Backtracking-Based Instruction Scheduling to Fill Branch Delay Slots

Conventional schedulers schedule operations in dependence order and never revisit or undo a scheduling decision on any operation. In contrast, backtracking schedulers may unschedule operations and can often generate better schedules. This paper develops and evaluates the backtracking approach to fill branch delay slots. We first present the structure of a generic backtracking scheduling algorithm and prove that it terminates. We then describe two more aggressive backtracking schedulers and evaluate their effectiveness. We conclude that aggressive backtracking-based instruction schedulers can effectively improve schedule quality by eliminating branch delay slots with a small amount of additional computation.     

Grid Resource Availability Prediction-Based Scheduling and Task Replication

The frequent and volatile unavailability of volunteer-based Grid computing resources challenges Grid schedulers to make effective job placements. The manner in which host resources become unavailable will have different effects on different jobs, depending on their runtime and their ability to be checkpointed or replicated. A multi-state availability model can help improve scheduling performance by capturing the various ways a resource may be available or unavailable to the Grid. This paper uses a multi-state model and analyzes a machine availability trace in terms of that model. Several prediction techniques then forecast resource transitions into the model’s states. We analyze the accuracy of our predictors, which outperform existing approaches. We also propose and study several classes of schedulers that utilize the predictions, and a method for combining scheduling factors. We characterize the inherent tradeoff between job makespan and the number of evictions due to failure, and demonstrate how our schedulers can navigate this tradeoff under various scenarios. Lastly, we propose job replication techniques, which our schedulers utilize to replicate those jobs that are most likely to fail. Our replication strategies outperform others, as measured by improved makespan and fewer redundant operations. In particular, we define a new metric for replication efficiency, and demonstrate that our multi-state availability predictor can provide information that allows our schedulers to be more efficient than others that blindly replicate all jobs or some static percentage of jobs.     

Optimal Modulo Scheduling Through Enumeration

Resource-constrained software-pipelining has played an increasingly significant role in exploiting instruction-level parallelism and has drawn intensive academic and industrial interest. The challenge is to find a schedule which is optimal : i.e., given the data dependence graph (DDG) for a loop, find the fastest possible schedule under given resource constraints while keeping register usage minimal. This paper proposes a novel enumeration based modulo scheduling approach to solve this problem. The proposed approach does not require any awkward reworking of constraints into linear form and employs a realistic register model. The set of schedules enumerated also allows us to characterize the schedule space and address questions such as whether schedules using a small number of registers tend to require a large number of function units. The proposed approach has been implemented under the MOST testbed at McGill University. Experimental results on more than 1000 loops from popular benchmark programs show that enumeration is generally faster at obtaining optimal schedules than integer linear programming approaches. Compared to Huff's Slack Scheduling , enumeration found a faster schedule for almost 15% of loops, with a mean improvement of 18%. 10% of the remaining loops required fewer registers under enumeration, with a mean reduction of 16%.     

Cello: A Disk Scheduling Framework for Next Generation Operating Systems*

In this paper, we present the Cello disk scheduling framework for meeting the diverse service requirements of applications. Cello employs a two-level disk scheduling architecture, consisting of a class-independent scheduler and a set of class-specific schedulers. The two levels of the framework allocate disk bandwidth at two time-scales: the class-independent scheduler governs the coarse-grain allocation of bandwidth to application classes, while the class-specific schedulers control the fine-grain interleaving of requests. The two levels of the architecture separate application-independent mechanisms from application-specific scheduling policies, and thereby facilitate the co-existence of multiple class-specific schedulers. We demonstrate that Cello is suitable for next generation operating systems since: (i) it aligns the service provided with the application requirements, (ii) it protects application classes from one another, (iii) it is work-conserving and can adapt to changes in work-load, (iv) it minimizes the seek time and rotational latency overhead incurred during access, and (v) it is computationally efficient.     

Quantum-Based Earliest Deadline First Scheduling for Multiservices

Latency-rate (LR) schedulers have shown their ability in providing fair and weighted sharing of bandwidth with an upper bound on delivery latency of packets while earliest departure first (EDF) schedulers have shown their ability in providing LR-decoupled service whereby the delivery latency of packets is not bounded by the reserved rate. However, EDF schedulers require traffic shapers to ensure flow protection. We propose quantum-based earliest deadline first scheduling (QEDF), a quantum-based scheduler that provides flow protection, throughput guarantee and delay bound guarantee for flows that require LR-coupled and LR-decoupled types of reservations. It classifies flows into time-critical (TC), jitter-sensitive (JS), and rate-based (RB) classes and uses a quality-of-service forwarding rule to determine the next packet to be serviced by the scheduler. It provides nonpreemptive priority service to TC queues. This allows LR-decoupled reservation for flows that have a low rate and intolerable delay. Packets from JS queues can be delayed by other packets if forwarding the latter will not result in the former missing its deadline. As a quantum-based scheduler, the QEDF scheduler provides throughput guarantees for RB queues. We present both analytical and simulation results of QEDF, whereby we evaluated QEDF in its deployment as a single-class as well as a multiservice scheduler     

Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids

We address non-preemptive non-clairvoyant online scheduling of parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. We analyze allocation strategies depending on the type and amount of information they require. We conduct a comprehensive performance evaluation study using simulation and demonstrate that our strategies perform well with respect to several metrics that reflect both user- and system-centric goals. Unfortunately, user run time estimates and information on local schedules does not help to significantly improve the outcome of the allocation strategies. When examining the overall Grid performance based on real data, we determined that an appropriate distribution of job processor requirements over the Grid has a higher performance than an allocation of jobs based on user run time estimates and information on local schedules. In general, our experiments showed that rather simple schedulers with minimal information requirements can provide a good performance.     

A distributed scheduler for air traffic flow management

A system was developed to efficiently schedule aircraft into congested resources over long ranges and present that schedule as a decision support system. The scheduling system consists of a distributed network of independent schedulers, loosely coupled by sharing capacity information. This loose coupling insulates the schedules from uncertainty in long-distance estimations of arrival times, while allowing precise short-term schedules to be constructed. This ??rate profile?? mechanism allows feasible schedules to be produced over long ranges, essentially constructing precise short-range schedules that also ensure that future scheduling problems are solvable while meeting operational constraints. The system was tested operationally and demonstrated reduced airborne delay and improved coordination.     

分布式实时嵌入式中间件的调度体系

在实时CORBA1.2的基础上扩展了一种分布式调度体系,这种调度体系可以获取全局信息,从而本地调度器可以获得更加准确的调度信息.这里只讨论本地调度器使用截止期调度策略的情况,因此这种调度器可以使得分布式任务在截止期之前完成的可能性增大.如果本地调度器使用其他的调度算法,可以在这种体系中给出相应的机制来提供更准确的调度信息.文中主要描述了这种调度体系的设计与实现.     

Stable Scheduling Policies for Maximizing Throughput in Generalized Constrained Queueing Systems

We consider a class of queueing networks referred to as "generalized constrained queueing networks" which form the basis of several different communication networks and information systems. These networks consist of a collection of queues such that only certain sets of queues can be concurrently served. Whenever a queue is served, the system receives a certain reward. Different rewards are obtained for serving different queues, and furthermore, the reward obtained for serving a queue depends on the set of concurrently served queues. We demonstrate that the dependence of the rewards on the schedules alter fundamental relations between performance metrics like throughput and stability. Specifically, maximizing the throughput is no longer equivalent to maximizing the stability region; we therefore need to maximize one subject to certain constraints on the other. Since stability is critical for bounding packet delays and buffer overflow, we focus on maximizing the throughput subject to stabilizing the system. We design provably optimal scheduling strategies that attain this goal by scheduling the queues for service based on the queue lengths and the rewards provided by different selections. The proposed scheduling strategies are however computationally complex. We subsequently develop techniques to reduce the complexity and yet attain the same throughput and stability region. We demonstrate that our framework is general enough to accommodate random rewards and random scheduling constraints.     

VMSF—一种内核级虚拟机监控器调度框架

虚拟化技术由于具有提高资源利用率、降低系统总体拥有成本等优点得到越来越多的关注。虚拟机成为计算机系统的一种新型应用模式,但虚拟机应用在服务质量保证和协同运行等方面与传统商用操作系统面向的应用不同,虚拟机监控器应针对此类应用的特点设计相应的调度算法。但是,在传统基于宿主操作系统的虚拟化技术中,虚拟机的调度由宿主操作系统的标准调度器完成。本文提出一种不修改宿主操作系统现有调度机制的虚拟机调度扩展框架VMSF,该框架允许第三方自行开发适于虚拟机系统的调度算法。最后通过在Linux上开源的内核级虚拟机监控器KVM上移植Xen的Credit调度器验证了本文研究的有效性。     

