An optimized content delivery approach based on demand–supply theory in disruption-tolerant networks

2022

Soft Comput

Self Cite

One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the subproblems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as stateof-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

“…Similar to what we did in Eqs. ( 7) and ( 12), we can write the following relationships using resource mapping vectors to virtual machines: (17) , .…”

Section: B) Cost Of Carbon Dioxide Productionmentioning

confidence: 99%

“…The method of selecting the initial population may have an important effect on the speed of convergence [17]. If no information is available, the random method is used [14].…”

Section: -1-1-initial Populationmentioning

confidence: 99%

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Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Abbasi-khazaei

2022

Soft Comput

Self Cite

“…To confirm the accuracy of the simulation results, we must perform statistical analysis. Analysis of Variance (ANOVA) compares statistics among the three methods 39,40 . For this purpose, we perform the ANOVA test on three methods: BDARA , traditional double‐auction, and non‐auction .…”

Section: Performance Evaluationmentioning

confidence: 99%

A bartering double auction resource allocation model in cloud environments

Koochaksaraei

Haghighat

Concurrency and Computation

2022

Self Cite

Resource provisioning and allocation are two essential operations of cloud service providers (CSPs). Due to limited resources, it may sometimes not be cost-effective for CSPs to obtain them from the market. On the other hand, releasing some less profitable resources may violate the service level agreement (SLA). This article presents an auction-inspired model for providing/demanding resources without exchanging money among CSPs. The core idea is that CSPs do not need to pay monetary values. Instead, they exchange free resources through an auction mechanism and are encouraged to cooperate. This model, which we call bartering double auction resource allocation, helps CSPs receive the resources they need in the form of virtual machine instances with different types of functionalities. Under this mechanism, if one party violates the SLA, the other takes back its resources to avoid possible loss. The experimental results using the CloudSim simulator and the statistical analyses show that the proposed mechanism can increase CSPs' social welfare and profitability while significantly reducing SLA violations. Also, the proposed model provides four essential economic properties, that is, individual rationality, incentive compatibility, budget balancing, and economic efficiency.

mentioning

confidence: 99%

Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

Abbasi-khazaei

2022

Preprint

Self Cite

One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.