A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems

Akbari, Mohammad; Rashidi, Hassan

doi:10.1016/j.eswa.2016.05.014

Cited by 67 publications

(28 citation statements)

References 40 publications

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“…To ensure effective scheduling on heterogeneous virtual machines and reduce task execution time, Akbari and Rashidi [42] proposed a Multi-Objective Scheduling Cuckoo Optimization Algorithm (MOSCOA). In their proposed approach, each cuckoo is used as a scheduling solution.…”

Section: Related Workmentioning

confidence: 99%

Cloud customers service selection scheme based on improved conventional cat swarm optimization

Gabi

Ismail

Zainal

et al. 2020

Neural Comput & Applic

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With growing demand on resources situated at the cloud datacenters, the need for customers' resource selection techniques becomes paramount in dealing with the concerns of resource inefficiency. Techniques such as metaheuristics are promising than the heuristics, most especially when handling large scheduling request. However, addressing certain limitations attributed to the metaheuristic such as slow convergence speed and imbalance between its local and global search could enable it become even more promising for customers service selection. In this work, we propose a cloud customers service selection scheme called Dynamic Multi-Objective Orthogonal Taguchi-Cat (DMOOTC). In the proposed scheme, avoidance of local entrapment is achieved by not only increasing its convergence speed, but balancing between its local and global search through the incorporation of Taguchi orthogonal approach. To enable the scheme to meet customers' expectations, Pareto dominant strategy is incorporated providing better options for customers in selecting their service preferences. The implementation of our proposed scheme with that of the benchmarked schemes is carried out on CloudSim simulator tool. With two scheduling scenarios under consideration, simulation results show for the first scenario, our proposed DMOOTC scheme provides better service choices with minimum total execution time and cost (with up to 42.87%, 35.47%, 25.49% and 38.62%, 35.32%, 25.56% reduction) and achieves 21.64%, 18.97% and 13.19% improvement for the second scenario in terms of execution time compared to that of the benchmarked schemes. Similarly, statistical results based on 95% confidence interval for the whole scheduling scheme also show that our proposed scheme can be much more reliable than the benchmarked scheme. This is an indication that the proposed DMOOTC can meet customers' expectations while providing guaranteed performance of the whole cloud computing environment.

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Section: Related Workmentioning

confidence: 99%

Cloud customers service selection scheme based on improved conventional cat swarm optimization

Gabi

Ismail

Zainal

et al. 2020

Neural Comput & Applic

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show abstract

“…Their proposed algorithm presents a new heuristic to generate an initial population and also proposed new operators aiming to guarantee the diversity and convergence. Akbari and Rashidi [36] employed cuckoo optimization algorithm for static task graph scheduling in heterogeneous computing systems. Because this algorithm works on continuous space, hence, they proposed a discretization of this algorithm to solve the task graph scheduling problem.…”

Section: Background and Previous Workmentioning

confidence: 99%

Learning Based Genetic Algorithm for Task Graph Scheduling

Izadkhah

2019

Applied Computational Intelligence and Soft Computing

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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.

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“…(Oh et al 2016). Lei Xu et al Have also used annealing algorithm with genetic algorithm to allocate resources for a multi-user system (Lei et al 2016), and Akbari and Rashidi use a multi-objective scheduling algorithm based on the cuckoo algorithm in Task allocation has been used in heterogeneous systems (Akbari et al 2016). In 2015, by Lin and Chiu used a hybrid particle swarm optimization algorithm and local search for resource allocation (Lin et al 2015), as well as Nadia Nedjah et al, distributed PSO-based algorithm for dynamic task allocation of robots have introduced (Nedjah et al 2015); Wei Hong et al Have also used PSO to assign two-level tasks (Wei et al 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

Bahrami

Argany

Samani

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.

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A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems

Cited by 67 publications

References 40 publications

Cloud customers service selection scheme based on improved conventional cat swarm optimization

Cloud customers service selection scheme based on improved conventional cat swarm optimization

Learning Based Genetic Algorithm for Task Graph Scheduling

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

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