FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Shojafar, Mohammad; Javanmardi, Saeed; Abolfazli, Saeid; Cordeschi, Nicola

doi:10.1007/s10586-014-0420-x

Cited by 145 publications

(53 citation statements)

References 26 publications

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“…Significant efforts have been made to provide efficient methods to solve the scheduling problem. Based on fuzzy theory and a genetic algorithm, Shojafar et al [15, 16] presented a hybrid job scheduling approach to assign jobs with reducing total execution time and execution cost in cloud computing. Using the gravitational emulation local search algorithm, Hosseinabadi et al [17] proposed a novel algorithm to solve the job-shop scheduling problem in Small and Medium Enterprises.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Independent Partitions in Integrated Modular Avionics Systems

Chen

Han

2016

PLoS ONE

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Recently the integrated modular avionics (IMA) architecture has been widely adopted by the avionics industry due to its strong partition mechanism. Although the IMA architecture can achieve effective cost reduction and reliability enhancement in the development of avionics systems, it results in a complex allocation and scheduling problem. All partitions in an IMA system should be integrated together according to a proper schedule such that their deadlines will be met even under the worst case situations. In order to help provide a proper scheduling table for all partitions in IMA systems, we study the schedulability of independent partitions on a multiprocessor platform in this paper. We firstly present an exact formulation to calculate the maximum scaling factor and determine whether all partitions are schedulable on a limited number of processors. Then with a Game Theory analogy, we design an approximation algorithm to solve the scheduling problem of partitions, by allowing each partition to optimize its own schedule according to the allocations of the others. Finally, simulation experiments are conducted to show the efficiency and reliability of the approach proposed in terms of time consumption and acceptance ratio.

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Section: Introductionmentioning

confidence: 99%

Scheduling Independent Partitions in Integrated Modular Avionics Systems

Chen

Han

2016

PLoS ONE

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“…This work is based on the swarm intelligence techniques of bacterial foraging optimization (BFO) [13] and evolutionary computing concept of genetic algorithm (GA) [14][15][16]. BFO was introduced by the person Passino and it is inspired by the social foraging behavior of Escherichia coli.…”

Section: International Journal Of Intelligent Engineering and Systemsmentioning

confidence: 99%

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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Cloud computing is a powerful computing technology, which render a flexible services at anywhere to the user. One of the major issue of cloud computing was scheduling. In this work, a bacterial foraging optimization algorithm with genetic algorithm (GABFO) was combined to find out trustworthy scheduling problems in cloud workflow. Generally job scheduling and resource allocation in cloud is a tedious optimization problem at the time of considering QoS requirements. Lot of existing works under scheduling only concentrates on cost optimization and deadline problems, and it ignores the importance of reliability, availability and robustness. The main subscription of my work is to state a new optimized approach to schedule the jobs efficiently and allocate the resources in a efficient manner by introducing GABFO algorithm. Experiments were done in PSO, Genetic, BFO and then Genetic and BFO was combined to generate a hybrid optimized result, and the work was compared with above mentioned algorithms. The algorithms were executed for 52 iterations and totally 10 runs are calculated. The size of the job as well as virtual machines was varied for each iteration to calculate performance variation. We considered the optimization parameter as time and cost, and throughput. The work is implemented in cloudsim to create a simulated cloud environment. Final result shows better performance and maximum resource utilization in GABFO when compared to PSO, GA, BFO.

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“…Establishing a fuzzy logic model starts with the identification of input and output linguistic variables and the shaping of the fuzzy rules [26,[29][30][31]. The fuzzy numbers, used in the expression of linguistic variables, can take different shapes such as triangular, trapezoid, bell shaped and curved.…”

Section: Fuzzy Logic Modelmentioning

confidence: 99%

A new fuzzy logic based career guidance system: WEB-CGS

et al. 2017

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Original scientific papers Choosing a career affects individuals' social life deeply in terms of many dimensions. However, choosing the right career is becoming increasingly difficult given the existence of an increasing number of professions and training opportunities. Consequently, the importance of career orientation increases. In this study, a system that can automatically offer vocational guidance has been developed. This new system is referred to as WEB-CGS (webbased carrier guidance system) and works as a fuzzy logic based web service. The aim is that it will make it easier for an individual to choose the right profession. In this system, students' prior educational successes and teachers' views were integrated in a manner which made it possible to identify the students' professional interests and capacities. The system forecasts vocational school students' interest with regard to Information Technology, ElectricsElectronics, Accounting, and Automotive. Promising results were obtained with regard to 300 unbiased 9 th grade students in terms of orienting them towards an appropriate profession. Keywords: artificial intelligence; career guidance; higher education; student profiling Novi sustav profesionalnog usmjeravanja zasnovan na neizrazitoj logici: WEB-CGSIzvorni znanstveni članak Izbor zanimanja na mnogo načina uvelike utječe na društveni život pojedinaca. Ipak, izbor odgovarajuće karijere postaje sve teži kad se uzme u obzir postojanje sve većeg broja zanimanja i mogućnosti usmjeravanja. Shodno tome, sve je veća važnost profesionalnog usmjeravanja. U ovom se radu razvija sustav pomoću kojega se automatski može ponuditi profesionalno usmjeravanje. To je WEB-CGS (web-based carrier guidance system) koji funkcionira kao web usluga koja se zasniva na neizrazitoj (fuzzy) logici. Cilj je olakšati pojedincu izbor odgovarajućeg zanimanja. U tom sustavu integrirani su prethodni uspjesi u obrazovanju učenika i mišljenja nastavnika te je omogućeno prepoznavanje profesionalnih interesa i mogućnosti učenika. Sustav predviđa interes učenika za usmjeravanje u području informacijske tehnologije, elektrike-elektronike, računovodstva i industrije automobila. Postignuti su obećavajući rezultati usmjeravanja za 300 neopredijeljenih studenata 9. razreda u strukovnoj srednjoj školi.

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FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Cited by 145 publications

References 26 publications

Scheduling Independent Partitions in Integrated Modular Avionics Systems

Scheduling Independent Partitions in Integrated Modular Avionics Systems

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

A new fuzzy logic based career guidance system: WEB-CGS

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