Due to the variety in available cloud providers along with the frequent changes in strategic objectives of an enterprise, migrating existing software components to the cloud has become a challenging decision in the software maintenance phase. “Financial” and “customer satisfaction” viewpoints are two important strategic objectives of all enterprises that greatly affect the decision about migration to the cloud. Moreover immense number of target cloud services with too many configurations and cost models has made the search space of possible migrations huge. Many existing approaches of software migration to the cloud have modeled the problem as deployment optimization of software components over available platforms, while in this research following a valid migration plan is intended rather than proposing a final optimal migration solution (deployment). A migration plan is a sequence of actions to be taken by the technical team to move the software components to the cloud stepwise. Since at each stage of the migration there might be many valid alternative paths to follow, a recommender module is proposed to direct the management by recommending the best migration plan out of all valid plans in a Labeled Transition System. The recommendation is based on the current state of the enterprise which is estimated using a two-state Hidden Markovian Model by observing ambient signals. The empirical study showed that particularly in dynamic and changing conditions the proposed adaptive and plan-oriented method succeeded in posing lower accrued maintenance costs on the enterprise over time with confidence 90% compared to the non-adaptive method due to its reactive and self-balancing nature.

中文翻译：

---

在动态环境中自适应且经济高效地迁移到云方法

由于可用云提供商的多样性以及企业战略目标的频繁变化，将现有软件组件迁移到云已成为软件维护阶段的一个具有挑战性的决定。“财务”和“客户满意度”的观点是所有企业的两个重要战略目标，极大地影响了迁移到云的决策。此外，海量的目标云服务，配置和成本模型过多，使得可能迁移的搜索空间巨大。许多现有的软件迁移到云的方法将问题建模为软件组件在可用平台上的部署优化，而在本研究中，遵循有效的迁移计划的目的是而不是提出最终的最佳迁移解决方案（部署）。迁移计划是技术团队为将软件组件逐步迁移到云而采取的一系列操作。由于在迁移的每个阶段可能有许多有效的替代路径要遵循，因此建议模块通过从标记转换系统中的所有有效计划中推荐最佳迁移计划来指导管理。该建议基于企业的当前状态，该状态使用二态隐马尔可夫模型通过观察环境信号进行估计。