Jul 2026· International Conference on Computer Communications and Networks· pp. 1-6· 0 citations· 26 references
Abstract
Public LLM services serve diverse multi-agent applications with varying workflow dependencies and performance requirements. Requests generated by these applications often exhibit commonality and interdependence, yet current systems largely ignore such application-level structure. As a result, at the LLM engine cluster level, assigning requests to engines with the shortest queue can cause inefficient KV-cache transfers across GPUs. Using three representative multi-agent applications, we show that current scheduling methods miss opportunities to (a) improve performance through KV-cache reuse and reduced data transfer, and (b) increase goodput via batch management informed by workflow dependencies. Motivated by these observations, we propose a Workflow-Aware Scheduling system for Multi-Agent LLM systems (WaSMa) that incorporates cluster-and engine-level scheduling to optimize LLM request execution across GPU resources. Experimental results show that WaSMa reduces the P95 tail latency by up to 48% and improves goodput by up to 107% compared to existing methods.
Concurrent multi-agent workflows expose future dependencies and serving-state requirements while running on heterogeneous GPU pools with time-varying load, model residency, and resource availability. The logical workflow defines the required computation, whereas its physical scheduling units, model-lifecycle actions, resource ordering, and placement must be selected according to the observed pool state. We present a prediction-guided runtime that uses workflow forecasts to construct and optimize a physical execution graph. Predictor estimates device-specific activation latency, peak memory, and model-loading cost, then propagates these predictions through workflow dependencies to forecast activation readiness and future model demand. Constructor builds semantics-preserving fusion and model-lifecycle alternatives, while Scheduler jointly optimizes their selection, placement, and execution order based on the live pool state. Across a workload spanning three workflow scenarios on a heterogeneous GPU pool, our system reduces end-to-end makespan and overall p95 completion latency under burst arrivals by up to 36.8% and 25.9%, respectively, over state-of-the-art workflow schedulers. It also saves up to 24.63 GPU-s per completed session.
Jing-Hao Wang, Yi-Feng Zhang, Xiao Zhou et al.· 0 citations
This survey presents a systematic taxonomy and technical review of dynamic orchestration strategies designed to address communication overhead, KV cache management challenges, and increased token consumption within large Language Model-based Multi-Agent Systems.
Heet Nagoriya, H. Raithatha· International Journal of Kno...· 0 citations
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.
Ali Zafar Sadiq, Hai-Ying Shen· International Conference on...· 0 citations
LLM agents solve complex tasks by executing multi-step workflows that interleave LLM inference with external tool calls, yet execution efficiency is often the dominant bottleneck in real deployments because LLM-generated workflows are typically chain-structured and inherently sequential, limiting parallelism and underutilizing available compute resources. We propose CoAct, a training-free framework that parallelizes agent workflows by casting execution as an online task allocation problem: CoAct prompts the LLM to generate a pool of discrete subtasks and performs online dispatch by selecting, whenever a worker becomes available, the next task that minimizes an incremental task-contrastive objective, encouraging high similarity among tasks executed on the same path (positive pairs) and low similarity across different paths (negative pairs) to reduce cross-worker interaction and synchronization. CoAct further supports speculative redundancy via selective re-execution to improve robustness and mitigate tail latency. Experiments on tool-augmented agent workloads show that CoAct improves per-step execution efficiency and resource utilization while achieving competitive or superior task accuracy, demonstrating that contrastive online dispatch can expose substantial parallelism in LLM-agent workflows without retraining the underlying model.
Yuyang Peng, Yanling Xu, Shu-Yi Wang et al.· Proceedings of the 32nd ACM...· 1 citation
A Task-Oriented Prefix-Aware Scheduler that jointly decides which agent prefixes to keep in the cache and which requests to schedule for execution and scores candidate post-decision states by trading off the expected reduction in each task's longest remaining service path against the near-term benefit of downstream prefix reuse.
Hongqiu Ni, Han Tian, Chi Zhang et al.· 1 citation
EASy is proposed, a trainable agentic framework that jointly optimizes task performance and computational efficiency through reinforcement learning and consistently achieves stronger performance-efficiency trade-offs than strong agentic baselines.
Junnan Liu, Linhao Luo, Thuy-Trang Vu et al.· 0 citations
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