scholarly journals An ILP Formulation for the Task Graph Scheduling Problem Tailored to Bi-Dimensional Reconfigurable Architectures

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
F. Redaelli ◽  
M. D. Santambrogio ◽  
S. Ogrenci Memik
Keyword(s):  
Task Scheduling ◽  
Optimal Schedule ◽  
Reconfigurable Architecture ◽  
Reconfigurable Architectures ◽  
Task Graph ◽  
Scheduling Problem ◽  
Physical Constraints ◽  
Schedule Length ◽  
Extended Analysis ◽  
Task Graph Scheduling

This work proposes an exact ILP formulation for the task scheduling problem on a 2D dynamically and partially reconfigurable architecture. Our approach takes physical constraints of the target device that is relevant for reconfiguration into account. Specifically, we consider the limited number of reconfigurators, which are used to reconfigure the device. This work also proposes a reconfiguration-aware heuristic scheduler, which exploitsconfiguration prefetching, module reuse, andantifragmentationtechniques. We experimented with a system employing two reconfigurators. This work also extends the ILP formulation for a HW/SW Codesign scenario. A heuristic scheduler for this extension has been developed too. These systems can be easily implemented using standard FPGAs. Our approach is able to improve the schedule quality by 8.76% on average (22.22% in the best case). Furthermore, our heuristic scheduler obtains the optimal schedule length in 60% of the considered cases. Our extended analysis demonstrated that HW/SW codesign can indeed lead to significantly better results. Our experiments show that by using our proposed HW/SW codesign method, the schedule length of applications can be reduced by a factor of 2 in the best case.

THE TIME COMPLEXITY OF SCHEDULING INTERVAL ORDERS WITH COMMUNICATION IS POLYNOMIAL

1993 ◽  
Vol 03 (01) ◽  
pp. 53-58 ◽  
Author(s):  
HESHAM H. ALI ◽  
HESHAM EL-REWINI
Keyword(s):  
Execution Time ◽  
Time Complexity ◽  
Linear Time ◽  
Communication Cost ◽  
Interval Order ◽  
Task Graph ◽  
Scheduling Problem ◽  
Interval Orders ◽  
Schedule Length ◽  
Np Complete

Papadimitrjou and Yannakakis showed that unit execution time tasks in interval orders can be scheduled in linear time on N processors when communication cost is ignored. The objective function was to minimize the schedule length. They have also shown that the generalization of this problem to arbitrary execution times is NP- complete . In this paper, we study the problem of scheduling task graphs with communication on N processors when the task graph is an interval order. We prove that this scheduling problem can be solved in polynomial time when the execution cost of the system tasks is identical and equal to the communication cost between any pair of processors. We introduce an algorithm of O(Ne) to minimize the schedule length, where e is the number of arcs in the interval order.

scholarly journals Optimal Cooking Procedure Presentation System for Multiple Recipes and Investigating Its Effect

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 67
Author(s):  
Jin Nakabe ◽  
Teruhiro Mizumoto ◽  
Hirohiko Suwa ◽  
Keiichi Yasumoto
Keyword(s):  
Task Scheduling ◽  
Execution Time ◽  
Cooking Time ◽  
Sequential Optimization ◽  
Task Graph ◽  
Scheduling Problem ◽  
Cooking Skill ◽  
The Difference ◽  
Increasing Demand ◽  
Reduce Execution Time

As the number of users who cook their own food increases, there is increasing demand for an optimal cooking procedure for multiple dishes, but the optimal cooking procedure varies from user to user due to the difference of each user’s cooking skill and environment. In this paper, we propose a system of presenting optimal cooking procedures that enables parallel cooking of multiple recipes. We formulate the problem of deciding optimal cooking procedures as a task scheduling problem by creating a task graph for each recipe. To reduce execution time, we propose two extensions to the preprocessing and bounding operation of PDF/IHS, a sequential optimization algorithm for the task scheduling problem, each taking into account the cooking characteristics. We confirmed that the proposed algorithm can reduce execution time by up to 44% compared to the base PDF/IHS, and increase execution time by about 900 times even when the number of required searches increases by 10,000 times. In addition, through the experiment with three recipes for 10 participants each, it was confirmed that by following the optimal cooking procedure for a certain menu, the actual cooking time was reduced by up to 13 min (14.8% of the time when users cooked freely) compared to the time when users cooked freely.

scholarly journals Learning Based Genetic Algorithm for Task Graph Scheduling

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Habib Izadkhah
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Algorithm ◽  
Solution Space ◽  
Optimization Process ◽  
Learning Criterion ◽  
Task Graph ◽  
Scheduling Problem ◽  
Static Scheduling ◽  
Steepest Ascent ◽  
Task Graph Scheduling

Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority of the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering how to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to address static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria named Steepest Ascent Learning Criterion and Next Ascent Learning Criterion where we use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling improves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion during the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches.

scholarly journals AN EFFICIENT TASK SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS BY IMPROVED GENETIC ALGORITHM

2013 ◽  
pp. 52-58
Author(s):  
K. SUNITHA ◽  
MRS. P V SUDHA
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Heterogeneous Systems ◽  
Simultaneous Optimization ◽  
Multiprocessor System ◽  
Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Distributed Computing Systems

Task Scheduling problem for heterogeneous systems is concerned with arranging the various tasks to be executed on various processors of a system so that computing resources are utilized most effectively. Parallel processing refers to the concept of speeding-up the execution of a task by dividing the task into multiple fragments that can execute simultaneously, each on its own processor i.e. it is the simultaneous processing of the task on two or more processors in order to obtain faster results. It can be effectively used for tasks that involve a large number of calculations, have time constraints and can be divided into a number of smaller tasks. The scheduling problem deals with the optimal assignment of a set of tasks onto parallel multiprocessor system and orders their execution so that the total completion time is minimized. An Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. This precedence relationship among tasks can be represented as Directed Acyclic Graph (DAG). In this paper, a scheduling algorithm has been proposed to schedule DAG tasks on Heterogeneous processor which uses Genetic algorithm to get optimal schedule. The scheduling problem is also considered. This study includes a search for an optimal mapping of the task and their sequence of execution and also search for an optimal configuration of the parallel system. An approach for the simultaneous optimization of all these three components of scheduling method using genetic algorithm is presented and its performance is evaluated in comparison with the Min-Min and Max-Min scheduling methods.

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

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