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A Novel Security-Driven Scheduling Algorithm for Precedence-Constrained Tasks in Heterogeneous Distributed Systems

机译:异构分布式系统中优先约束任务的新型安全驱动调度算法

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摘要

In the recent past, security-sensitive applications, such as electronic transaction processing systems, stock quote update systems, which require high quality of security to guarantee authentication, integrity, and confidentiality of information, have adopted heterogeneous distributed system (HDS) as their platforms. This is primarily due to the fact that single parallel-architecture-based systems may not be sufficient to exploit the available parallelism with the running applications. Most security-aware applications end up in handling dependence tasks, also referred to as Directed Acyclic Graph (DAG), on these HDSs. Unfortunately, most existing algorithms for scheduling such DAGs in HDS fail to fully consider security requirements. In this paper, we systematically design a security-driven scheduling architecture that can dynamically measure the trust level of each node in the system by using differential equations. To do so, we introduce task priority rank to estimate security overhead of such security-critical tasks. Furthermore, we propose a security-driven scheduling algorithm for DAGs which can achieve high quality of security for applications. Our rigorous performance evaluation study results clearly demonstrate that our proposed algorithm outperforms the existing scheduling algorithms in terms of minimizing the makespan, risk probability, and speedup. We also observe that the improvement obtained by our algorithm increases as the security-sensitive data of applications increases.
机译:在最近的过去,对安全敏感的应用程序(例如电子交易处理系统,股票报价更新系统)需要高质量的安全性以确保信息的身份验证,完整性和机密性,并已将异构分布式系统(HDS)用作其平台。 。这主要是由于基于单个并行体系结构的系统可能不足以利用正在运行的应用程序的可用并行性。大多数了解安全的应用程序最终要处理这些HDS上的依赖项任务,也称为有向非循环图(DAG)。不幸的是,大多数现有的用于在HDS中调度此类DAG的算法都无法充分考虑安全性要求。在本文中,我们系统地设计了一种安全性驱动的调度体系结构,该体系结构可以使用微分方程动态地测量系统中每个节点的信任级别。为此,我们引入任务优先级等级,以估算此类安全关键型任务的安全性开销。此外,我们提出了一种针对DAG的安全性驱动调度算法,该算法可为应用程序实现高质量的安全性。我们严格的性能评估研究结果清楚地表明,在最小化制造周期,风险概率和加速方面,我们提出的算法优于现有的调度算法。我们还观察到,随着应用程序对安全性敏感数据的增加,我们的算法获得的改进也随之增加。

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