一种可行的分布式硬实时容错调度算法

针对分布式硬实时系统发生处理机故障后,当前周期内的任务实例和后续实例相对截止期限的不同紧迫程度,提出非紧迫周期内延迟策略——DNUP(delay in non-urgent period).该策略能够尽可能地推迟非紧迫实例的执行,使得低优先级实例有更多的机会完成其紧迫周期内的执行,从而实现处理器空闲(slack)资源的合理挪动.仿真实验结果表明,与其他几个著名的分布式容错调度算法相比,DNUP策略能够提高任务的可调度性,从而有效减少了所需处理机的数目.     

Performance-driven processor allocation

In current multiprogrammed multiprocessor systems, to take into account the performance of parallel applications is critical to decide an efficient processor allocation. In this paper, we present the performance-driven processor allocation policy (PDPA). PDPA is a new scheduling policy that implements a processor allocation policy and a multiprogramming-level policy, in a coordinated way, based on the measured application performance. With regard to the processor allocation, PDPA is a dynamic policy that allocates to applications the maximum number of processors to reach a given target efficiency. With regard to the multiprogramming level, PDPA allows the execution of a new application when free processors are available and the allocation of all the running applications is stable, or if some applications show bad performance. Results demonstrate that PDPA automatically adjusts the processor allocation of parallel applications to reach the specified target efficiency, and that it adjusts the multiprogramming level to the workload characteristics. PDPA is able to adjust the processor allocation and the multiprogramming level without human intervention, which is a desirable property for self-configurable systems, resulting in a better individual application response time.     

Static processor allocation in a soft real-time multiprocessorenvironment

Soft real-time environments consist of jobs that must receive service within a particular time interval. If service for a specific job is not completed by the end of its time interval, it is said to be lost; in addition, the computation time expended on the job is wasted, and any further computation for the job is discontinued. The goal of a system designer is to provide an environment that minimizes the number of jobs that are lost. If a parallel environment is available, the system designer has two options: Allow each processor to execute a job individually, or let multiple processors cooperate in executing a job. This article shows, for two classes of static allocation policies, that simple comparative analytical models may be used to indicate which option minimizes the number of lost jobs, as a function of workload intensity. The first class of policies, called equal partitions, statically decomposes the system into equal-size sets of processors and executes one job per partition. These policies are frequently employed in other contexts. The second class of policies, called two partitions, statically partitions the processors into two sets, not necessarily of the same size. Surprisingly, it is observed mathematically that even for statistically identical jobs, this class of policies is superior to equal partitions under certain loadings. The analysis is validated experimentally with a workload executed on a 16-node iPSC/2 hypercube     

Guaranteed Response Times in a Hard-Real-Time Environment

This paper describes a scheduling algorithm for a set of tasks that guarantees the time within which a task, once started, will complete. A task is started upon receipt of an external signal or the completion of other tasks. Each task has a rxed set of requirements in processor time, resources, and device operations needed for completion of its various segments. A worst case analysis of task performance is carried out. An algorithm is developed for determining the response times that can be guaranteed for a set of tasks. Operating system overhead is also accounted for.     

Noncontiguous processor allocation algorithms for mesh-connectedmulticomputers

Current processor allocation techniques for highly parallel systems are typically restricted to contiguous allocation strategies for which performance suffers significantly due to the inherent problem of fragmentation. As a result, message-passing systems have yet to achieve the high utilization levels exhibited by traditional vector supercomputers. We are investigating processor allocation algorithms which lift the restriction on contiguity of processors in order to address the problem of fragmentation. Three noncontiguous processor allocation strategies-paging allocation, random allocation, and the Multiple Buddy Strategy (MBS)-are proposed and studied in this paper. Simulations compare the performance of the noncontiguous strategies with that of several well-known contiguous algorithms. We show that noncontiguous allocation algorithms perform better overall than the contiguous ones, even when message-passing contention is considered. We also present the results of experiments on an Intel Paragon XP/S-15 with 208 nodes that show noncontiguous allocation is feasible with current technologies     

Optimal and suboptimal processor allocation for hypercycle-basedmultiprocessors

Allocating nodes in a concurrent computer system depends on the topology of the system. In this work, we present a number of processor allocation strategies for Hypercycle based concurrent systems. Hypercycles is a class of multidimensional interconnection networks which includes such widely used networks as the binary n-cubes, k-ary n-cubes, generalized hypercubes etc. The allocation strategies presented include a statically optimal first-fit allocation, a suboptimal-first fit, and strategies with extended search space through the inclusion of additional search lists formed by permuting the base through which a hypercycle is defined. For all these strategies, we examine their optimality and present simulation results characterizing their performance relative to each other     

