Aug 2026· Proceedings of the ACM SIGCOMM 2026 Conference· pp. 491-504· 0 citations· 50 references
TL;DR
Hestia is proposed, a framework that achieves long-term stable oversubscription through workload aggregation through a smoothing-based method to classify workloads suitable for aggregation according to their periodicity, and an aggregation algorithm to minimize the overall MCV.
Abstract
Cloud providers commonly employ oversubscription strategies to maximize profitability, leveraging the significant gap between the resources purchased by tenants and those actually consumed by their workloads. However, the temporal volatility of workloads may lead to overload on oversubscribed nodes. To address this issue, existing works typically focus on designing reactive rescheduling mechanisms triggered by overload events or adopt conservative oversubscription strategies to mitigate overload risks. Nonetheless, these solutions compromise either tenant experience or provider profitability. In fact, reducing the temporal volatility of workloads is key to addressing the above challenges. We observe that many workloads exhibit temporal complementarity. Aggregating such workloads can effectively mitigate temporal volatility, thereby improving overall resource utilization. Motivated by this insight, we first design a new metric, called Maximum-based Coefficient of Variation (MCV), to quantify the temporal volatility of workloads. We then propose Hestia, a framework that achieves long-term stable oversubscription through workload aggregation. Specifically, we propose a smoothing-based method to classify workloads suitable for aggregation according to their periodicity. Subsequently, we design an aggregation algorithm to minimize the overall MCV, and treat the aggregated workloads as the units for oversubscription. Experimental results show that, using CPU as a representative example, Hestia reduces MCV by 43.3% and increases oversubscription profit by 66.74%.
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· Computers· 0 citations
TADOF is a dynamic optimization framework that jointly performs energy-aware elastic scaling and task migration under drift-aware periodic modeling and burst detection and observed about 18–34% lower energy and 15–28% lower migration cost than threshold and A2C/TD3 baselines while preserving QoS.
Juan Guo, Yanqun Zuo, Zhixian Chang· Cluster Computing· 0 citations
The results show that soft SLO limits reduce corrective rescheduling actions by 49% compared to hard-limit approaches while maintaining acceptable performance guarantees, and resource-aware scheduling decreases node-level congestion and further mitigates SLO violations, demonstrating the effectiveness of incorporating application-level flexibility and hardware-level insights into scheduling and rescheduling decisions.
Oliver Larsson, Thijs Metsch, Cristian Klein et al.· 0 citations
Modern cloud data warehouses decouple compute from storage, making it easy for organizations to access the same underlying data with multiple compute clusters. This flexibility is often used for performance isolation among diverse workloads, so that each workload meets its latency service-level objective (SLO) more reliably. For example, interactive dashboards, ad hoc analysis, and batch jobs can each run on separate clusters. However, this dedicated-cluster approach requires each compute cluster to be continuously scaled to adapt to workload evolution, with over-provisioning wasting resources and under-provisioning risking SLO violations. We present AutoSLO, a latency-SLO-aware workload management framework for multi-cluster cloud data warehouses. AutoSLO operates across three timescales through three key components. First, a periodic Policy Tuner plans proactive cluster scaling actions and tunes configuration parameters, using simulations of history-derived workload forecasts. Second, an SLO-aware reactive Autoscaler adjusts the active cluster set when recent workload behavior deviates from the forecast. Third, an online Query Router reacts to live load when placing each query, using a concurrency-aware latency predictor to avoid SLO violations. On realistic Redbench workloads, AutoSLO successfully meets latency SLOs of varying strictness, reducing cost by a mean of 26.4% compared to the per-scenario next-best baseline. Component-level evaluations show that the Query Router and Autoscaler respectively reduce SLO violation rates by a mean of 47.8% and 93.7%, relative to their corresponding alternatives. Finally, we show that the Policy Tuner can reduce the SLO violation rate by a mean of 44.6% using a single day of workload history, and that each component is efficient given its intended operating timescale.
A hybrid congestion-aware autoscaling framework for serverless platforms inspired by TCP slow-start and congestion control principles is proposed, which significantly improves latency performance, scalability, and resource efficiency in serverless environments.
Mohammad Tari, Mostafa Ghobayee-Arani, Jafar Pouramini· International Journal of Clo...· 0 citations