Cost Optimization of Real-Time Cloud Applications Using Developmental Genetic Programming

Deniziak, Stanislaw; Ciopinski, Leszek; Pawiński, Grzegorz; Wieczorek, Karol; Bak, Slawomir

doi:10.1109/ucc.2014.126

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…Kim et al [38] modeled a real-time service as a real-time virtual machine request and proposed a number of algorithms to reduce power consumption. The developmental genetic programming approach has been proposed by Deniziak [39] to schedule soft real-time applications specified as a set of distributed echo algorithms. Their objective was to minimize the total costs of the IaaS services, while guaranteeing QoS.…”

Section: Literature Reviewmentioning

confidence: 99%

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

2017

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Abstract:In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

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Section: Literature Reviewmentioning

confidence: 99%

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

2017

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“…The majority of research reviewed was empirical however this is skewed by the computer science research which was substantially based on experimentation. Examples of empirical analysis are [20], who present a methodology for the cost optimization of IaaS application, and [21], who present a cloud computing brokerage model to identify the most cost-efficient tariff for IaaS services. Examples of nonempirical analysis are [22], who present a general mathematical model for ROI estimation, and [23], who propose a taxonomy of the costs associated with application migration to the cloud.…”

Section: Descriptive Analysis and Discussionmentioning

confidence: 99%

Quantifying the Financial Value of Cloud Investments: A Systematic Literature Review

Rosati

Fox

Kenny

et al. 2017

2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. This paper conducts a multidisciplinary systematic literature review drawing from CS, IS, and Business disciplines to understand the current evidence on the quantification of financial value from cloud computing investments. The study identified 53 articles, which were coded in an analytical framework across six themes (measurement type, costs, benefits, adoption type, actor and service model). Future research directions were presented for each theme. The review highlights the need for multidisciplinary research which both explores and further develops the conceptualization of value in cloud computing research, and research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios.

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“…Although the method gives quite good results, we will consider to apply more sophisticated optimization methods like constraint logic programming, mathematical programming [22] and developmental genetic programming [23].…”

Section: Discussionmentioning

confidence: 99%

“…A 5 : Φ(v 6 ,{m6 6 }) → ( [5,3], [13,29],{x 9 ,m13 7 ,m14 8 }) | Φ(v 6 ,{m16 6 }) → ( [5,3], [13,29],{x 10 ,m15 5 ,m14 8 } A 6 :Φ(v 7 ,{m11 7 }) → ( [9,5], [23,50],{x 11 ,m16 6 }) | Φ(v 7 ,{m13 7 }) → ( [9,5], [23,50],{x 12 [5,3], [12,30],{m28 5 …”

Section: M12 2 })mentioning

confidence: 99%

Scheduling of Distributed Algorithms for Low Power Embedded Systems

Deniziak¹,

Dzitkowski²

2016

ijacsa

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Abstract-Recently, the advent of embedded multicore processors has created interesting technologies for power management. Systems consisting of low-power and high-efficient cores create new possibilities for the optimization of power consumption. However, new design methods, dedicated to these technologies should be developed. In this paper we present a method of static task scheduling for low-power real-time embedded systems. We assume that the system is specified as a distributed algorithm, then it is implemented using multi-core embedded processor with low-power processing capabilities. We propose a new scheduling method to create the optimal or suboptimal schedule. The goal of optimization is to minimize the power consumption while all time constraints will be satisfied or the quality of service will be as high as possible. We present experimental results, obtained for sample systems, showing advantages of our method.

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Cost Optimization of Real-Time Cloud Applications Using Developmental Genetic Programming

Cited by 15 publications

References 16 publications

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Quantifying the Financial Value of Cloud Investments: A Systematic Literature Review

Scheduling of Distributed Algorithms for Low Power Embedded Systems

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