This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modeling, discrete-event-based simulation, hierarchical modeling, and then use empirically calibrated sensitivity analysis to identify effective strategies. The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risks.
翻译:本条涉及制定高效和可靠的业务保全政策的问题,这些政策确保长期保持比特级信息的完整性,并存在各种现实世界的技术、法律、组织和经济威胁。我们开发了一个系统化的定量预测框架,将正式建模、独立活动模拟、分级建模、分级建模结合起来,然后使用经经验调整的敏感性分析来确定有效的战略。该框架为建立各种保护政策和威胁的模型提供了灵活性。由于这一框架是开放的来源,很容易在云计算环境中部署,因此可以用来根据对特定情景成本、可靠性和风险的独立估计进行分析。