异构并行分布式系统可信调度理论与方法研究
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摘要
随着社会经济与信息技术的飞速发展,以网络计算和并行计算为基础的分布式计算已成为IT技术的重要发展方向,特别是异构并行分布式系统因其低成本高性能而受到企业界和学术界的广泛关注。但系统规模持续扩大的同时,系统平均无故障时间却越来越低,任务执行行为可靠性已成为保证并行应用成功的关键。其次,分布式系统为实现信息保密性、数据完整性和边界安全保护而面临大量安全威胁,这些威胁主要源于网络环境下资源与用户行为的不可控性和不确定性。另一方面,并行应用实践表明任务实际执行时间往往受输入数据、系统环境和任务本身If分支的影响而具有随机性。面对由可靠性、安全性、任务计算量随机性等导致的分布式系统可信性,提出了考虑可信性的任务调度理论与方法,以期对对提高分布式系统应用性能具有重要意义的资源管理展开深入研究与探索,力图解决其中的部分关键理论与技术问题。
本文首先建立由任务DAG模型构建器、任务计算量评估、任务调度器、任务分配、信任管理、信任值计算、可靠性分析、安全开销计算、任务可信评估等模块构成的可信调度体系结构。针对异构计算系统任务在不同处理机上执行时其计算成本不同的特点,克服以任务执行平均值、中间值、最好值或最坏值等方法给任务调度带来的困惑,提出异构计算系统计算能力异构因子α,并依此实现任务优先级计算和处理机选择。在此基础上,提出基于任务复制的表调度算法(HEFD)。模拟实验结果表明任务调度算法HEFD优于HEFT、HLD和HCPFD算法。
其次,针对大规模分布式计算系统的异构性、动态性和广域性等特点,提出可靠性驱动的层次调度体系。分析任务在处理机上执行的可靠性、数据在通信网络上通信的可靠性及相互关系,建立应用程序任务执行行为可靠性模型。在此基础上提出可靠性驱动的分层任务调度算法(HRDS)。其中,全局任务调度器负责把应用程序分配给虚拟节点,局部调度器则在虚拟节点内实现基于DAG模型的任务调度与分配。实验结果表明相对于经典调度算法MCMS和HEFT,HRDS具有较好的性能。
针对分布式计算系统面临的安全威胁,受经济学品牌形象实践与心理学理论启发,在研究分布式信任表现形式与特点基础上提出基于博弈论微分对策技术的信任值动态量化计算方法。依据用户任务安全需求和分布式系统提供的安全信任保障,提出任务执行行为安全性开销计算方法和安全性风险评估技术。最后,提出考虑任务执行行为安全开销的调度模型和调度算法SDS。实验结果表明SDS算法不仅能有效提高系统安全性,还具有较好的调度性能。
经典任务调度算法一般假定任务计算量通过软件工程配置技术或预测技术获得,且是确定不变值。而并行应用计算实践却表明任务计算量具有随机性,这种不精确性将明显影响调度算法性能。因而针对此问题,本文首先证明基于DAG模型随机任务调度长度的下限是以任务期望构成的确定型任务调度长度。然后,研究任务计算量服从正态分布的应用程序,利用Clark方程实现并行任务完成时间期望与方差的计算,在此基础上提出随机sb_level近似计算算法。受确定型表调度算法DLS启发,提出针对随机任务调度问题的随机动态级调度算法(SDLS)。实验结果表明随机动态级调度算法具有较好的调度性能。
With the advent of new high-speed networks, it is now possible to connect a collection of distributed, cost-effective, and possibly heterogeneous resources in the form of a heterogeneous parallel distributed systems, such as Grid Computing, Cloud computing, P2P Computing, Web Service, etc. Heterogeneous distributed processing is a promising approach to meet the computational requirements and data processing of a large number of current and emerging applications including information processing, electronic transaction processing systems, stock quote update systems, E-commerce online services, digital government, fluid flow, weather modeling, and image processing. However, to fully exploit and effectively use heterogeneous computing systems, many problems, such as resource management, task scheduling, task processing time with random, system reliability, security, must be carefully dealt with.
To solve these problems, we first build a trusted scheduling architecture, which include the model such as Task DAG Model, Task Processing Evaluation, Task Scheduler, Trust Manager, Trust Value Computation, Security Overhead, Reliability Analysis, and Trusted Evaluation. Then, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target heterogeneous computing systems using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.
Thirdly, parallel distributed applications executing in large-scale heterogeneous distributed systems, such as Grid Computing, inevitably encounter different types of failures such as hardware failure, program failure, and storage failure. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing Grid scheduling algorithms do not adequately consider the reliability requirements of an application. In recognition of this problem, we design a hierarchical reliability-driven scheduling architecture that includes both a local scheduler and a global scheduler. The local scheduler aims to effectively measure task reliability of an application in a Grid virtual node and incorporate the precedence constrained tasks'reliability overhead into a heuristic scheduling algorithm. In the global scheduler, we propose a hierarchical reliability-driven scheduling algorithm based on quantitative evaluation of independent application reliability. Our experiments, based on both randomly generated graphs and the graphs of some real applications, show that our hierarchical scheduling algorithm performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.
Fourthly, in heterogeneous distributed systems, security-sensitive applications will run at a lower security levels, thereby leading to low quality of security. Another, security-sensitive applications will be at a higher security levels with higher security overheads, which can result in poor performance. These two scenarios could happen because nonsecurity-driven schedulers do not take security overheads into account while making scheduling decisions. To solve this problem, 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.
