分布式实时系统中的预测调度算法

对于分布式实时系统中的周期性任务,人们提出了一系列静态分配调度算法,有效地解决了各种特定条件下的任务分配和调度问题.这些算法的主要特点是,它们均要求被调度任务的特征参数为已知条件.然而在很多实时系统中,周期性任务的运行时间或任务数量常常是一些具有一定规律的随机过程,因而上述静态算法的效能将受到限制.在分析了特定应用背景中的处理流程之后,抽象得到两类随机任务模型,针对这两类模型介绍了在分布式实时系统中已经得到应用的静态分配调度算法SAA(static allocation algorithms),进而提出了多任务分配调度的预测算法PAA(predicting allocation algorithm).它根据周期性任务执行时间或子任务数量的统计特性,实现任务参量的合理预测和多任务的动态调度,以提高系统的实时性能.仿真结果表明,对于两类任务模型,PAA算法与SAA算法相比,在任务完成时间、负载均衡度、系统响应时间及任务夭折率等多方面均有显著改善.     

Decentralized management of bi-modal network resources in a distributed stream processing platform

This paper presents resource management techniques for allocating communication and computational resources in a distributed stream processing platform. The platform is designed to exploit the synergy of two classes of network connections—dedicated and opportunistic. Previous studies we conducted have demonstrated the benefits of such bi-modal resource organization that combines small pools of dedicated computers with a very large pool of opportunistic computing capacities of idle computers to serve high throughput computing applications. This paper extends the idea of bi-modal resource organization into the management of communication resources. Since distributed stream processing applications demand large volume of data transmission between processing sites at a consistent rate, adequate control over the network resources is important to ensure a steady flow of processing. The system model used in this paper is a platform where stream processing servers at distributed sites are interconnected with a combination of dedicated and opportunistic communication links. Two pertinent resource allocation problems are analyzed in detail and solved using decentralized algorithms. One is mapping of the processing and the communication tasks of the stream processing workload on the processing and the communication resources of the platform. The other is the dynamic re-allocation of the communication links due to variations in the capacity of the opportunistic communication links. Overall optimization goal of the allocations is higher task throughput and better utilization of the expensive dedicated links without deviating much from the timely completion of the tasks. The algorithms are evaluated through extensive simulation with a model based on realistic observations. The results demonstrate that the algorithms are able to exploit the synergy of bi-modal communication links towards achieving the optimization goals.     

分布式容错系统的任务分配算法

文章提出了分布式容错系统的任务分配算法,该算法考虑了系统任务的周期性、冗余性特点,以处理机负载平衡为目标,通过三步静态分配实现了任务在处理机中的冗余分布,在系统执行过程中的处理机故障,通过冗余任务动态唤醒实现系统重构。     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

Resource allocation in grid computing

Grid computing, in which a network of computers is integrated to create a very fast virtual computer, is becoming ever more prevalent. Examples include the TeraGrid and Planet-lab.org, as well as applications on the existing Internet that take advantage of unused computing and storage capacity of idle desktop machines, such as Kazaa, SETI@home, Climateprediction.net, and Einstein@home. Grid computing permits a network of computers to act as a very fast virtual computer. With many alternative computers available, each with varying extra capacity, and each of which may connect or disconnect from the grid at any time, it may make sense to send the same task to more than one computer. The application can then use the output of whichever computer finishes the task first. Thus, the important issue of the dynamic assignment of tasks to individual computers is complicated in grid computing by the option of assigning multiple copies of the same task to different computers. We show that under fairly mild and often reasonable conditions, maximizing task replication stochastically maximizes the number of task completions by any time. That is, it is better to do the same task on as many computers as possible, rather than assigning different tasks to individual computers. We show maximal task replication is optimal when tasks have identical size and processing times have a NWU (New Worse than Used; defined later) distribution. Computers may be heterogeneous and their speeds may vary randomly, as is the case in grid computing environments. We also show that maximal task replication, along with a c μ rule, stochastically maximizes the successful task completion process when task processing times are exponential and depend on both the task and computer, and tasks have different probabilities of completing successfully.     

