A novel scheduling with multi-criteria for high-performance computing systems: an improved genetic algorithm-based approach

Biswas, Tarun; Kuila, Pratyay; Ray, Anjan Kumar

doi:10.1007/s00366-018-0676-5

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…The following steps are required in the process of this experiment: programming in CloudSim, parameter setting and performance evaluation. We evaluated the performance of our FHCS with three important traditional heuristic algorithms: GA [15], Ant Colony System (ACS) [24] and PSO [25] method in the same DC. The number of VMs range from 10 to 50.…”

Section: Methodsmentioning

confidence: 99%

“…A dynamic VM consolidation method was presented by Farahnakian et al [14] for eliminating redundant VM migrations and for reducing SLA destructions with the help of a utilisation prediction model. Enhanced GA was proposed by Biswas et al [15] for better scheduling process. Maximisation of resource utilisation, speed up ratio and minimisation of load balancing, make span were considered as multi objectives to perform scheduling process.…”

Section: Literature Surveymentioning

confidence: 99%

“…If we consider the generation size is 'm' then 'm/2' swarm (fittest swarm) is taken for further computations and the remaining are destroyed. The condition (15) defines the fruitfly probability which is taken for further process. The high probability of fruit flies has the most possibilities for the next process and the next generation…”

Section: Fruit Fly Hybridised Cuckoo Searchmentioning

confidence: 99%

See 2 more Smart Citations

FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

2020

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Section: Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Fruit Fly Hybridised Cuckoo Searchmentioning

confidence: 99%

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FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

2020

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“…The problem that should be solved to make the required cargo distribution in a way to get the ideal CG index value is a non-linear problem [20][21][22]. In this respect, the solution is to use evolutionary genetic algorithm GA [23,24], PSO based on particle swarm methodology [25,26], or swarm based (WSA) algorithms based on overlapping and motions attracted by agents [27,28]. With this study, not mentioned in the literature, the problem of load distribution in line with the ideal CG index value will be resolved in n heuristic way, and by extension, fuel consumption and safety will reach the top point on cargo aircrafts.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Cargo Distribution Computation in Airbus A330 Cargo Aircraft with Optimization Algorithms

2023

Teh. vjesn.

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Weight and balance problems are one of the main reasons for cargo aircraft accidents including around 30% of accidents that are due to Center of Gravity (CG). Because the pilots often calculate CG index using Load & Trim Sheets manually or use a set of simple formulas, in these calculations, it is only checked whether CG index is within the safe zone instead of determining the ideal value. In order for the safety and fuel economy to be maximized in an aircraft, CG index should be calculated at the ideal value given in the Aircraft Handling Manual. Due to safety and cost concerns, airline companies prefer non-commercial optimization solutions. Therefore, we proposed new heuristic approaches that have been motivated by a real-world application for a major airline company. First, we applied standard GA, WSA, PSO algorithms to obtain a solution that is as close as possible to the ideal CG index in an Airbus A330 cargo plan. Then, we modified standard WSA and PSO algorithms to decrease the error value and to better achieve the ideal CG index. These proposed heuristic solutions have the potential to help the pilots flying cargo aircraft with maximum safety and minimum fuel consumption.

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“…The performance of hybrid algorithms was compared with many heuristics and metaheuristics. Authors in [24] proposed a novel genetic algorithm (Im-GA) based independent task scheduling technique to optimize four conflicting objectives, makespan, load balancing, resource utilization, and speed up ratio using a novel mutation technique. Authors in [25] also proposed a GA based scheduling algorithm for scheduling Bag-of-tasks in computational grids using modified mutation strategy to improve the makespan.…”

Section: B Scheduling On Independent Sequential Jobsmentioning

confidence: 99%

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Chhabra*,

Singh,

Kahlon

2019

IJRTE

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Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

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A novel scheduling with multi-criteria for high-performance computing systems: an improved genetic algorithm-based approach

Cited by 19 publications

References 26 publications

FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

Application of Cargo Distribution Computation in Airbus A330 Cargo Aircraft with Optimization Algorithms

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

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