Jul 2026· IEEE International Conference on Cloud Computing· pp. 500-506· 0 citations· 28 references
Abstract
Serverless computing is a cost-effective, on-demand paradigm for deploying application workflows, but it faces a key challenge in efficient resource allocation. Platforms must provision resources that satisfy Service Level Objectives (SLOs) while minimizing operational cost. In real workflows, execution time depends on input size, but most approaches are input-agnostic, causing SLO violations for large inputs and overprovisioning for small ones. Recent input-aware schedulers still rely on static averages along the critical path, failing to capture dynamic, input-dependent dataflow across stages. In this work, we propose a Correlation-Aware Proportional Critical Path (C-PCP) method that employs a dynamic, input-aware weighting model derived from online profiling data. A correlation matrix captures statistical dependencies between the input and output sizes of sequential functions, enabling dynamic critical path computation, more accurate sub-SLO allocation, and improved resource profile assignment. Additionally, we develop a dynamic programming based theoretical lower bound to provide a rigorous performance benchmark. For experimental validation, workflow task profiling data is collected from execution traces on a Knative-based testbed deployed over Kubernetes, ensuring realistic modeling of workflow behavior. We evaluate the proposed approach on diverse real-world and synthetic workflows, demonstrating consistent improvements over state-of-the-art methods in both SLO satisfaction and cost efficiency.
PRISM, a prediction-guided runtime framework that jointly selects model variants and CPU allocations for containerized edge microservices, and adapts each pipeline stage in place and minimizes predicted CPU-package energy under deadline, resource, and offline model-level Quality of Result constraints is presented.
Uwe Gropengießer, Thomas Reuter, Dominik Schön 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
Results show that ECD-DVFS provides a balanced trade-off among performance, energy efficiency, reliability, cost, and QoS through adaptive and energy-aware workflow scheduling in hybrid fog–edge–cloud environments.
Zhihao Peng, Behnam Barzegar, Parmida Tavakoli et al.· Journal of Supercomputing· 0 citations
Stateful serverless (Function-as-a-Service) environments, whose workers host state servers, are increasingly used for stream processing. A stream application is a pipeline of operators, where each operator forwards intermediate data downstream through a chained request. As input rates fluctuate, the system should adjust operator parallelism and place instances across workers to sustain the incoming rate. Existing approaches do so without fully accounting for chained-request overhead, leading them to misestimate the required number of workers. Too few leave the cluster unable to keep up with the input rate, while too many route a larger fraction of chained requests across worker boundaries, increasing end-to-end latency. We propose CLASP, a scaling and scheduling strategy for stream processing in stateful serverless environments. At runtime, CLASP estimates execution cost and chained-request cost from observed metrics. Under a capacity model that covers the two costs, it adjusts operator parallelism and packs operators onto the fewest workers that can sustain the target input rate. Once a scaling decision is made, CLASP migrates each operator's state together with its instances, thereby minimizing execution pause time. Experiments show that CLASP improves throughput by up to 3.3x and reduces median end-to-end latency by up to 76% compared with state-of-the-art scaling strategies.
Tian Qi, M. A. Rodriguez, Rajkummar Buyya· 0 citations
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.
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations
Efficient workflow scheduling is essential for improving testing efficiency in large-scale Vehicle-to-Everything (V2X) protocol conformance testing. Existing directed acyclic graph (DAG) schedulers treat all test components uniformly, overlooking the inherent structural heterogeneity of V2X workflows which contain mixed independent and precedence-constrained components. This leads to cross-subsystem interference, low resource utilization, and extended makespan. This paper presents AOE–CP (AON DAG with Edge-Weighted Transformation and Critical Path Scheduling), a structure-aware hybrid scheduling architecture for V2X testing. Unlike existing heuristic-based improvements, AOE–CP achieves performance gains through domain-specific structural reorganization rather than new scheduling rules. It integrates three mechanisms: (i) structural decomposition to decouple independent and dependent components and resolve their conflicting optimization objectives; (ii) critical path thread isolation to eliminate cross-subsystem resource competition and guarantee zero critical path waiting time for V2X dominant-topology workflows; (iii) atomic operation-based time estimation supporting fully offline scheduling without runtime profiling overhead. Experiments show that, for large-scale workflow scenarios, AOE–CP reduces normalized makespan by 14–16% and 22–24% compared with HEFT (Heterogeneous Earliest Finish Time) and CPOP (Critical Path On Processor), respectively. Scheduling overhead is only 2.1 ms for 1000-component workflows. AOE-CP reaches makespan saturation with four threads, versus 12 for HEFT and 28 for CPOP, demonstrating superior resource efficiency and scalability. The framework can also be generalized to other mixed-dependency workflow scenarios.
Unknown authors· Algorithms· 0 citations
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