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Optimization of time constrained N-version programming service components with competing task execution and version corruption processes

机译:具有竞争任务执行和版本破坏过程的时间受约束的N版本编程服务组件的优化

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This paper models a software service component implementing the N-version programming (NVP) redundancy on the cloud computing platform to enhance the service reliability. Specifically, multiple versions of the same service component are activated in parallel on different servers of the cloud to perform the requested service. At required service response time, the output is determined based on a threshold first-past-the-post voting rule (output with the most votes and the number of these votes exceeds a predetermined threshold). However, effectiveness of the NVP approach can be greatly compromised by co-residence attacks, a common type of cyber-attacks launched to corrupt user's service through co-residing user's and attacker's virtual machines on the same cloud server. This paper formulates and solves an optimization problem, particularly, a minmax game problem that finds the number of service component versions (SCVs) and the threshold to maximize the user's utility while considering a strategic attack behavior aiming to maximize the attacker's utility. The solution methodology encompasses a probabilistic model of evaluating the service success probability (SSP) and corruption attack success probability (CAP), two performance metrics used in the computation of the user's and attacker's utilities. Examples are analyzed to demonstrate influences of different model parameters on SSP, CAP, and solutions to the considered optimization problem.
机译:本文对在云计算平台上实现N版本编程(NVP)冗余的软件服务组件进行建模,以提高服务可靠性。具体而言,在云的不同服务器上并行激活同一服务组件的多个版本,以执行请求的服务。在所需的服务响应时间,基于阈值先后投票规则(输出的投票最多且这些投票的数量超过预定阈值)确定输出。但是,共存攻击会严重损害NVP方法的有效性,共存攻击是通过在同一云服务器上同时驻留用户和攻击者的虚拟机而发起的破坏用户服务的常见网络攻击。本文提出并解决了一个优化问题,尤其是minmax游戏问题,该问题找到服务组件版本(SCV)的数量和阈值以最大化用户的效用,同时考虑旨在最大化攻击者效用的战略攻击行为。该解决方案方法包括一个概率模型,用于评估服务成功概率(SSP)和破坏攻击成功概率(CAP),这两个性能度量用于计算用户和攻击者的效用。对示例进行了分析,以说明不同模型参数对SSP,CAP和所考虑的优化问题的解决方案的影响。

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