cloud service schedulers

Abstract: Tool-using agents can initiate consequential infrastructure changes, yet evidence required for admission may expire while other checks run or depend on a shared fault domain. We formulate evidence acquisition as joint witness selection and scheduling under quorum, diversity, freshness, deadline, and resource constraints. Assurance-Aware Semantic Scheduling (AAS) combines integer-program selection, dispatch-aware temporal scheduling, bounded diagnostic expansion, and receipt-aware repair. Formal results state the assumptions needed for dispatch-time freshness and finite diagnostic expansion. In three generated infrastructure workloads, AAS produces 1,075/1,200 valid candidates versus 647/1,200 for constraint-aware forward scheduling; stale candidates fall from 440 to 12. Paired sensitivity studies reuse the same instances and operation latency draws across parameter settings. A corrected timeout intervention finds 18/20 admissions with repair or full resynthesis versus 0/20 for a static plan, with lower committed cost when receipts are reused. On 20 constructed cases requiring a certified decomposition cut, refinement recovers an oracle-matching feasible plan every time. These are controlled simulation results; the bounded oracle shares a temporal search component, and transfer to deployed systems remains untested.

Abstract: We consider allocation of indivisible goods to agents with equal entitlements and subadditive valuations. As an ex-post fairness notion we consider the maximin share (MMS), and as an ex-ante fairness notion we consider the maximum expectation share (MES), which is always at least as large as the MMS, and sometimes much larger. We present a simple transformation that for every $0 < ρ\le 1$, given any algorithm that produces $ρ$-MMS allocations, transforms it into a randomized allocation algorithm that offers $ρ$-MMS ex-post simultaneously with $η$-MES ex-ante. We prove several new properties of MES, and use them to show that $η\ge \min[\fracρ{2 + ρ}, \frac{1}{4}]$. We also present cases in which the transformation results in a higher value of $η$. Applying our transformation to currently known allocation algorithms shows for subadditive valuations the existence of randomized allocations that are simultaneously $Ω(\frac{1}{\log\log n})$-MES ex-ante and $Ω(\frac{1}{\log\log n})$-MMS ex-post, and for XOS valuations the existence of randomized allocations that are simultaneously $\frac{4}{27}$-MES ex-ante and $\frac{4}{17}$-MMS ex-post.

Abstract: In cross-silo split learning, Parallel Split Learning with Global Sampling forms representative pooled batches when class distributions differ across clients, but ignores client delay when several clients can supply the same class. We introduce Latency Budgeted Parallel Split Learning with Global Sampling, which separates each pooled batch's integer class target from the choice of clients that supply its examples. The flow variant formulates this assignment as an integral network-flow problem and minimizes modeled client-side completion time for the current target. The fast variant uses a greedy next-completion rule to reduce schedule-construction cost. Both preserve the target stream and use every local example once per epoch. A planning rule selects between the variants while accounting for the cost of constructing both candidate schedules. On CIFAR-10, the flow variant reduces modeled training time by 6.75%, with a 0.30 percentage-point decrease in final accuracy. On Tiny ImageNet with 20 candidate classes per client, the fast variant reduces modeled time by 16.87% and reaches all four validation targets earlier than the latency-unaware baseline. Across 405 schedule comparisons, the planning rule stays within 2% of the lower realized cost in 96.54% of cases. In our evaluation, latency-aware provider assignment reduces modeled training time without changing the prescribed class targets, while the preferred variant depends on whether assignment savings outweigh schedule-construction overhead.