Allocation and scheduling of precedence-related periodic tasks

This paper discusses a static algorithm for allocating and scheduling components of periodic tasks across sites in distributed systems. Besides dealing with the periodicity constraints, (which have been the sole concern of many previous algorithms), this algorithm handles precedence, communication, as well as replication requirements of subtasks of the tasks. The algorithm determines the allocation of subtasks of periodic tasks to sites, the scheduled start times of subtasks allocated to a site, and the schedule for communication along the communication channel(s). Simulation results show that the heuristics and search techniques incorporated in the algorithm are very effective     

Repeated auctions for robust task execution by a robot team

We present empirical results of an auction-based algorithm for dynamic allocation of tasks to robots. The results have been obtained both in simulation and using real robots. A distinctive feature of our algorithm is its robustness to uncertainties and to robot malfunctions that happen during task execution, when unexpected obstacles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are resubmitted for bids every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks, enabling the robots to recover from failures and reducing the overall time for task completion.     

Survey on Grid Resource Allocation Mechanisms

Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing- and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.     

Dynamic threshold for imbalance assessment on load balancing for multicore systems

The introduction of multicore microprocessors has enabled smaller organizations to invest in high performance shared memory parallel systems. These systems ship with standard operating systems using preset thresholds for task imbalance assessment to activate load balancing. Unfortunately, this will unnecessarily trigger task migrations when the number of tasks is a few multiples of the number of processing cores. We illustrate this unnecessary task migration behavior through simulation and introduce a dynamic threshold for task imbalance assessment that is dependent on the number of tasks and the number of processing cores. This is as a replacement for the static threshold that is used by standard operating systems. With the dynamic threshold method, we are able to illustrate a performance gain of up to 17% on a synthetic benchmark and up to 25% gain using the Integer Sort Benchmark from the National Aeronautics and Space Administration (NASA) Advanced Supercomputing Parallel Benchmark Suite.     

计算网格中基于时间均衡的并行粗粒度任务调度算法

考虑网格资源异构、自治、动态等特性,讨论本地用户具有强占优先权情况下的任务调度问题,提出了TBBS(Time-Balancing Based Scheduling Algorithm)算法.建立调度优化模型,以期望完成时间最小为目标选择执行任务的最佳资源组合.以时间均衡策略将任务分解并调度到资源上执行,减少了子任务同步时因等待而产生的延时,获得较好的并行计算性能.采用重复调度策略,适应计算网格中资源的特性.     

智能建筑室内环境分布式可计算WSN任务调度研究

针对智能建筑室内环境下并行计算的动态任务调度问题,构建了基于分布式CPS思想的无线传感器网络(WSN)模型,并分别设计了基于可计算复杂性的任务分配策略和基于动态调度算法的任务调度策略。通过先将任务分配成若干个子任务,采用多带图灵机输入任务,由合适的计算节点进行计算,形成有向无环图,再按调度优先级排列任务,形成任务调度序列表,依序处理任务,从而达到了将任务分配、调度和执行相结合的目的。实验结果表明该策略可有效减少智能建筑室内环境分布式可计算WSN分布运行时任务之间的通讯时间和等待时间,同时提高了任务调度的成功率,最终优化系统的运行效率。     

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.     

嵌入式系统中BP算法多任务调度性能的分析

对于多任务、多进程实时系统中的周期性任务,有一系列静态分配调度算法能有效地解决各种特定条件下的任务分配和调度问题,但这些算法均要求被调度任务的特征参数为已知条件,在很多实时系统中,周期性任务的运行时间或任务数量常常是一些具有一定规律的随机过程,上述静态算法的效能将受到限制。该文描述的神经网络能够充分利用不同时间和空间的数据信息,有较强的学习功能,提高了系统的性能和效率。     

Termination detection for dynamically distributed systems with non-first-in-first-out communication

We propose a new algorithm for detecting termination of distributed systems. The algorithm works correctly whether the system is static or dynamic, whether the interprocess communication is synchronous or asynchronous, and whether the communication channels are first-in-first-out or not. The algorithm requires, in the worst case, O(nm) control messages in all, where n is the number of processes in the system and m is the total number of messages transmitted during the operation of the system. After the system terminates, the algorithm is able to detect the termination using only O(n) control messages; it is optimal if the system concerned is static.     