Fifthly, most of the existing researches focus on deterministic version of scheduling problem with deterministic processing time. In practice, the task processing time is not deterministic and acts as random variables. Thus, we first build a stochastic scheduling model and prove that the lower bound of expected schedule length is that all the tasks'processing time are replaced by their expected values. Then, we propose a method of computing the stochastic tasks path length for series-parallel model based on Clark's equations. To solve the problem efficiently, we propose a Stochastic Dynamic Level Scheduling algorithm(SDLS), which based on stochastic bottom level sb_levels and stochastic dynamic level (SDL). Our rigorous performance evaluation study results clearly demonstrate that the proposed stochastic scheduling algorithm significantly outperforms existing algorithm in terms of makespan, speedup, and makespan standard deviation.
本文首先建立由任务DAG模型构建器、任务计算量评估、任务调度器、任务分配、信任管理、信任值计算、可靠性分析、安全开销计算、任务可信评估等模块构成的可信调度体系结构。针对异构计算系统任务在不同处理机上执行时其计算成本不同的特点,克服以任务执行平均值、中间值、最好值或最坏值等方法给任务调度带来的困惑,提出异构计算系统计算能力异构因子α,并依此实现任务优先级计算和处理机选择。在此基础上,提出基于任务复制的表调度算法(HEFD)。模拟实验结果表明任务调度算法HEFD优于HEFT、HLD和HCPFD算法。
其次,针对大规模分布式计算系统的异构性、动态性和广域性等特点,提出可靠性驱动的层次调度体系。分析任务在处理机上执行的可靠性、数据在通信网络上通信的可靠性及相互关系,建立应用程序任务执行行为可靠性模型。在此基础上提出可靠性驱动的分层任务调度算法(HRDS)。其中,全局任务调度器负责把应用程序分配给虚拟节点,局部调度器则在虚拟节点内实现基于DAG模型的任务调度与分配。实验结果表明相对于经典调度算法MCMS和HEFT,HRDS具有较好的性能。
针对分布式计算系统面临的安全威胁,受经济学品牌形象实践与心理学理论启发,在研究分布式信任表现形式与特点基础上提出基于博弈论微分对策技术的信任值动态量化计算方法。依据用户任务安全需求和分布式系统提供的安全信任保障,提出任务执行行为安全性开销计算方法和安全性风险评估技术。最后,提出考虑任务执行行为安全开销的调度模型和调度算法SDS。实验结果表明SDS算法不仅能有效提高系统安全性,还具有较好的调度性能。
经典任务调度算法一般假定任务计算量通过软件工程配置技术或预测技术获得,且是确定不变值。而并行应用计算实践却表明任务计算量具有随机性,这种不精确性将明显影响调度算法性能。因而针对此问题,本文首先证明基于DAG模型随机任务调度长度的下限是以任务期望构成的确定型任务调度长度。然后,研究任务计算量服从正态分布的应用程序,利用Clark方程实现并行任务完成时间期望与方差的计算,在此基础上提出随机sb_level近似计算算法。受确定型表调度算法DLS启发,提出针对随机任务调度问题的随机动态级调度算法(SDLS)。实验结果表明随机动态级调度算法具有较好的调度性能。
With the advent of new high-speed networks, it is now possible to connect a collection of distributed, cost-effective, and possibly heterogeneous resources in the form of a heterogeneous parallel distributed systems, such as Grid Computing, Cloud computing, P2P Computing, Web Service, etc. Heterogeneous distributed processing is a promising approach to meet the computational requirements and data processing of a large number of current and emerging applications including information processing, electronic transaction processing systems, stock quote update systems, E-commerce online services, digital government, fluid flow, weather modeling, and image processing. However, to fully exploit and effectively use heterogeneous computing systems, many problems, such as resource management, task scheduling, task processing time with random, system reliability, security, must be carefully dealt with.
To solve these problems, we first build a trusted scheduling architecture, which include the model such as Task DAG Model, Task Processing Evaluation, Task Scheduler, Trust Manager, Trust Value Computation, Security Overhead, Reliability Analysis, and Trusted Evaluation. Then, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target heterogeneous computing systems using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.
Thirdly, parallel distributed applications executing in large-scale heterogeneous distributed systems, such as Grid Computing, inevitably encounter different types of failures such as hardware failure, program failure, and storage failure. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing Grid scheduling algorithms do not adequately consider the reliability requirements of an application. In recognition of this problem, we design a hierarchical reliability-driven scheduling architecture that includes both a local scheduler and a global scheduler. The local scheduler aims to effectively measure task reliability of an application in a Grid virtual node and incorporate the precedence constrained tasks'reliability overhead into a heuristic scheduling algorithm. In the global scheduler, we propose a hierarchical reliability-driven scheduling algorithm based on quantitative evaluation of independent application reliability. Our experiments, based on both randomly generated graphs and the graphs of some real applications, show that our hierarchical scheduling algorithm performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.
Fourthly, in heterogeneous distributed systems, security-sensitive applications will run at a lower security levels, thereby leading to low quality of security. Another, security-sensitive applications will be at a higher security levels with higher security overheads, which can result in poor performance. These two scenarios could happen because nonsecurity-driven schedulers do not take security overheads into account while making scheduling decisions. To solve this problem, 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.
Fifthly, most of the existing researches focus on deterministic version of scheduling problem with deterministic processing time. In practice, the task processing time is not deterministic and acts as random variables. Thus, we first build a stochastic scheduling model and prove that the lower bound of expected schedule length is that all the tasks'processing time are replaced by their expected values. Then, we propose a method of computing the stochastic tasks path length for series-parallel model based on Clark's equations. To solve the problem efficiently, we propose a Stochastic Dynamic Level Scheduling algorithm(SDLS), which based on stochastic bottom level sb_levels and stochastic dynamic level (SDL). Our rigorous performance evaluation study results clearly demonstrate that the proposed stochastic scheduling algorithm significantly outperforms existing algorithm in terms of makespan, speedup, and makespan standard deviation.
引文
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