Self-Learning Disk Scheduling

Performance of disk I/O schedulers is affected by many factors, such as workloads, file systems, and disk systems. Disk scheduling performance can be improved by tuning scheduler parameters, such as the length of read timers. Scheduler performance tuning is mostly done manually. To automate this process, we propose four self-learning disk scheduling schemes: Change-sensing Round-Robin, Feedback Learning, Per-request Learning, and Two-layer Learning. Experiments show that the novel Two-layer Learning Scheme performs best. It integrates the workload-level and request-level learning algorithms. It employs feedback learning techniques to analyze workloads, change scheduling policy, and tune scheduling parameters automatically. We discuss schemes to choose features for workload learning, divide and recognize workloads, generate training data, and integrate machine learning algorithms into the Two-layer Learning Scheme. We conducted experiments to compare the accuracy, performance, and overhead of five machine learning algorithms: Decision Tree, Logistic Regression, Naïve Bayes, Neural Network, and Support Vector Machine Algorithms. Experiments with real-world and synthetic workloads show that self-learning disk scheduling can adapt to a wide variety of workloads, file systems, disk systems, and user preferences. It outperforms existing disk schedulers by as much as 15.8% while consuming less than 3%-5% of CPU time.     

Artificial Neural Network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments

Monitoring of the system performance in highly distributed computing environments is a wide research area. In cloud and grid computing, it is usually restricted to the utilization and reliability of the resources. However, in today’s Computational Grids (CGs) and Clouds (CCs), the end users may define the special personal requirements and preferences in the resource and service selection, service functionality and data access. Such requirements may refer to the special individual security conditions for the protection of the data and application codes. Therefore, solving the scheduling problems in modern distributed environments remains still challenging for most of the well known schedulers, and the general functionality of the monitoring systems must be improved to make them efficient as schedulers supporting modules.In this paper, we define a novel model of security-driven grid schedulers supported by an Artificial Neural Network (ANN). ANN module monitors the schedule executions and learns about secure task–machine mappings from the observed machine failures. Then, the metaheuristic grid schedulers (in our case—genetic-based schedulers) are supported by the ANN module through the integration of the sub-optimal schedules generated by the neural network, with the genetic populations of the schedules.The influence of the ANN support on the general schedulers’ performance is examined in the experiments conducted for four types of the grid networks (small, medium, large and very large grids), two security scheduling modes—risky and secure scenarios, and six genetic-based grid schedulers. The generated empirical results show the high effectiveness of such monitoring support in reducing the values of the major scheduling criteria (makespan and flowtime), the run times of the schedulers and the grid resource failures.     

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers appropriate to each specific architecture implementation to achieve the best possible program speed. However, these heuristic schedulers are time-consuming and expensive to build. We present empirical results using both rollouts and reinforcement learning to construct heuristics for scheduling basic blocks. In simulation, the rollout scheduler outperformed a commercial scheduler on all benchmarks tested, and the reinforcement learning scheduler outperformed the commercial scheduler on several benchmarks and performed well on the others. The combined reinforcement learning and rollout approach was also very successful. We present results of running the schedules on Compaq Alpha machines and show that the results from the simulator correspond well to the actual run-time results.     

An efficient search method for job-shop scheduling problems

We present an efficient search method for job-shop scheduling problems. Our technique is based on an innovative way of relaxing and subsequently reimposing the capacity constraints on some critical operations. We integrate this technique into a fast tabu search algorithm. Our computational results on benchmark problems show that this approach is very effective. Upper bounds for 11 well-known test problems are thus improved. Through the work presented We hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The peripheral conditions for such a system are ripe with the increasingly widespread adoption of enterprise information systems and plant floor tracking systems based on bar code or wireless technologies. One of the remaining obstacles, however, is the fact that scheduling problems arising from many production environments, including job-shops, are extremely difficult to solve. Motivated by recent success of local search methods in solving the job-shop scheduling problem, we propose a new diversification technique based on relaxing and subsequently reimposing the capacity constraints on some critical operations. We integrate this technique into a fast tabu search algorithm and are able to demonstrate its effectiveness through extensive computational experiments. In future research, we will consider other diversification techniques that are not restricted to critical operations.     

Scheduling of hard real-time multi-phase multi-thread (MPMT) periodic tasks

In this paper we study the scheduling of parallel and real-time recurrent tasks on multiprocessor platforms. Firstly, we propose a new parallel task model which allows recurrent tasks to be composed of several phases, each one composed of several threads. Each thread requires a single processor for execution and can be scheduled simultaneously. We then propose an algorithm to transpose popular Fork-Join task model to our MPMT task model. Secondly, we define several kinds of real-time schedulers that can be applied to our parallel task model. We distinguish between two scheduling classes: Hierarchical schedulers and Global Thread schedulers. We present and prove correct an exact schedulability test for each class. Lastly, we also evaluate the performance of our scheduling paradigm in comparison with Gang scheduling by means of simulations. In this work we extend the work of Lupu and Goossens in Scheduling of hard real-time multi-thread periodic tasks (Real-Time and Network Systems, 2011) which considers mono-phase multi-thread task model. We extend their previous results to a Multi-Phase Multi-Thread task model.     