Implementing a dynamic processor allocation policy for multiprogrammed parallel applications in the SolarisTM

Parallel applications typically do not perform well in a multiprogrammed environment that uses time‐sharing to allocate processor resources to the applications' parallel threads. Co‐scheduling related parallel threads, or statically partitioning the system, often can reduce the applications' execution times, but at the expense of reducing the overall system utilization. To address this problem, there has been increasing interest in dynamically allocating processors to applications based on their resource demands and the dynamically varying system load. The Loop‐Level Process Control (LLPC) policy (Yue K, Lilja D. Efficient execution of parallel applications in multiprogrammed multiprocessor systems. 10th International Parallel Processing Symposium, 1996; 448–456) dynamically adjusts the number of threads an application is allowed to execute based on the application's available parallelism and the overall system load. This study demonstrates the feasibility of incorporating the LLPC strategy into an existing commercial operating system and parallelizing compiler and provides further evidence of the performance improvement that is possible using this dynamic allocation strategy. In this implementation, applications are automatically parallelized and enhanced with the appropriate LLPC hooks so that each application interacts with the modified version of the Solaris operating system. The parallelism of the applications are then dynamically adjusted automatically when they are executed in a multiprogrammed environment so that all applications obtain a fair share of the total processing resources. Copyright © 2001 John Wiley & Sons, Ltd.     

Performance of parallel computations with dynamic processor allocation

In parallel adaptive mesh refinement (AMR) computations the problem size can vary significantly during a simulation. The goal here is to explore the performance implications of dynamically varying the number of processors proportional to the problem size during simulation. An emulator has been developed to assess the effects of this approach on parallel communication, parallel runtime and resource consumption. The computation and communication models used in the emulator are described in detail. Results using the emulator with different AMR strategies are described for a test case. Results show for the test case, varying the number of processors, on average, reduces the total parallel communications overhead from 16 to 19% and improves parallel runtime time from 4 to 8%. These results also show that on average resource utilization improves more than 37%.     

Two-level utilization-based processor allocation for scheduling moldable jobs

The Journal of Supercomputing - Most modern parallel programs are written with the moldable property. However, most existing parallel computing systems treat such parallel programs as rigid jobs...     

An effective processor allocation strategy for multiprogrammedshared-memory multiprocessors

Existing techniques for sharing the processing resources in multiprogrammed shared-memory multiprocessors, such as time-sharing, space-sharing, and gang-scheduling, typically sacrifice the performance of individual parallel applications to improve overall system utilization. We present a new processor allocation technique called Loop-Level Process Control (LLPC) that dynamically adjusts the number of processors an application is allowed to use for the execution of each parallel section of code, based on the current system load. This approach exploits the maximum parallelism possible for each application without overloading the system. We implement our scheme on a Silicon Graphics Challenge multiprocessor system and evaluate its performance using applications from the Perfect Club benchmark suite and synthetic benchmarks. Our approach shows significant improvements over traditional time-sharing and gang-scheduling. It has performance comparable to, or slightly better than, static space-sharing, but our strategy is more robust since, unlike static space-sharing, it does not require a priori knowledge of the applications' parallelism characteristics     

A top-down processor allocation scheme for hypercube computers

An efficient processor allocation policy is presented for hypercube computers. The allocation policy is called free list since it maintains a list of free subcubes available in the system. An incoming request of dimension k (2^k nodes) is allocated by finding a free subcube of dimension k or by decomposing an available subcube of dimension greater than k. This free list policy uses a top-down allocation rule in contrast to the bottom-up approach used by the previous bit-map allocation algorithms. This allocation scheme is compared to the buddy, gray code (GC), and modified buddy allocation policies reported for the hypercubes. It is shown that the free list policy is optimal in a static environment, as are the other policies, and it also gives better subcube recognition ability compared to the previous schemes in a dynamic environment. The performance of this policy, in terms of parameters such as average delay, system utilization, and time complexity, is compared to the other schemes to demonstrate its effectiveness. The extension of the algorithm for parallel implementation, noncubic allocation, and inclusion/exclusion allocation is also given     

Dynamic processor allocation in scalable multiprocessors using boolean algebra *

In this paper, we study a new model for dynamic processor allocation in k-ary n-dimensional mesh or torus multiprocessors. The model uses Boolean functions to represent free processors and allocates processors by applying Boolean operations on the functions. The processor allocation algorithms based on the Boolean model can be implemented easily using binary decision diagrams(BDDs)and related software packages. To enhance the efficiency of the allocation algorithms, a reordering procedure will be introduced to change the ordering of Boolean variables in the BDD representation and thereby change the free subcube composition. Such a change leads to an improved free processor recognition capability. Complexities of the proposed allocation algorithms will be analyzed. Performance of the algorithms will be evaluated using simulation and compared with other approaches.     

