Task scheduling in distributed real-time systems

An approach to the flow shop scheduling of the computation process in distributed realtime systems is considered. This approach is based on the concept of a solvable class of systems for which simple optimal scheduling algorithms exist.     

Message scheduling for array re-decomposition on distributed memory systems

For many parallel applications on distributed memory systems, array re-decomposition is usually required to enhance data locality and reduce the communication overheads. How to effectively schedule messages to improve the performance of array re-decomposition has received much attention in recent years. This paper is devoted to develop efficient scheduling algorithms using the compiling information provided by array distribution patterns, array alignment patterns and the periodic property of array accesses. Our algorithms not only avoid inter-processor contention, but also reduces real communication cost and communication generation time. The experimental results show that the performance of array redecomposition can be significantly improved using our algorithms     

An investigation in parallel execution of answer set programs on distributed memory platforms: Task sharing and dynamic scheduling

Answer Set Programming (ASP) is a novel logic programming paradigm, that has already had a profound impact in several application domains, especially in the areas of knowledge representation and reasoning.In spite of the development of excellent inference engines for ASP, efficiency and scalability remain challenging aspects that prevent the use of ASP in various real-world domains. Parallelism has been identified as a natural avenue to address these problems.This paper describes the design of a complete ASP parallel engine, derived from the basic design of the Smodels architecture. The paper places emphasis on addressing the problem of the irregular structure of the search trees generated by typical ASP computations (in a Smodels-like computation), which requires the use of dynamic load balancing mechanisms. The paper provides a systematic investigation of alternative strategies for dynamic scheduling and task sharing. These are the two components that more directly affect the efficiency of a parallel engine.     

High-throughput query scheduling with spatial clustering based on distributed exponential moving average

In distributed scientific query processing systems, leveraging distributed cached data is becoming more important. In such systems, a front-end query scheduler distributes queries among many application servers rather than processing queries in a few high-performance workstations. Although many query scheduling policies exist such as round-robin and load-monitoring, they are not sophisticated enough to exploit cached results as well as balance the workload. Efforts were made to improve the query processing performance using statistical methods such as exponential moving average. However, existing methods have limitations for certain query patterns: queries with hotspots, or dynamic query distributions. In this paper, we propose novel query scheduling policies that take into account both the contents of distributed caching infrastructure and the load balance among the servers. Our experiments show that the proposed query scheduling policies outperform existing policies by producing better query plans in terms of load balance and cache-hit ratio.     

设备网格中的任务调度算法

基于设备网格环境中仪器设备的利用率和提交任务的QoS需求来考虑,结合任务调度算法Min-min,提出了一种设备网格中的Qos-Balance任务调度算法.该算法既保证了负载均衡性和又可满足提交任务的QoS需求.实验结果表明,该算法是一种可行的设备网格任务调度算法.最后介绍了算法实验的结果分析.     

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.     

Domain-dependent distributed models for railway scheduling

Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NP-complete, so closure and heuristic search are usually used. However, many problems are inherently distributed and the problem complexity can be reduced by dividing the problem into a set of subproblems. Nevertheless, general distributed techniques are not always appropriate to distribute real-life problems. In this work, we model the railway scheduling problem by means of domain-dependent distributed constraint models, and we show that these models maintained better behaviors than general distributed models based on graph partitioning. The evaluation is focused on the railway scheduling problem, where domain-dependent models carry out a problem distribution by means of trains and contiguous sets of stations.     

Task scheduling for parallel sparse Cholesky factorization

This paper presents a solution to the problem of partitioning the work for sparse matrix factorization to individual processors on a multiprocessor system. The proposed task assignment strategy is based on the structure of the elimination tree associated with the given sparse matrix. The goal of the task scheduling strategy is to achieve load balancing and a high degree of concurrency among the processors while reduçing the amount of processor-to-processor data comnication, even for arbitrarily unbalanced elimination trees. This is important because popular fill-reducing ordering methods, such as the minimum degree algorithm, often produce unbalanced elimination trees. Results from the Intel iPSC/2 are presented for various finite-element problems using both nested dissection and minimum degree orderings.Research supported by the Applied Mathematical Sciences Research Program, Office of Energy Research, U.S. Department of Energy under contract DE-AC05-84OR21400 with Martin Marietta Energy Systems Inc.     

