An incremental ant colony optimization based approach to task assignment to processors for multiprocessor scheduling

Boveiri, Hamid Reza

doi:10.1631/fitee.1500394

Cited by 16 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because there is not any metric to specify the properness of assigning each ready task to a district cluster, we are actually facing an unknown problem. Inspired by References Boveiri (2014, 2015a), Boveiri and Khayami (2017), and Boveiri (2017), it was proved that the CLA is one of the best for task graph assigning in multiprocessor environments. Accordingly, because the nature of underlying infrastructure and connectivity of computational units (processors vs. clusters) is very similar, we can justify the utilization of CLA for task assigning in the cluster computing environments.…”

Section: The Proposed Approachmentioning

confidence: 98%

“…Already, we have explicitly distinguished these two disparate-in-nature subproblems and introduced different intelligent methods, for example, ant colony optimization (ACO)-based methods (Boveiri, 2010(Boveiri, , 2016, cellular learning automata (CLA)-based approaches (Boveiri, 2014(Boveiri, , 2015a, and cuckoo optimization algorithm (COA)-based ones (Boveiri, 2017(Boveiri, , 2019Boveiri & Elhoseny, 2018) to tackle the sequence and assigning subproblems for fully connected homogeneous multiprocessor systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications

2020

Self Cite

View full text Add to dashboard Cite

Nowadays, increase in time complexity of applications and decrease in hardware costs are two major contributing drivers for the utilization of high-performance architectures such as cluster computing systems. Actually, cluster computing environments, in the contemporary sophisticated data centres, provide the main infrastructure to process various data, where the biomedical one is not an exception. Indeed, optimized task scheduling is key to achieve high performance in such computing environments. The most distractive assumption about the problem of task scheduling, made by the state-of-the-art approaches, is to assume the problem as a whole and try to enhance the overall performance, while the problem is actually consisted of two disparate-in-nature subproblems, that is, sequencing subproblem and assigning one, each of which needs some special considerations. In this paper, an efficient hybrid approach named ACO-CLA is proposed to solve task scheduling problem in the mesh-topology cluster computing environments. In the proposed approach, an enhanced ant colony optimization (ACO) is developed to solve the sequence subproblem, whereas a cellular learning automata (CLA) machine tackles the assigning subproblem. The utilization of background knowledge about the problem (i.e., tasks' priorities) has made the proposed approach very robust and efficient. A randomly generated data set consisting of 125 different random task graphs with various shape parameters, like the ones frequently encountered in the biomedicine, has been utilized for the evaluation of the proposed approach. The conducted comparison study clearly shows the efficiency and superiority of the proposed approach versus traditional counterparts in terms of the performance. From our first metric, that is, the NSL (normalized schedule length) point of view, the proposed ACO-CLA is 2.48% and 5.55% better than the ETF (earliest time first), which is the second-best approach, and the average performance of all other competing methods. On the other hand, from our second metric, that is, the speedup perspective, the proposed ACO-CLA is 2.66% and 5.15% better than the ETF (the second-best approach) and the average performance of all the other competitors.

show abstract

Section: The Proposed Approachmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Liu and Kroll [13] aimed to solve the multirobot task allocation problems, and they developed a subpopulation-based genetic algorithm by using inversion mutation and selection. Other centralized algorithms like ant colony optimization [14], genetic algorithm [15], and wolf pack algorithm [16] also show effectiveness in solving the task allocation problem for multirobot systems. Though the centralized algorithms show advantages in optimization, the huge real-time communication pressure of the center and complexity of computation limit its performance.…”

Section: Related Workmentioning

confidence: 99%

“…For each UAV in the candidate set, Algorithm 1 would calculate the task's earliest finish time in an overlapping mode on the premise that task t i P can overlap UAV u j 's last task. The overlapping qualification is decided by the fact that the environment-threatened value e t i is larger than the threshold, and u j 's last task is a backup copy with the condition that backup copy was not overlapped (see lines [8][9][10][11][12][13][14][15]. In order to obtain a load balance for UAVs, the earliest finish time in a nonoverlapping mode would be calculated for comparison, and the algorithm finally acquires an earlier finish time (see lines [16][17][18][19][20][21].…”

Section: Assigning Algorithm For Primary Copiesmentioning

confidence: 99%

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Chen

Bao

Men

et al. 2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs’ vulnerability, so damage-tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage-tolerant framework for assigning real-time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup-Primary overlapping and Backup-Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage-tolerant and overlapping mechanism, for the first time, we propose a new damage-tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks’ success without affecting the effectiveness of swarms in confrontational environments.

show abstract

“…Therefore, the evolutionary algorithms are the most common methods to solve this kind of problem. The classical evolutionary algorithms include the simulated annealing algorithm (SAA) [12], the tabu search algorithm (TS) [13], the genetic algorithm (GA) [14], the ant colony optimization algorithm (ACO) [15], and particle swarm optimization algorithm (PSO) [16].…”

Section: Introductionmentioning

confidence: 99%

Task Assignment of UAV Swarm Based on Wolf Pack Algorithm

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

To perform air missions with an unmanned aerial vehicle (UAV) swarm is a significant trend in warfare. The task assignment among the UAV swarm is one of the key issues in such missions. This paper proposes PSO-GA-DWPA (discrete wolf pack algorithm with the principles of particle swarm optimization and genetic algorithm) to solve the task assignment of a UAV swarm with fast convergence speed. The PSO-GA-DWPA is confirmed with three different ground-attack scenarios by experiments. The comparative results show that the improved algorithm not only converges faster than the original WPA and PSO, but it also exhibits excellent search quality in high-dimensional space.

show abstract

An incremental ant colony optimization based approach to task assignment to processors for multiprocessor scheduling

Cited by 16 publications

References 11 publications

An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications

An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Task Assignment of UAV Swarm Based on Wolf Pack Algorithm

Contact Info

Product

Resources

About