwitch or InfiniBand-NDR clusters. & Would enable direct evaluation of coflow algorithms on real fabrics, guiding both network stack tuning and scheduler design (see also [PERSON]’s 18% JCT improvement in Section 3). \\ \cline{2-3} Secure multi-tenant GPU isolation & No microbenchmark suite reports cache/SM side-channel leakage across MIG or comparable partitions under production drivers. & Establishes the empirical basis for certifying isolation guarantees and designing schedulers that safely colocate untrusted tenants. \\ \hline \multirow{4}{*}{Energy-aware DVFS under tail-latency SLOs} & DVFS studies typically report only median (p50) inference latency; tail latencies (p90–p99) under load remain unmeasured. & Clarifies the true energy–latency trade-off, enabling operators to meet service-level objectives while minimizing power consumption. \\ \cline{2-3}  & No open trace logs tensor-parallel bandwidth or collective communication latency beyond 16-GPU slices. & Provides ground truth for placement algorithms to minimize communication hotspots in large-model training. \\ \hline \multirow{4}{*}{Cross-objective schedulers (cost \(\times\) carbon \(\times\) fairness)} & Benchmarks lack real-time electricity pricing, carbon intensity, and per-user slowdown metrics in a single trace. & Enables Pareto-optimal scheduler design that balances budget, sustainability, and equity goals. \\ \cline{2-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantitative blind spots currently blocking progress in GPU scheduling research. improvements rather than isolated case studies. Without such data, advances in areas like coflow scheduling, DVFS, or carbon-aware bidding will remain anecdotal and difficult to reproduce. Ultimately, effective GPU scheduling is a problem of systems synthesis--combining elegant algorithms with the practical complexities of drivers, containers, telemetry, and multi-tenant isolation. This synthesis now requires an empirical foundation that reflects the scale, security, and sustainability imperatives of modern AI workloads. Building that foundation is the field's most urgent and unifying challenge. Conceptualization, R.C., F.L.