Compiling for distributed memory architectures

The lack of high-level languages and good compilers for parallel machines hinders their widespread acceptance and use. Programmers must address issues such as process decomposition, synchronization, and load balancing. We have developed a parallelizing compiler that, given a sequential program and a memory layout of its data, performs process decomposition while balancing parallelism against locality of reference. A process decomposition is obtained by specializing the program for each processor to the data that resides on that processor. If this analysis fails, the compiler falls back to a simple but inefficient scheme called run-time resolution. Each process's role in the computation is determined by examining the data required for execution at run-time. Thus, our approach to process decomposition is data-driven rather than program-driven. We discuss several message optimizations that address the issues of overhead and synchronization in message transmission. Accumulation reorganizes the computation of a commutative and associative operator to reduce message traffic. Pipelining sends a value as close to its computation as possible to increase parallelism. Vectorization of messages combines messages with the same source and the same destination to reduce overhead. Our results from experiments in parallelizing SIMPLE, a large hydrodynamics benchmark, for the Intel iPSC/2, show a speedup within 60% to 70% of handwritten code     

Stochastic clustering for organizing distributed information sources

The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism.     

Task scheduling and motion planning for an industrial manipulator

In many robotic industrial applications, a manipulator should move among obstacles and reach a set of task-points in order to perform a pre-defined task. It is quite important as well as very complicated to determine the time-optimum sequence of the task-points visited by the end-effector's tip only once assuring that the manipulator's motion through the successive task-points is collision-free.     

面向分布式实时系统的安全驱动调度算法研究

针对现有实时调度算法无法适应动态安全需求的问题,构建了一种安全驱动调度模型,该模型从系统安全级别、系统安全服务和任务安全策略三个方面描述了实时系统的动态安全需求,并设计了一种基于安全驱动的实时任务调度器框架。以该模型和框架为基础,提出了一种安全驱动调度算法(Security Driven Scheduling Algorithm,SDSA)。从全局角度对新到达任务进行可调度性检查,并将可调度任务分配到合适的处理机上运行。按照系统安全级别来动态调整已分配到各处理机上实时任务的安全策略,使其达到安全性和可调度性的最优平衡。采用优先级抢占式策略对各实时任务进行调度。仿真结果表明,SDSA算法与其他同类算法相比,在系统动态安全需求的适应性、关键任务的可调度性以及安全防危能力等方面具有较好的表现。     

Multiple query scheduling for distributed semantic caches

In distributed query processing systems, load balancing plays an important role in maximizing system throughput. When queries can leverage cached intermediate results, improving the cache hit ratio becomes as important as load balancing in query scheduling, especially when dealing with computationally expensive queries. The scheduling policies must be designed to take into consideration the dynamic contents of the distributed caching infrastructure. In this paper, we propose and discuss several distributed query scheduling policies that directly consider the available cache contents by employing distributed multidimensional indexing structures and an exponential moving average approach to predicting cache contents. These approaches are shown to produce better query plans and faster query response times than traditional scheduling policies that do not predict dynamic contents in distributed caches. We experimentally demonstrate the utility of the scheduling policies using MQO, which is a distributed, Grid-enabled, multiple query processing middleware system we developed to optimize query processing for data analysis and visualization applications.     

Efficient techniques for clustering and scheduling onto embedded multiprocessors

Multiprocessor mapping and scheduling algorithms have been extensively studied over the past few decades and have been tackled from different perspectives. In the late 1980's, the two-step decomposition of scheduling nto clustering and cluster-scheduling - was introduced. Ever since, several clustering and merging algorithms have been proposed and individually reported to be efficient. However, it is not clear how effective they are and how well they compare against single-step scheduling algorithms or other multistep algorithms. In this paper, we explore the effectiveness of the two-phase decomposition of scheduling and describe efficient and novel techniques that aggressively streamline interprocessor communications and can be tuned to exploit the significantly longer compilation time that is available to embedded system designers. We evaluate a number of leading clustering and merging algorithms using a set of benchmarks with diverse structures. We present an experimental setup for comparing the single-step against the two-step scheduling approach. We determine the importance of different steps in scheduling and the effect of different steps on overall schedule performance and show that the decomposition of the scheduling process indeed improves the overall performance. We also show that the quality of the solutions depends on the quality of the clusters generated in the clustering step. Based on the results, we also discuss why the parallel time metric in the clustering step may not provide an accurate measure for the final performance of cluster-scheduling.     

