Stochastic Modeling of SLA-Aware Edge–Cloud Orchestration under Cold Starts

This repository contains the reproducibility data and verification code supporting the study “Stochastic Modeling of SLA-Aware Edge–Cloud Orchestration under Cold Starts: Deadline-Phase Scheduling and Elastic Provisioning.” The study develops a stochastic edge–cloud orchestration model that jointly represents observable deadline phases, marked-batch arrivals, SLA-aware service scheduling, non-cancellable stochastic cold starts, elastic-capacity activation, retention, release, and draining. Controlled synthetic experiments are used to isolate the effects of arrival correlation, deadline evolution, service speed, activation and release thresholds, and setup-time distributions. The package contains an independently executable explicit-customer discrete-event simulation, selected replication-level numerical outputs underlying the confirmatory sensitivity analyses, summary statistics and contrasts, exact CTMC–DES certification records for two tractable anchors, software requirements, execution instructions, automated verification code, and SHA-256 integrity checks. The deposited evidence comprises 1,560 replication-level observations and two exact CTMC–DES anchors covering 18 simultaneous metric comparisons. The synthetic experimental design is intentional. Production traces generally confound or omit the individual mechanisms required for controlled identification, including burst correlation, deadline-phase evolution, service-speed variation, cold-start distributions, and capacity-retention decisions. No external, proprietary, or confidential dataset is used. The automated verification script checks file integrity, metadata consistency, customer-flow conservation, activation balance, replication records, CTMC boundary and overflow gates, and simultaneous CTMC–DES validation results. The standalone DES can also be executed as an independent smoke test. The deposited CSV and JSON files constitute the authoritative numerical record for the reported confirmatory and certification results.本代码仓库包含支撑论文《冷启动下感知服务水平协议（Service Level Agreement, SLA）的边云编排随机建模：截止相位调度与弹性配置》的可复现性数据与验证代码。 该研究构建了边云编排随机模型，可联合表征可观测的截止相位、标记批次到达、感知SLA的服务调度、不可取消的随机冷启动、弹性容量激活、保留、释放与排空流程。本研究采用受控合成实验，以分离到达相关性、截止时间演进、服务速率、激活与释放阈值、启动时间分布等因素的影响。 本套件包含可独立执行的显式客户离散事件仿真程序、支撑验证性敏感性分析的选定重复实验层级数值输出、汇总统计量与对比结果、两个易处理基准锚点的精确连续时间马尔可夫链（Continuous-Time Markov Chain, CTMC）-离散事件仿真（Discrete Event Simulation, DES）验证记录、软件依赖说明、执行指南、自动化验证代码以及SHA-256完整性校验值。 本仓库提交的佐证数据包含1560份重复实验层级的观测结果，以及两个覆盖18项同步指标对比的精确CTMC-DES基准锚点。 本合成实验设计具有明确的针对性。实际生产场景的轨迹数据通常会混淆或省略受控识别所需的各类独立机制，包括突发相关性、截止相位演进、服务速率变化、冷启动分布以及容量保留决策。本研究未使用任何外部、专有或机密数据集。 自动化验证脚本可校验文件完整性、元数据一致性、客户流守恒性、激活平衡性、重复实验记录、CTMC边界与溢出门限，以及同步CTMC-DES验证结果。该独立DES程序亦可作为独立冒烟测试执行。本仓库提交的CSV与JSON文件构成了本文报告的验证性与认证性结果的权威数值记录。