Greenslater: On Satisfying Green SLAs in Distributed Clouds

Amokrane, Ahmed; Langar, Rami; Zhani, Mohamed Faten; Boutaba, Raouf; Pujolle, Guy

doi:10.1109/tnsm.2015.2440423

Cited by 37 publications

(12 citation statements)

References 22 publications

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“…GreenSLA concept is being subject of recent attention by the academic community [6], [16], [17], [18], [19], [20]. In [17], authors propose a novel approach for the reduction of energy consumption in data centres that will yield benefits both in terms of running costs and its environmental impact, where the method is based on the introduction of collaborative interactions and flexibility clauses in contracts between all the DC ecosystem entities.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, a performance evaluation based on a proof of concept implementation is presented in this paper. Recent proposals [18], [19], [20] tackles the GreenSLA as a contract between the ITC and DC where several strategies are proposed inside the DC to reduce energy consumption and increase the use of renewable energy when available. The specific strategies used in the DC depends on the nature of its systems.…”

Section: Related Workmentioning

confidence: 99%

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Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts

Basmadjian

Botero

Giuliani

et al. 2018

IEEE Trans. Smart Grid

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Abstract-The power grid has become a critical infrastructure, which modern society cannot do without. It has always been a challenge to keep power supply and demand in balance; the more so with the recent rise of intermittent renewable energy sources. Demand response schemes are one of the counter measures, traditionally employed with large industrial plants. This paper suggests to consider data centres as candidates for demand response as they are large energy consumers and as they are able to adapt their power profile sufficiently well. To unlock this potential, we suggest a system of contracts that regulate collaboration and economic incentives between the data centre and its energy supplier (GreenSDA) as well as between the data centre and its customers (GreenSLA). Several presented use cases serve to validate the suitability of data centers for demand response schemes.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts

Basmadjian

Botero

Giuliani

et al. 2018

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

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“…In [19] and [20], a dynamic, on-demand VM migration based algorithms were proposed for distributed data centres. These works were devoted to reducing the carbon footprint based on specified service level agreement (SLA).…”

Section: Related Workmentioning

confidence: 99%

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Alhumaima

Ahmed

Al-Raweshidy

2018

IEEE Trans. on Green Commun. Netw.

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Abstract-In cloud radio access networks (C-RAN), more accurate prediction of the number of virtual machines (VMs) one server can support would improve network capacity and energy efficiency (EE). In this paper, the problem of allocating an optimal number of VMs to the cloud server is introduced. Monte Carlo based evolutionary algorithm (PSO, QPSO or GA) are used to find the suboptimal number of VMs that optimises the energy efficiency (EE) of C-RAN. To enable such evaluation, a power model is proposed to evaluate the power consumption (PC) of each unit within a virtualised server. This evaluation occurs under the circumstances of increased number of hosted VMs, and processed resource blocks (RBs) at each VM. Moreover, power allocation methods are proposed to transmit the power from base band unit (BBU) pool to the remote radio heads (RRHs), and from RRHs to the users (UEs). This allocation is based on the combination of one or more of RRH distance, RRH channel gain, UE distance, UE channel gain, and UE path loss. The EE problem was constrained to crucial quality of service (QoS) indicators, including minimum UE data rate, number of allocated RBs, and latency imposed due to virtualisation.

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“…Amorkane et al proposed a cloud resource management framework, called Greenslater, to investigate the way cloud providers can meet SLAs with green requirements. Their proposed framework allows cloud providers to provision virtual data centers, which are distributed geographically.…”

Section: Related Workmentioning

confidence: 99%

Toward a hierarchical and architecture‐based virtual machine allocation in cloud data centers

Rahmanian

Horri

Dastghaibyfard

2017

Int J Communication

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Number of cloud data centers which consists of hundreds of hosts has increased tremendously around the world due to increase in demands for cloud services. It is expected energy consumption of data centers will reach 139.8 billion Kwh by 2020. Many algorithms are proposed to reduce energy consumption as well as service level agreement violationby minimizing the number of active hosts.Current proposed algorithms do not consider data center architecture, the physical position of hosts, and energy consumption of numerous switches that are in data centers. In this paper, a novel hierarchical cloud resource management is proposed that not only minimizes the number of hosts but also aggregates virtual machines on a limited subset of data center racks and modules to minimize energy consumption. Experimental results with Cloudsim show that our proposed algorithm reduces energy consumption up to 26% and service level agreement violation up to 96%. KEYWORDS data center architecture, dynamic consolidation, power efficiency, resource management, virtualization 1 | INTRODUCTIONIn recent years, cloud computing has attracted attention from both the academic and industrial communities. Cloud computing services are pay-as-you-go and are easily accessible via internet around the world. 1,2 The infrastructure for cloud services are cloud data centers that are composed of hundreds of computational hosts (servers), tens of high speed switches, and other network devices, all of which consume a significant amount of energy. Number of cloud data centers has increased tremendously around the world due to increase in demands for cloud services.Data center energy consumption in the world was 61 billion kWh in 2006 and 100 billion kWh in 2011, and it is expected to reach 139.8 billion kWh by 2020. [3][4][5] Hence, data center energy consumption is of great concern because it increases carbon dioxide (CO 2 ) emission, which has a catastrophic environmental impact. 6 Servers, storage, switches, and other network devices are the main sources of energy consumption in data centers. These devices consume energy near maximum power levels even when they are idle or not fully utilized.Cloud services are provided through virtualization, 7-9 ie, assigning a virtual machine (VM) for each cloud service. 10 Virtualization technology allows allocation and execution of multiple VMs on a host that leads to an increase in host utilization that brings about energy efficiency in a cloud data center. Virtualization technology also provides live migration VMs from 1 host to another in case a host is overutilized or underutilized. There will be a waste of energy if servers are underutilized, because servers use around 30% of their peak power consumption while they are in idle mode 70% of time. 11To tackle energy consumption of underutilized hosts, a common solution is VM consolidation. 9,12 If a host is underutilized or has no work to do, VM consolidation if possible, tries to live migrate its VMs to another host and switch it to sleep mode or turns it off to con...

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Greenslater: On Satisfying Green SLAs in Distributed Clouds

Cited by 37 publications

References 22 publications

Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts

Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Toward a hierarchical and architecture‐based virtual machine allocation in cloud data centers

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