Multi-objective Parallel Machines Scheduling for Fault-Tolerant Cloud Systems

Gąsior, Jakub; Seredyński, Franciszek

doi:10.1007/978-3-319-03859-9_21

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…Table 1 presents the parameter settings of the selected scheduling techniques. The parameter values of the ACO are based on [ 29 , 50 ], while the parameter values for the GA are based on [ 24 , 51 ]. The parameter settings for the GBLCA are based on [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…GA is a metaheuristic optimization method inspired by the Darwinian evolutionary theory [ 22 , 23 ]. Ga̧sior and Seredyński [ 24 ] put forward a multi-objective parallel machine scheduling technique using GA to increase fault tolerance adaptability in the Cloud computing environment. The approach provides not just a single optimal solution, but a set of results that are not subjugated by one another.…”

Section: Related Workmentioning

confidence: 99%

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Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

et al. 2016

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Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

et al. 2016

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“…Here table 2 shows some of the selected scheduling algorithm's parameter settings. Parameter settings of Genetic Algorithm (GA) are inspired from [13,14] whereas in case Ant Colony Optimization (ACO) parameters are taken from [15,16] and that of the same from GBLCA are derived from [5].…”

Section: Experimentationmentioning

confidence: 99%

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Gade¹,

Mundukur²,

Thakare³

2019

ISI

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Scheduling is a heart of cloud computing as without appropriate scheduling it is impossible to get the desired results. Primary focus of this article is to focus on minimization of makespan, minimum utilization of resources and make the cloud services economic for an independant task. Out of the various task scheduling strategies, in last few years meta-heuristic algorithms have gained recognition in successful operation of task scheduling algorithms. League Championship based Algorithm (LCA) is fascinated from sports leagues through which best team/task in this case can be find out for scheduling. Task scheduling using Adaptive League Championship Algorithm (ALCA) is employed in this article and thereby it shrinks makespan, cloud utilization and cost. ALCA is implemented with cloudsim simulator using java as a programming tool and scheduling has followed the non-premptive approach. Implementation of ALCA results reducation in makespan by 32.95 %, 20.99 % and 7.29 % against customary Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Global League Championship Algorithm (GBLCA) respectively. ALCA also reduces significantly cloud utilization value and improves economy of scale. ALCA may serve as preferred choice for cloud broker as it proved to be multipurpose in the area of makespan, resource utilization and economy.

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“…Lin and Li (2004) presented a polynomial algorithm for the parallel machine scheduling problem with unit-length jobs. Many people successfully developed the genetic algorithm (GA) for parallel machine scheduling problem with different constraints and objectives, such as minimizing the completion on non-identical parallel machines with fuzzy logic (Alcan and Başlıgil, 2011), workflow balancing with precedence constraints (Rajakumar et al, 2006), minimizing the weighted completion time with precedence (Ramachandra and Elmaghraby, 2006), minimizing the maximum lateness with dynamic job arrivals (Malve and Uzsoy, 2007), and multi-objective (Gasior and Seredynski, 2013). However, there is a noticeable gap between the theory and the practice of scheduling, because some real situations are ignored.…”

Section: Introductionmentioning

confidence: 99%

A human–machine interaction approach of block erection schedule with three-dimensional spatial constraints

Bao

Zhen

Zhang

et al. 2016

Concurrent Engineering

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When the spatial constraints are taken into account, the problem about dispatch and optimization of block erection in shipbuilding becomes more complicated. A human-machine interaction approach is proposed and extended to quickly find an approximate optimal solution. First, a steer genetic algorithm enables users to guide the computing to an effective iterative direction and leverages the computing. The model of block erection dispatch to the crane is a parallel machine scheduling problem with spatial constraints. The genetic crossover operators can be guided according to the activities of block erection in shipbuilding. Two criteria are presented to justify the effectiveness of the proposed algorithm and a fitness function is given to represent the makespan. Moreover, three-dimensional spatial constraints, which affect the parallel machine scheduling problem heavily, are visualized. A better erection path can be obtained dynamically, when the users are in an immersive environment. Finally, a three-dimensional layout prototype system of block erection is developed with real size, embedded with an interactive genetic algorithm optimization to solve this problem with the objective of minimizing makespan.

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Multi-objective Parallel Machines Scheduling for Fault-Tolerant Cloud Systems

Cited by 14 publications

References 13 publications

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

A human–machine interaction approach of block erection schedule with three-dimensional spatial constraints

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