Sadhasivam narayanan (15 risultati)
- Brossura
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 51,43
EUR 2,29 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: As New. Unread book in perfect condition.
- Brossura
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 55,12
EUR 2,29 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
- Brossura
Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 53,92
EUR 13,85 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. In.
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 53,71
EUR 17,34 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 55,74
EUR 17,34 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Condizione: As New. Unread book in perfect condition.
Altre immagini- Brossura
Da: preigu, Osnabrück, Germaniapreigu
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 39,99
EUR 70,00 spedizioneSpedito da Germania a U.S.A.Quantità: 5 disponibili
Taschenbuch. Condizione: Neu. Certain Investigation on Improved PSO Algorithm for Workflow Scheduling in Cloud Computing Environments | Sadhasivam Narayanan | Taschenbuch | 44 S. | Englisch | 2017 | Anchor Academic Publishing | EAN 9783960671923 | Verantwortliche Person für die EU: Dryas Verlag, ein Imprint der Bedey und Thoms M…edia GmbH, Hermannstal 119k, 22119 Hamburg, kontakt[at]dryas[dot]de | Anbieter: preigu.
- Brossura
- Print on Demand
Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 57,49
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Brossura
- Print on Demand
Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 56,26
EUR 3,80 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Brossura
- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 39,99
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud servi…ce providers and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find good solutions quickly in a restricted area. However, the heuristics optimization methods do not provide a suitable solution for the scheduling problem.Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems. In order to minimize the defined objective of task resource mapping, improved versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling performance with less computational burden. In recent years, PSO has been successfully applied to solve different kinds of problems. It is famous for its easy realization and fast convergence, while suffering from the possibility of early convergence to local optimums. In the proposed Improved Particle Swarm Optimization (IPSO) algorithm, whenever early convergence occurs, the original particle swarm would be considered the worst positions an individual particle and worst positions global particle the whole swarm have experienced. 44 pp. Englisch.
- Brossura
- Print on Demand
Da: Majestic Books, Hounslow, , Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 62,28
EUR 7,51 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand pp. 44.
- Brossura
- Print on Demand
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 67,94
EUR 3,47 spedizioneSpedito in U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand pp. 44.
- Brossura
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 63,18
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND pp. 44.
- Brossura
- Print on Demand
Da: moluna, Greven, , Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 39,99
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud… computing environments. The cloud service providers and consumers.
- Brossura
- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 39,99
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud service p…roviders and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find good solutions quickly in a restricted area. However, the heuristics optimization methods do not provide a suitable solution for the scheduling problem.Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems. In order to minimize the defined objective of task resource mapping, improved versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling performance with less computational burden. In recent years, PSO has been successfully applied to solve different kinds of problems. It is famous for its easy realization and fast convergence, while suffering from the possibility of early convergence to local optimums. In the proposed Improved Particle Swarm Optimization (IPSO) algorithm, whenever early convergence occurs, the original particle swarm would be considered the worst positions an individual particle and worst positions global particle the whole swarm have experienced.Diplomica Verlag, Hermannstal 119k, 22119 Hamburg 44 pp. Englisch.
- Brossura
- Print on Demand
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 39,99
EUR 60,43 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud service pr…oviders and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find good solutions quickly in a restricted area. However, the heuristics optimization methods do not provide a suitable solution for the scheduling problem.Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems. In order to minimize the defined objective of task resource mapping, improved versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling performance with less computational burden. In recent years, PSO has been successfully applied to solve different kinds of problems. It is famous for its easy realization and fast convergence, while suffering from the possibility of early convergence to local optimums. In the proposed Improved Particle Swarm Optimization (IPSO) algorithm, whenever early convergence occurs, the original particle swarm would be considered the worst positions an individual particle and worst positions global particle the whole swarm have experienced.
