Joint Optimization of Resource Provisioning in Cloud Computing

Chase, Jonathan; Niyato, Dusit

doi:10.1109/tsc.2015.2476812

Cited by 78 publications

(18 citation statements)

References 25 publications

(36 reference statements)

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“…The value of annual power costs is calculated based on the unit cost of power usage of all the equipment, such as cooling systems, servers, routers, and lighting. For instance, , based on the Tenaga National sources, the value of a unit cost for commercial sectors in Malaysia is 0.39 Ringgit [50].…”

Section: Working Of Proposed Pemc Calculatormentioning

confidence: 99%

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

et al. 2020

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The power consumption of cloud data centers has a considerable impact on the environment and climate change nowadays. Researchers are seeking to find practical solutions to reduce power consumption in data centers while guaranteeing the desired level of services and service level objectives. With the establishment of the data center industry, the demand for computation and data storage has been continually rising. Energy efficiency is one of the most significant issues faced by these big data centers to meet such high computational requirements. There are many industry acceptable metrics available such as PUE, DCiE, DCP, etc. Power Usage Effectiveness (PUE) metric has proven to be the most popular in measuring energy efficiency; however, it measures the power efficiency alone with no consideration for CO 2 emissions and the costs involved in total power usage across data centers. In this paper, we proposed a novel Power Efficiency Measurement Calculator (PEMC) that combines and calculates the power efficiency, CO 2 emissions, and the total annual costs incurred. The pseudocode and algorithm to perform these specific PUE, DCiE, and CO 2 emission functions are given to explain the working of proposed work. Finally, the proposed PEMC calculator was tested and validated through a case study performed in one of the tierlevel data centers in Malaysia and the results demonstrate its effectiveness compared with Power Usage Effectiveness (PUE) and other known calculators.

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Section: Working Of Proposed Pemc Calculatormentioning

confidence: 99%

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

et al. 2020

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“…Research approaches in the area of cloud cost optimisation are also divided by the perspective of optimisation. As observed through literature review and several surveys [6,13,14,[26][27][28], many resource scheduling solutions and optimisation algorithms are intended for infrastructure service providers, dealing with the optimisation of resource allocation in data centres [29][30][31][32]. Fewer solutions are provided for optimisation of services for SaaS providers, who often need a way to evaluate if the placement of the service in a cloud environment is optimal from the perspective of execution cost and quality of service.…”

Section: Related Workmentioning

confidence: 99%

Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

Stupar

Huljenić

2019

Scientific Programming

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With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.

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“…A stochastic programming formulation was presented in Chase and Niyato to allocate optimally VMs and bandwidth in a CC environment and handling both demand and price uncertainty. But, cost optimization of resource provisioning was not at required level.…”

Section: Related Workmentioning

confidence: 99%

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Gokulraj¹,

Geetha²

2018

Int J Communication

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Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation-based firefly resource cost optimization (PSC-FRCO) technique is designed. Pearson service correlation-based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation-based firefly resource cost optimization technique initially uses Pearson service correlation in which the user-required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost-optimized cloud resources to users to afford required service from the cloud server. Thus, PSC-FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC-FRCO technique enhances enhances RUE and lessens RT as compared to state-of-the-art works.

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Joint Optimization of Resource Provisioning in Cloud Computing

Cited by 78 publications

References 25 publications

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

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