Heterogeneous Earliest Finish Time Research Articles

An effective duplication-based task-scheduling algorithm for heterogeneous systems

One of the main issues in distributed computing systems is to determine an optimal task-scheduling method. In this paper, we present an effective algorithm to schedule a directed acyclic graph onto a heterogeneous distributed system, called the Earliest Starting and Finishing time Duplication based (ESFD) algorithm. Our proposed algorithm considers all parameters associated with the processors, the task, and its immediate predecessors to perform an effective task allocation. The ESFD algorithm is a three-phase algorithm with a dynamic phase to assign a priority to each task. Simulation results on both random graphs and real application graphs show that the network storage link, speedup, and efficiency generated by ESFD algorithm are better than those generated by the well-known Heterogeneous Critical Parent Trees, Heterogeneous Earliest Finish Time, and Levelized Heavily Communicating Node First algorithms.

Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters

This paper addresses the problem of minimizing the scheduling length (make-span) of a batch of jobs with different arrival times. A job is described by a direct acyclic graph (DAG) of parallel tasks. The paper proposes a dynamic scheduling method that adapts the schedule when new jobs are submitted and that may change the processors assigned to a job during its execution. The scheduling method is divided into a scheduling strategy and a scheduling algorithm. We also propose an adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan. The results of a comparison of this algorithm with another DAG scheduler using a simulation of several machine configurations and job types shows that P-HEFT gives a shorter makespan for a single DAG but scores worse for multiple DAGs. Finally, the results of the dynamic scheduling of a batch of jobs using the proposed scheduler method showed significant improvements for more heavily loaded machines when compared to the alternative resource reservation approach.

Load balanced reliable task scheduling algorithm for heterogeneous systems

In a heterogeneous system, processor and network failure are inevitable and can have adverse effect on the complex applications executing on the systems. To reduce the rate of these failures, matching and scheduling algorithms should take into account the objectives of minimizing schedule length makespan and reducing the probability of failure. Equitable distribution of workload over resources contributes in reducing the probability of failure. The Heterogeneous Earliest Finish Time HEFT algorithm has been proved as a performance effective task scheduling algorithm, addressing the objective of minimizing makespan. Reliable Dynamic Level Scheduling RDLS algorithm is a bi-objective scheduling algorithm that maximizes the reliability more effectively. Though the reliable version of HEFT algorithm RHEFT considers failure rate in scheduling decision, the improvement in reliability is less, compared to that of RDLS. To overcome this deficiency, we propose to incorporate the task--processor pair finding step of RDLS in HEFT algorithm, since it meets both the objectives of minimizing the makespan and maximizing the reliability. We define the load on a processor as the amount of time the processor is engaged in completing the scheduled subtasks. In this paper, a modification to the HEFT is proposed as a new algorithm called Improved Reliable HEFT IRHEFT for minimizing the schedule length, balancing the load and maximizing the reliability of schedule. The algorithm is compared for its performance with RDLS algorithm for randomly generated task graphs and a real application task graph.

Process Scheduling in Heterogeneous Multiprocessor Systems Using Task Duplication

Scheduling tasks in heterogeneous parallel and distributed computing environments continues to be a challenging problem. In this paper, the authors investigate the Heterogeneous Earliest Finish Time (HEFT) algorithm, along with alternative scheduling policies for task prioritising phases and the Critical Path on a Processor (CPOP) for scheduling tasks on a heterogeneous multiprocessor system. The authors show that by combining the HEFT algorithm selection policy with the task duplication strategy, it is possible to further reduce the schedule length produced by both HEFT and CPOP. The process scheduling algorithm presented in this paper compares favourably with other algorithms that use a similar strategy. The proposed algorithm has a time complexity of ?(¦V¦2(p + d)), whererepresents the number of tasks, p represents the number of processors and d the maximum in-degree of tasks.

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

A heterogeneous computing environment is a suite of heterogeneous processors interconnected by high-speed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when aiming at high performance in this kind of environment. The problem is generally addressed in terms of task scheduling, where tasks are the schedulable units of a program. The task scheduling problems have been shown to be NP-complete in general as well as several restricted cases. In this study we present a simple scheduling algorithm based on list scheduling, namely, low complexity Performance Effective Task Scheduling (PETS) algorithm for heterogeneous computing systems with complexity O (e) (p+ log v), which provides effective results for applications represented by DAGs. The analysis and experiments based on both randomly generated graphs and graphs of some real applications show that the PETS algorithm substantially outperforms the existing scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT), Critical-Path-On a Processor (CPOP) and Levelized Min Time (LMT), in terms of schedule length ratio, speedup, efficiency, running time and frequency of best results.

Iterative list scheduling for heterogeneous computing

Optimal scheduling of parallel applications on distributed computing systems represented by directed acyclic graph (DAG) is NP-complete in the general case. List scheduling is a very popular heuristic method for DAG-based scheduling. However, it is more suited to homogenous distributed computing systems. This paper presents an iterative list scheduling algorithm to deal with scheduling on heterogeneous computing systems. The main idea in this iterative scheduling algorithm is to improve the quality of the schedule in an iterative manner using results from previous iterations. The algorithm first uses the heterogeneous earliest-finish-time (HEFT) algorithm to find an initial schedule and iteratively improves it. Hence the algorithm can potentially produce shorter schedule length. The simulation results show that in the majority of the cases, there is significant improvement to the initial schedule. The algorithm is also found to perform best when the tasks to processors ratio is large.

An Algorithm for Task Scheduling in Heterogeneous Computing Environment

Heterogeneous computing systems are of considerable interest because a single parallel architecture may not be adequate for exploiting all the parallelism available in the program. In some cases, heterogeneous systems perform better than a single large machine and that too at a lower cost. In heterogeneous systems, task scheduling is done with a view to minimize the overall completion time of a parallel application by properly allocating the tasks to the processors. In this paper, a task-scheduling algorithm called Alternate Heterogeneous Earliest Finish Time (AHEFT) is proposed and its performance is analyzed with respect to Heterogeneous Earliest Finish Time (HEFT) algorithm. A simulation environment has been developed in C++ and both the algorithms were implemented. Different weighted Directed Acyclic Graphs (DAG's) were used to compare the two algorithms. A network of fully connected heterogeneous processors was assumed for task scheduling. The performance indices used for comparison of algorithms are Schedule Length, Speedup Factor and Efficiency. The results substantiate the fact that for higher values of CCR (Communication to Computation Ratio), AHEFT algorithm performs better than HEFT. Moreover, as the number of tasks in the DAG increase, the performance improvement of AHEFT algorithms is more as compared to HEFT.

Performance-effective and low-complexity task scheduling for heterogeneous computing

Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.