May 2026· Proceedings of the VLDB Endowment· Vol 19, pp. 1867-1879· 0 citations· 40 references
Computer ScienceEngineering
TL;DR
This work proposes and develops an open-source policy simulation framework, LoadStar, which forms a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads, and defines a resource optimization problem for placing Cosmos DB replicas onto VM nodes, and develops the Luna model for forecasting future load distributions.
Abstract
Large-scale managed cloud databases leverage sophisticated load Packing and Migration (PAM) algorithms, which provide the efficiencies necessary for running these services at scale on cloud resources. Research into optimizing the resources and reliability of cloud databases at massive scales is limited by a lack of public NoSQL workloads. We address this in the context of
Cosmos DB
, Microsoft's flagship cloud-hosted NoSQL database. We first propose
open-source NoSQL workloads
from real Cosmos DB clusters, and analyze these traces to derive a novel reliability metric,
Distressed Resource Volume (DRV)
, which captures the quality of service experienced by the end user. We then develop an
open-source policy simulation framework, LoadStar
, powered by a non-parametric statistical model of estimating the QoS of real traffic patterns. These form a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads. We then define a
resource optimization problem
for placing Cosmos DB replicas onto VM nodes, develop the Luna model for
forecasting
future load distributions, and the Orbit
PAM algorithm
that uses these forecasts to trigger and rebalance stressed replicas, to reduce tail-errors. Our experiments, validated using LoadStar for these workloads, demonstrate Orbit's benefits over the existing Cosmos DB policy and a worst-fit optimized baseline, with higher load delivered at lower error rates and up to 35% reduction in resources. These have been deployed in production, with potential savings of $100
Ms
/yr while improving service reliability for millions of customers.
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
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.
Energy consumption in cloud-based relational database management systems (RDBMS) has emerged as a critical challenge facing modern data centers, with operational energy costs accounting for up to 40% of total infrastructure expenditure. Traditional query scheduling strategies, including First-Come-First-Served (FCFS) and Shortest-Job-First (SJF), prioritize performance optimization while neglecting explicit energy consumption considerations. This study proposes an Energy-Driven Adaptive Scheduling (EDAS) algorithm that dynamically prioritizes queries based on estimated CPU utilization, disk I/O costs, and historical energy profiles without requiring modifications to the underlying database engine. Experimental evaluation was conducted on a cloud-based MySQL 8.0 system deployed on Amazon Web Services (AWS) EC2 instances using light (50 queries), medium (150 queries), and heavy (300 queries) workloads derived from Sakila and TPC-H benchmarks. Results demonstrate that energy-aware scheduling exhibits workload-dependent performance characteristics: SJF achieves optimal energy efficiency under light and medium workloads with 14.2% and 16.0% savings respectively, while EDAS achieves measurable energy savings of 10.4%, 12.3%, and 20.5% under heavy workloads compared to FCFS, SJF, and baseline scheduling approaches. EDAS demonstrates greater resilience under CPU throttling conditions, maintaining 15.8% energy reduction when processor frequency drops from 2.5 GHz to 1.8 GHz. The energy-delay product (EDP) improves by 18.7% under heavy workloads, indicating superior energy-performance trade-offs. This study establishes the importance of workload-aware query scheduling for improving cloud database energy efficiency and provides practical guidelines for implementing energy-conscious scheduling in production RDBMS environments. The proposed approach reduces operational costs by approximately $1,247 annually per mid-sized database instance while maintaining ACID compliance and query performance guarantees.
Keywords: energy-aware computing, query scheduling, cloud database, green computing, energy efficiency, adaptive scheduling, TPC-H benchmark, energy-delay product
Shankar Kumar· International Journal of Cre...· 0 citations
The new EMC+ proposal is an OS‐driven elasticity manager for container‐based environments that continuously estimates idle core cycles left by regular (inelastic) applications, and reallocates idle cores to elastic ones, even during short time intervals, and has minimal impact on the performance and QoS of colocated inelastic applications.
J. C. Saez, Carlos Bilbao, Manuel Prieto-Matías· Concurrency and Computation· 0 citations