Assignment and scheduling communicating periodic tasks indistributed real-time systems

Presents an optimal solution to the problem of allocating communicating periodic tasks to heterogeneous processing nodes (PNs) in a distributed real-time system. The solution is optimal in the sense of minimizing the maximum normalized task response time, called the system hazard, subject to the precedence constraints resulting from intercommunication among the tasks to be allocated. Minimization of the system hazard ensures that the solution algorithm allocates tasks so as to meet all task deadlines under an optimal schedule, whenever such an allocation exists. The task system is modeled with a task graph (TG), in which computation and communication modules, communication delays and intertask precedence constraints are clearly described. Tasks described by this TG are assigned to PNs by using a branch-and-bound (B&B) search algorithm. The algorithm traverses a search tree whose leaves correspond to potential solutions to the task allocation problem. We use a bounding method that prunes, in polynomial time, nonleaf vertices that cannot lead to an optimal solution, while ensuring that the search path leading to an optimal solution will never be pruned. For each generated leaf vertex, we compute the exact cost using the algorithm developed by Peng and Shin (1993). The lowest-cost leaf vertex (one with the least system hazard) represents an optimal task allocation. Computational experiences and examples are provided to demonstrate the concept, utility and power of the proposed approach     

Complex Task Allocation in Mobile Surveillance Systems

In mobile surveillance systems, complex task allocation addresses how to optimally assign a set of surveillance tasks to a set of mobile sensing agents to maximize overall expected performance, taking into account the priorities of the tasks and the skill ratings of the mobile sensors. This paper presents a market-based approach to complex task allocation. Complex tasks are the tasks that can be decomposed into subtasks. Both centralized and hierarchical allocations are investigated as winner determination strategies for different levels of allocation and for static and dynamic search tree structures. The objective comparison results show that hierarchical dynamic tree task allocation outperforms all the other techniques especially in complex surveillance operations where large number of robots is used to scan large number of areas.     

同构型分布式计算机系统的启发式任务分配算法

本文讨论一种启发式任务分配方法，称之为改进的ｌｉｓｔ分配方法，它适用于分配一组具有先后关系和通信延迟的任务集到同构型分布式计算机系统上。文中描述了此分配方法的原理和算法，给出相应的仿真流程图，并对具有不同拓扑结构，任务运行时间和通信时间满足多种概率分布的任务集进行了分析和仿真。结果表明，当处理器个数小于任务集的并行度，任务粒度大于５时，任务分配效率大于８０％。     

分布式系统中的动态任务分配算法设计

设计一种分布式系统中的动态任务分配算法，并对它所使用的数据结构、实现方法以及稳定性加以讨论。本算法采用双向启动策略，即发送者和接受者都能进行启动、而且能根据系统总负载和任务等待量等自适应地选择启动策略的使用。同时利用阈值和阈长把系统中的节点分为接受节点，负载适中节点和发送节点、采用启发式方法进行任务分配。     

Task allocation model for distributed systems

The problem of distributing tasks to processors in a distributed computing system is addressed. A task should be assigned to a processor whose capabilities are most appropriate for the execution of that task and excessive interprocessor communication is avoided. A simple algorithm for task allocation is presented. The execution costs and communication costs of the tasks are represented by arrays. A task is either assigned to a processor or fused with another task using a simple criterion. The execution and communication costs are then modified suitably. The process continues until all the tasks are assigned to processors. This algorithm also facilitates incorporation of various system constraints. It is applicable to random program structures and to a system containing any number of processors.     

Optimal Dispersal of Certificate Chains

A regeneration-theory approach is undertaken to analytically characterize the average overall completion time in a distributed system. The approach considers the heterogeneity in the processing rates of the nodes as well as the randomness in the delays imposed by the communication medium. The optimal one-shot load balancing policy is developed and subsequently extended to develop an autonomous and distributed load-balancing policy that can dynamically reallocate incoming external loads at each node. This adaptive and dynamic load balancing policy is implemented and evaluated in a two-node distributed system. The performance of the proposed dynamic load-balancing policy is compared to that of static policies as well as existing dynamic load-balancing policies by considering the average completion time per task and the system processing rate in the presence of random arrivals of the external loads