Cloud Architectures, Networks, Services, and Management

Fonseca, Nelson Luís Saldanha da; Boutaba, Raouf

doi:10.1002/9781119042655.ch1

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Then, these binary variables satisfy the constraints of Equation ( 3). The number of accepted tasks N can be achieved by Equation (4), where z i is calculated by Equation ( 5), which is an indicator of whether task t i is accepted in the hybrid cloud. The accepted ratio can be calculated by Equation (6).…”

Section: Task Processing Modelmentioning

confidence: 99%

“…The third approach exploits the hybrid cloud technology, where the provider temporarily extends its private cloud by renting ondemand resources from public clouds, when some user demand cannot be met. Therefore, most SMEs prefer a mixture of cloud computing models [4]. The hybrid cloud model has been applied in many fields, e.g., healthcare [5], virtual reality [6], smart farms [7], and robotics [8].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

2023

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The hybrid cloud has attracted more and more attention from various fields by combining the benefits of both private and public clouds. Task scheduling is still a challenging open issue to optimize user satisfaction and resource efficiency for providing services by a hybrid cloud. Thus, in this paper, we focus on the task scheduling problem with deadline and security constraints in hybrid clouds. We formulate the problem into mixed-integer non-linear programming, and propose a polynomial time algorithm by integrating swarm intelligence into the genetic algorithm, which is named SPGA. Specifically, SPGA uses the self and social cognition exploited by particle swarm optimization in the population evolution of GA. In each evolutionary iteration, SPGA performs the mutation operator on an individual with not only another individual, as in GA, but also the individual’s personal best code and the global best code. Extensive experiments are conducted for evaluating the performance of SPGA, and the results show that SPGA achieves up to a 53.2% higher accepted ratio and 37.2% higher resource utilization, on average, compared with 12 other scheduling algorithms.

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Section: Task Processing Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

2023

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“…Hybrid cloud, combining a local cloud (private cloud) and a public cloud, is a cost-efficient way to address the problem of insufficient resources in the local cloud when its users have a peak resource demand as the peak load is much larger than average, but transient [1]. Surveyed by the European Network and Information Security Agency (ENISA), most of the small to medium enterprises prefer a mixture of cloud computing models (public cloud, private cloud) [2].…”

Section: Introductionmentioning

confidence: 99%

Managing Deadline-constrained Bag-of-Tasks Jobs on Hybrid Clouds with Closest Deadline First Scheduling

Wang¹,

Song²,

Sun³

et al. 2016

KSII TIIS

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“…Hybrid cloud, combining a local cloud (private cloud) and a public cloud, is a cost-efficient way to address the problem of insufficient resources in the local cloud when its users have a peak resource demand as the peak load is much larger than average, but transient [1]. Surveyed by the European Network and Information Security Agency (ENISA), most of the small SpringSim-HPC 2016 April 3-6, Pasadena, CA, USA c 2016 Society for Modeling & Simulation International (SCS) to medium enterprises prefer a mixture of cloud computing models (public cloud, private cloud) [2].…”

Section: Introductionmentioning

confidence: 99%

Managing Deadline-constrained Bag-of-Tasks Jobs on Hybrid Clouds

Wang¹,

Song²,

Sun³

et al. 2016

Preprint

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Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources. In this paper, we study on managing deadline-constrained bag-of-tasks jobs on hybrid clouds. We present a binary nonlinear programming (BNP) problem to model the hybrid cloud management where the utilization of physical machines (PMs) in the local cloud/cluster is maximized when the local resources are enough to satisfy the deadline constraints of jobs, while when not, the rent cost from the public cloud is minimized. To solve this BNP problem in polynomial time, we proposed a heuristic algorithm. Its main idea is assigning the task closest to its deadline to current core until the core cannot finish any task within its deadline. When there is no available core, the algorithm adds an available PM with most capacity or rents a new VM with highest cost-performance ratio. Extensive experimental results show that our heuristic algorithm saves 16.2%-76% rent cost and improves 47.3%-182.8% resource utilizations satisfying deadline constraints, compared with first fit decreasing algorithm.

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Cloud Architectures, Networks, Services, and Management

Cited by 11 publications

References 11 publications

An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

Managing Deadline-constrained Bag-of-Tasks Jobs on Hybrid Clouds with Closest Deadline First Scheduling

Managing Deadline-constrained Bag-of-Tasks Jobs on Hybrid Clouds

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