动态选择时钟的资源约束下调度算法

为拓展数据流综合可搜索解空间,使资源约束下的调度结果更加接近全局最优,提出一种动态选择时钟周期的资源约束下调度算法.在资源约束调度过程中,通过对单周期、多周期和链式操作进行组合来计算备选时钟;在调度过程中选择能够充分利用元件资源,并可减小数据通道延迟时间的时钟周期,最终完成最佳时钟下的资源约束下调度.该算法将资源约束的影响引入时钟周期的选择,可得到能够真正提高性能的最优时钟;在时钟选择过程中完成资源约束下调度,使调度和时钟选择同时完成,保证调度结果的全局最优性.实验结果表明,采用文中算法得到的时钟周期和调度结果保证了资源约束条件下的数据通道延时最小.     

Cautious transaction schedulers for database concurrency control

Cautious schedulers, which never resort to rollbacks for the purpose of concurrency control, are investigated. In particular, cautious schedulers for classes WW consisting of schedules serializable under the write-write constraints, and WRW, a superclass of W, are considered. The cautious WW-scheduler has a number of nice properties, one of which is the existence of a polynomial-time scheduling algorithm. Since cautious WRW-scheduling is, in general, NP-complete, some restrictions are introduced which allow polynomial-time scheduling. All of these cautious schedulers are based on the assumption that transaction predeclare their read and write sets on arrival. Anomalies which occur when transaction modify their read sets or write sets during execution are discussed and countermeasures are proposed     

Multiple Robot Rearrangement Planning Using a Territorial Approach and an Extended Project Scheduling Problem Solver

In this paper, we address a multiple robot rearrangement problem. For different applications, problem-solving methods should be able to cope with various working environments. We focus on small working environments in particular with a concentrated arrangement of objects and narrow corridors. In this type of environment, the rearrangement problem can be very complicated because of high computational cost for priority settings to prevent robots from colliding and constraints related to the order of transportation. We propose a practical algorithm that divides a complicated rearrangement problem into simple subproblems. In our method, the rearrangement problem can be reduced to a project scheduling problem using a territorial approach. The application of a territorial approach can relax the complexity of priority settings, but yields new kinds of constraints at the same time. We propose an extended project scheduling problem solver to address these constraints. The solver is constructed on the basis of meta-heuristic strategy and generates the order of transportation that observes constraints. The proposed method is tested in a simulated environment with up to four mobile robots and 12 movable objects. Simulation results show the effectiveness of our method with respect to the applicability and a reasonable calculation time.     

Scheduling algorithms for PIPE (pipelined image-processing engine)

In this paper we present heuristic scheduling algorithms for the National Bureau of Standards/Aspen Inc. Pipelined Image-Processing Engine (PIPE). PIPE is a special-purpose machine for low-level image processing consisting of a linearly connected array of processing stages. A program is specified as a directed acyclic graph (DAG). Our first algorithm schedules planar DAGs. It works top-down through the graph and uses the greedy approach to schedule operations on a stage. It uses several heuristics to control the movement of images between stages. The worst case time for the schedule generated by the algorithm is O(N) times the optimal schedule, where N is the maximum width of the graph. We generalize this algorithm to work on nonplanar graphs, using heuristics for repositioning images on the stages of PIPE. The worst case time for the more general algorithm is also O(N) times the optimal schedule. Finally, we analyze the problem of optimizing throughput and latency for a sequence of DAGs on PIPE.     

Affine-by-Statement Scheduling of Uniform and Affine Loop Nests over Parametric Domains

This paper deals with parallel scheduling techniques for uniform and affine loop nests. We deal with affine-by-statement scheduling, a powerful extension of Lamport′s hyperplane method where each statement within the loop nest is scheduled by a different timing function. We present a new, constructive and efficient method to determine the optimal (i.e., with smallest latency) affine-by-statement scheduling. We also consider parametric loop nests, where loop limits (in addition to being affine functions of outer loops) involve program variables whose values may not be known at compile-time (but are runtime constants). We then derive parameter-independent affine-by-statement schedules, and we show that these schedules are asymptotically as efficient as parameter-dependent solutions while much more regular. This theoretical result is of importance in practice, as regularity is a key factor for loop rewriting and code generation.