Task scheduling in multiprocessing systems

The complex problem of assigning tasks to processing elements in order to optimize a performance measure has resulted in numerous heuristics aimed at approximating an optimal solution. This article addresses the task scheduling problem in many of its variations and surveys the major solutions. The scheduling techniques we discuss might be used by a compiler writer to optimize the code that comes out of a parallelizing compiler. The compiler would produce grains of sequential code, and the optimizer would schedule these grains such that the program runs in the shortest time     

基于任务复制的调度算法

任务调度是并行分布式计算系统中最具挑战性的NP完全问题之一.基于任务复制的调度是一种有效的调度方法.在通信开销较小的情况下,现已有许多算法能产生最优调度.但其最优条件要么比较苛刻,要么比较复杂.因此,针对这些算法存在的问题,提出一个新的基于任务复制的聚集调度(TDCS)算法,不仅其最优条件简单、宽松,而且该算法具有更小的时间复杂度O(dvlogd),其中,V和d分别表示任务集中任务的个数和最大入度.     

Fast primal-dual distributed algorithms for scheduling and matching problems

In this paper we give efficient distributed algorithms computing approximate solutions to general scheduling and matching problems. All approximation guarantees are within a constant factor of the optimum. By “efficient”, we mean that the number of communication rounds is poly-logarithmic in the size of the input. In the scheduling problem, we have a bipartite graph with computing agents on one side and resources on the other. Agents that share a resource can communicate in one time step. Each agent has a list of jobs, each with its own length and profit, to be executed on a neighbouring resource within a given time-window. Each job is also associated with a rational number in the range between zero and one (width), specifying the amount of resource required by the job. Resources can execute non preemptively multiple jobs whose total width at any given time is at most one. The goal is to maximize the profit of the jobs that are scheduled. We then adapt our algorithm for scheduling, to solve the weighted b-matching problem, which is the generalization of the weighted matching problem where for each vertex v, at most b(v) edges incident to v, can be included in the matching. For this problem we obtain a randomized distributed algorithm with approximation guarantee of \frac16+e{\frac{1}{6+\epsilon}}, for any ${\epsilon >0 }${\epsilon >0 }. For weighted matching, we devise a deterministic distributed algorithm with the same approximation ratio. To our knowledge, we give the first distributed algorithm for the aforementioned scheduling problem as well as the first deterministic distributed algorithm for weighted matching with poly-logaritmic running time. A very interesting feature of our algorithms is that they are all derived in a systematic manner from primal-dual algorithms.     

A distributed agent system for port planning and scheduling

This paper describes a distributed agent system for dynamic port planning and scheduling. The proposed system comprises four agents, namely a port planning manager (PPM), a berth control agent (BCA), a shuttle allocation agent (SAA) and a yard storage agent (YSA). These agents communicate and cooperate with one another to work out the schedule for berth allocation and requirements for shuttles. Basically, the PPM maintains all the necessary information and provides a negotiation and communication locale for the BCA, SAA and YSA, which represent the berths, the shuttles and the container yard, respectively, to resolve their conflicts under its supervision. With the help of a GA-enhanced dynamic scheduler, the BCA attempts to optimise the berth allocation schedule using the data such as ship arrival information retrieved from the PPM. As soon as the SAA receives the schedule from the BCA, it proceeds to check for the availability of shuttles and work out the requirement for shuttles. On the other hand, the YSA generates the yard storage allocation schedule and the truck/train schedule for the transportation of containers.A case study is conducted to illustrate the capability of the distributed agent system. It has been shown that the proposed system is able to successfully generate the schedules for both the BCA and the SAA.     

Modeling and heuristics for scheduling of distributed job shops

This paper deals with the problem of distributed job shop scheduling in which the classical single-facility job shop is extended to the multi-facility one. The mathematical formulation of the problem is comprehensively discussed. Two different mixed integer linear programming models in form of sequence and position based variables are proposed. Using commercial software of CPLEX, the small sized problems are optimally solved. To solve large sized problems, besides adapting three well-known heuristics, three greedy heuristics are developed. The basic idea behind the developed heuristics is to iteratively insert operations (one at each iteration) into a sequence to build up a complete permutation of operations. The permutation scheme, although having several advantages, suffers from redundancy which is having many different permutations representing the same schedule. The issue is analyzed to recognize the redundant permutation. That improves efficiency of heuristics. Comprehensive experiments are conducted to evaluate the performance of the two models and the six heuristics. The results show sequence based model and greedy heuristics equipped with redundancy exclusion are effective for the problem.