网格任务调度方法研究

网格计算中的关键问题之一是计算任务在各个资源之间的调度。提出了基于量子遗传算法（QGA）的网格任务调度算法,以减少调度时间为主要目标,增加资源利用率为次要目标。该算法采用量子比特间接编码的方式,通过有向无环图（DAG）来描述子任务间的依赖关系,根据深度值来给子任务的执行顺序进行排序。仿真结果显示,无论是任务完成时间还是资源利用率,此方法都明显优于基于遗传算法（GA）的网格调度算法。     

Deadline division-based heuristic for cost optimization in workflow scheduling

Cost optimization for workflow applications described by Directed Acyclic Graph (DAG) with deadline constraints is a fundamental and intractable problem on Grids. In this paper, an effective and efficient heuristic called DET (Deadline Early Tree) is proposed. An early feasible schedule for a workflow application is defined as an Early Tree. According to the Early Tree, all tasks are grouped and the Critical Path is given. For critical activities, the optimal cost solution under the deadline constraint can be obtained by a dynamic programming strategy, and the whole deadline is segmented into time windows according to the slack time float. For non-critical activities, an iterative procedure is proposed to maximize time windows while maintaining the precedence constraints among activities. In terms of the time window allocations, a local optimization method is developed to minimize execution costs. The two local cost optimization methods can lead to a global near-optimal solution. Experimental results show that DET outperforms two other recent leveling algorithms. Moreover, the deadline division strategy adopted by DET can be applied to all feasible deadlines.     

Dynamic Load Balancing and Pricing in Grid Computing with Communication Delay

Due to the emergence of Grid computing over the Internet, there is presently a need for dynamic load balancing algorithms which take into account the characteristics of Grid computing environments. In this paper, we consider a Grid architecture where computers belong to dispersed administrative domains or groups which are connected with heterogeneous communication bandwidths. We address the problem of determining which group an arriving job should be allocated to and how its load can be distributed among computers in the group to optimize the performance. We propose algorithms which guarantee finding a load distribution over computers in a group that leads to the minimum response time or computational cost. We then study the effect of pricing on load distribution by considering a simple pricing function. We develop three fully distributed algorithms to decide which group the load should be allocated to, taking into account the communication cost among groups. These algorithms use different information exchange methods and a resource estimation technique to improve the accuracy of load balancing. We conducted extensive simulations to evaluate the performance of the proposed algorithms and strategies.     

A system-centric scheduling policy for optimizing objectives of application and resource in grid computing

In grid computing, grid users who submit applications and resources providers who provide resources have different motivations when they join the grid. Application-centric scheduling aims to optimize the performance of individual application. Resource-centric scheduling aims to optimize the resource utilization of resources provider. Due to autonomy both in grid users and resource providers, the objectives of application-centric and resource-centric scheduling often conflict. The paper proposes a system-centric scheduling that provides a solution of joint optimization of the objectives for both the grid resource and grid application. Utility functions are used to express the objectives of grid resource and application. The system-centric scheduling policy can be formulated as joint optimization of utilities of grid applications and grid resources, which combine both application centric and resource-centric scheduling benefits. Simulations are conducted to study the performance of the system-centric scheduling algorithm. The experiment results show that the system-centric scheduling algorithm yields significantly better performance than application-centric scheduling algorithm and resource-centric scheduling algorithm.     

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.     

Communication cost effective scheduling policies of nonclairvoyant jobs with load balancing in a grid

Effective load distribution is of great importance at grids, which are complex heterogeneous distributed systems. In this paper we study site allocation scheduling of nonclairvoyant jobs in a 2-level heterogeneous grid architecture. Three scheduling policies at grid level which utilize site load information are examined. The aim is the reduction of site load information traffic, while at the same time mean response time of jobs and fairness in utilization between the heterogeneous sites are of great interest. A simulation model is used to evaluate performance under various conditions. Simulation results show that considerable decrement in site load information traffic and utilization fairness can be achieved at the expense of a slight increase in response time.     

HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta-heuristic algorithms

Cloud computing is becoming a profitable technology because of it offers cost-effective IT solutions globally. A well-designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter-dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem-based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta-heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out-perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

A near-optimal database allocation for reducing the average waiting time in the grid computing environment

In a grid computing environment, a great many users may access the same database simultaneously. To reduce the average waiting time for all users, a grid designer usually replicates the frequently accessed database among nodes based on the load balance heuristic. On the other hand, users may raise identical queries regarding an issue of interest, e.g., stock information, on a database and each of the queries will be directed to any node having a replica of that database. That is, the same answer will be determined by multiple nodes. Consequently, there exist two shortcomings of poor data sharing and duplicate calculations if the database is not replicated and allocated adequately. In this paper, we aim to minimize average waiting time and try to overcome the two shortcomings by performing database allocation over multiple nodes without any replication. The main idea behind the proposed method is to map the original problem to the Euclidean space Rⁿ and to solve the mapped problem in Rⁿ by a gradient-based optimization technique. The theoretical analyses ensure that the proposed method can converge linearly and achieve near-optimal results.     

A design fix to supervisory control for fault-tolerant scheduling of real-time multiprocessor systems with aperiodic tasks

In the article ‘Supervisory control for fault-tolerant scheduling of real-time multiprocessor systems with aperiodic tasks’, Park and Cho presented a systematic way of computing a largest fault-tolerant and schedulable language that provides information on whether the scheduler (i.e., supervisor) should accept or reject a newly arrived aperiodic task. The computation of such a language is mainly dependent on the task execution model presented in their paper. However, the task execution model is unable to capture the situation when the fault of a processor occurs even before the task has arrived. Consequently, a task execution model that does not capture this fact may possibly be assigned for execution on a faulty processor. This problem has been illustrated with an appropriate example. Then, the task execution model of Park and Cho has been modified to strengthen the requirement that none of the tasks are assigned for execution on a faulty processor.     

Some problems in the stability of networked-control systems with periodic scheduling

This article addresses three stability problems related to networked-control systems (NCSs) with periodic scheduling, where control systems may have multiple samplings in a hyperperiod (a hyperperiod is a periodically repeated scheduling sequence for all tasks in an NCS). As expected, the analysis of a system with multiple samplings is much richer than the case with single sampling. For example, a system with two samplings may be stable (unstable) even if it is unstable (stable) when sampled by either sampling. In this context, it is important to understand how network-induced delays and multiple samplings affect the system's stability. In this article, three particular stability problems involving constant and/or time-varying parameters are investigated, and the corresponding stability regions are derived. Numerical examples and various discussions complete the presentation.     

A system for the design of packet-switched communication networks with economic tradeoffs

This paper studies the problem of designing a packet-switched communication network with tradeoffs between link costs and response time to users. It consists of assigning capacities to links in the network and determining the routes used by messages for all communicating node pairs in order to minimize total link fixed and variable costs. A tradeoff between link costs and response time to users is achieved by including a constraint that sets an upper limit on the average link queueing delay in the network. The topology of the network and the end-to-end traffic requirements are given. The problem is formulated as a nonlinear integer programming model. Unlike most of previous models, where the best route for a communicating node pair is restricted to a set of prespecified candidate routes, our model considers all possible routes for every communicating node pair. An efficient heuristic based on a Lagrangean relaxation of the problem is developed to generate feasible solutions. The results of extensive computational experiments across a variety of previously used networks are reported. These results indicate that the solution procedure is effective for a wide range of traffic loads and cost structures.     

On a geometric characterisation of zeros for non-square linear systems with time-delay in state

The concept of invariant zeros in a linear time-invariant system with point delay in state vector is discussed in the state space framework. These zeros are treated as the triples: complex number, non-zero state-zero direction and input-zero direction. Such treatment is strictly related to the output-zeroing problem and in that spirit the zeros can be easily interpreted. As is shown, for systems with matrix CB of full row-rank, general formulas for output-zeroing inputs can be obtained as well as a characterisation of invariant zeros as the roots of a certain quasi-polynomial can be given. The question of degeneracy/non-degeneracy of the system is also addressed. Moreover, it is shown that diagonal decoupling can be achieved by constant state feedbacks and a pre-compensator. The transfer matrix of the decoupled system is square and does not contain delay. The mathematical tools used in the analysis are the Moore–Penrose pseudo-inverse and singular value decomposition of a matrix.