A new processor allocation strategy with a high degree of contiguity in mesh-connected multicomputers

Relaxing the contiguity condition in non-contiguous allocation can reduce processor fragmentation and increase processor utilization. However, communication overhead could increase due to the potential increase in message distances. The communication overhead depends on how the allocation request is partitioned and allocated to free sub-meshes. In this paper, a new non-contiguous processor allocation strategy, referred to as Greedy-Available-Busy-List (GABL for short), is suggested for the mesh network, and is compared against the existing non-contiguous and contiguous allocation strategies. To demonstrate the performance gains achieved by our proposed strategy, we have conducted simulation runs under the assumption of wormhole routing technique. The results have revealed that the new strategy can reduce communication overhead and considerably improve performance in terms of the job turnaround time, system utilization, and jobs finish time.     

Optimal allocation in a multiresources graduated tariff communication environment

The designer of computer networks is often confronted with the problem of the optimal allocation of multiple communications resources, subject to a graduated tariff. Such optimality criteria for the correct mix of facilities for use in system design are obtained. The paper gives examples from data communications.     

An efficient recognition-complete processor allocation strategy fork-ary n-cube multiprocessors

Composed of various topologies, the k-ary n-cube system is desirable for accepting and executing topologically different tasks. To utilize its large amount of processor resources, several allocation strategies have been reported, each with certain restrictions that affect performance. For improvement, we propose a new allocation strategy for the k-ary n-cubes. The proposed strategy is an extension of the TC strategy for hypercubes and is able to recognize all subcubes with different topologies requested by tasks. Complexity analysis and performance comparison between related strategies are provided to demonstrate their advantages and disadvantages. Simulation results show that with full subcube recognition ability and no internal fragmentation, our strategy always exhibits better performance     

Limited contiguous processor allocation mechanism in the mesh-connected multiprocessors using compaction

In this paper, several efficient migration and allocation strategies have been compared on the mesh-based multiprocessor systems. The traditional non-preemptive submesh allocation strategies consist of two row boundary (TRB) and two column boundary (TCB). The existing migration mechanisms are online dynamic compaction-four corner (ODC-FC), limited top-down compaction (LTDC), TCB, and the combination of TCB and ODC-FC algorithms. Indeed, the new allocation method is presented in this paper. This mechanism has the benefits of two efficient traditional allocation algorithms. It is the combination of the TCB and TRB allocation methods. Also, in this process the impact of four key metrics on online mapping is considered. The parameters are average task execution time (ATET), average task system utilization (ATSU), average task waiting time (ATWT), and average task response time (ATRT). Using TCB and TRB mechanism with the migration strategies is shown that the new algorithm has better ATET, ATRT, ATWT, and ATSU. It has, respectively, 23.5494, 97.1216, 39.1291, and 4.142% improvements in comparison with the previous mechanisms.     

Optimal allocation of a work force in a toxic substance environment

Both industrial firms and governmental agencies are concerned with the high risks and costs of safety management in the work place. Manufacturers are interested in (1) operating efficiently in situations where toxic substances affect workers and (2) complyinhg with OSHA regulations. However, technological advances in manufacturing processes produce changes in the number, varieties and levels of toxic substances that affect manufacturing operations.

An approach to model and solve the allocation and scheduling of a work force in a toxic substance environment as a linear progrmaming problem is presented. The firm's objective is to maximize utilization of the work force, without violating any OSHA standards.

A variety of operational situations are analyzed to illustrate the modeling approach, the use of a low cost commercially available software package for a microcomputer and the results obtained.     

Analysis of processor allocation in multiprogrammed,distributed-memory parallel processing systems

A main objective of scheduling independent jobs composed of multiple sequential tasks in shared-memory and distributed-memory multiprocessor computer systems is the assignment of these tasks to processors in a manner that ensures efficient operation of the system. Achieving this objective requires the analysis of a fundamental tradeoff between maximizing parallel execution, suggesting that the tasks of a job be spread across all system processors, and minimizing synchronization and communication overheads, suggesting that the job's tasks be executed on a single processor. The authors consider a class of scheduling policies that represent the essential aspects of this processor allocation tradeoff, and model the system as a distributed fork-join queueing system. They derive an approximation for the expected job response time, which includes the important effects of various parallel processing overheads (such as task synchronization and communication) induced by the processor allocation policy