Software Consolidation as an Efficient Energy and Cost Saving Solution for a SaaS/PaaS Cloud Model

Tchana, Alain; Palma, Noël De; Safieddine, Ibrahim; Hagimont, Daniel; Diot, Bruno; Vuillerme, Nicolas

doi:10.1007/978-3-662-48096-0_24

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…The authors in [28] introduced a solution for dynamic software consolidation in order to decrease the number of VMs utilized. Software consolidation allows dynamically collocating different software applications on the same VM.…”

Section: Application Levelmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

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Section: Application Levelmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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“…The queuing algorithm is proposed for the placement of containers on VMs to reduce response time and efficient utilization of VMs [53]. Constraint satisfaction programming-based container placement algorithm is proposed to decrease billing cost and energy consumption by reducing the number of instantiated VMs [54]. A metaheuristic approach-based container placement is addressed to reduce migration, energy consumption, increase SLA, VM and PM utilization.…”

Section: Containersmentioning

confidence: 99%

Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers

et al. 2020

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Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.

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“…For example, the proposed queuing algorithm in [15] can be used to map the containers directly onto VMs rather than onto PMs to minimize the required number of VMs and, consequently, to minimize energy consumption. In [16], a placement algorithm for containers on VMs was proposed. The main goal of the proposed placement algorithm was to reduce the number of instantiated VMs to reduce billing costs and power consumption.…”

Section: Placement Of Containers On Vms (C-vm)mentioning

confidence: 99%

A placement architecture for a container as a service (CaaS) in a cloud environment

2019

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Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

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Software Consolidation as an Efficient Energy and Cost Saving Solution for a SaaS/PaaS Cloud Model

Cited by 9 publications

References 13 publications

Energy efficiency in cloud computing data centers: a survey on software technologies

Energy efficiency in cloud computing data centers: a survey on software technologies

Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers

A placement architecture for a container as a service (CaaS) in a cloud environment

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