FunPilot is presented, a system that enables rapid LLM-assisted diagnosis and remediation for serverless applications that uses an event-driven control loop to diagnose active symptoms, derive control knob updates, and validate remediation decisions while coordinating with the underlying autoscaler.
OpScale is presented, a practical operator-level orchestration framework of profiling, provisioning, placement, and runtime serving that attains SLOs with up to 36.3% fewer GPUs and 28% less power, or achieves 44% higher throughput under fixed cost budgets.
Xingqi Cui, Chieh-Jan Mike Liang, Ziang T. Tang et al.· 0 citations
CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.
Zejian Wang, Nan Lin, Zinuo Cai et al.· ACM Transactions on Architec...· 0 citations
While serverless computing is popular, its dominant Function-as-a-Service (FaaS) model is ill-suited for stream processing because its stateless, centrally orchestrated functions cannot efficiently handle continuous, low-latency event flows. We introduce Epico, a serverless runtime explicitly designed to resolve these inefficiencies at the runtime level. Epico executes pipeline stages as persistent WebAssembly components, enabling independent, zero-to-infinity autoscaling based on queue-depth SLOs and routing events directly between stages using broker-free ZeroMQ channels. To optimize short execution paths, it utilizes a credit-based sliding window to amortize inter-process communication costs. Evaluations demonstrate that Ahead-of-Time (AOT) compilation reduces cold-start latencies from hundreds of milliseconds to sub-millisecond ranges, while the credit window improves single-worker throughput by up to \(4.3\times\). Compared to Apache OpenWhisk, Epico bypasses the orchestrator bottlenecks and container overheads that typically hinder FaaS streaming workloads.
Matteo Della Bartola, Valerio Besozzi, Patrizio Dazzi et al.· 0 citations
This work asks whether a Large Language Model can replace the static routing policy itself, reading HAProxy and Prometheus telemetry every 10 seconds and isolating faulty servers through guardrailed calls to the HAProxy Data Plane API.
Stateful stream processing engines are critical for real-time analytics but lack efficient mechanisms for runtime elasticity. The dominant "stop-the-world" model, used by systems like Apache Flink, requires halting applications globally for a long time, while recent on-the-fly protocols introduce severe trade-offs: proactive approaches impose a continuous resource tax by constantly replicating state, and existing reactive solutions suffer from architectural complexity and external dependencies. This paper introduces Fugue, a novel, self-contained reactive protocol that provides seamless and resource-efficient elasticity. The core of Fugue is a two-phase design that combines a pre-emptive background state transfer with an atomic, lightweight barrier-based cutover. By moving the bulk of an operator's state off the critical path and unifying the final ownership transfer with the system's native exactly-once synchronization mechanism, Fugue guarantees correctness with minimal disruption and steady-state overhead. We implemented Fugue in Apache Flink and our evaluation on realistic benchmarks shows it reduces tail reconfiguration latency by up to 98.6% relative to native Flink while maintaining over 90% of peak throughput. Compared to reactive pull-based baselines, Fugue reduces end-to-end migration latency by up to 93.7%. Compared to proactive replication, it reaches comparable handover performance while avoiding continuous replication overhead. Together, these results demonstrate a strong combination of robustness, performance, and operational simplicity.
Unknown authors· Proceedings of the VLDB Endo...· 0 citations
RL-ICE is proposed as an innovative scheduler that can work in such a cloud continuum by leveraging a multi-cluster and hierarchical RL to satisfy both user Quality of Experience (QoE) metrics and tenant’s costs.
Doriana Monaco, Alessio Sacco, C. Casetti et al.· 0 citations
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