Adding energy efficiency to Openstack

Cima, Vojtech; Grazioli, Bruno; Murphy, Seán; Bohnert, Thomas Michael

doi:10.1109/sustainit.2015.7101358

Cited by 18 publications

(10 citation statements)

References 1 publication

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“…As first approximation, few heavy loaded servers consume less power than many lightly loaded servers, due to the nonnegligible energy drawn in idle state. Building on this rule of thumb, many consolidation algorithms use simple linear or stepwise models to describe the trade‐off between CPU usage and power consumption, since CPU is the main source of power consumption . However, these models cannot take into account the complexity brought by the presence of multiple CPUs, multiple cores, power management features (voltage/frequency scaling, low‐power idle, etc.)…”

Section: Related Workmentioning

confidence: 99%

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Balancing QoS and power consumption in programmable 5G infrastructures

Carrega

Portomauro

Repetto

et al. 2018

Trans Emerging Tel Tech

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The advent of fifth generation is promising to push far more intelligence than today to the network boundary, hence boosting novel computing models based on fog/edge paradigms. The need for proximity in computation, coupled with various forms of mobility, will be responsible for dynamic shifting of workload within the system, with large fluctuations in resource usage. This eventually turns into poor energy efficiency of the whole infrastructure. However, improving efficiency usually deteriorates quality of service, hence the dilemma about how to balance these two contrasting aspects. In this paper, we propose a framework that leverages the increasing programmability of ICT infrastructures to pursue a linear relationship between power consumption and workload, while safeguarding quality of service. Our approach is based on workload consolidation and extensions to existing cloud management software. We collected both real measurements from an experimental testbed and performance analysis from simulations to evaluate the consolidation strategy in more complex environments.

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Section: Related Workmentioning

confidence: 99%

“…However, these models cannot take into account the complexity brought by the presence of multiple CPUs, multiple cores, power management features (voltage/frequency scaling, low‐power idle, etc.) . Furthermore, servers from different vendors have different characteristics, and even the behavior of the same server changes while aging .…”

Section: Related Workmentioning

confidence: 99%

Balancing QoS and power consumption in programmable 5G infrastructures

Carrega

Portomauro

Repetto

et al. 2018

Trans Emerging Tel Tech

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“…6.2, where we investigate the impact of uncertainty on the power model, we have used a set of 50 PMs (which is approximately the common size of a rack in a datacenter [3]) and assume that each PM is equipped with different amounts of physical CPU and memory. The parameters related to PMs' power consumption such as maximum power consumption (P max ), idle power consumption (P idle ) are taken from [16], where two types of physical servers (Lynx Calleo 1240, and IBM x3550 M4) are used for an OpenStack testbed setup. However, unlike them, we take a range of values for the parameter P max and P idle in order to present PMs which are invariably heterogeneous in terms of their power consumption.…”

Section: Evaluation Scenarios and Parameter Settingsmentioning

confidence: 99%

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

2018

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Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

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“…Meanwhile, access to virtualized components is given to the client company so that it can build its own IT platforms. [7].  Platform as a Service: often simply referred to as PaaS, is a category of cloud computing that provides a platform and environment to allow developers to build applications and services over the internet.…”

Section: Service Model Of Cloud Computingmentioning

confidence: 99%

New Cloud Economy For Resolving A Problem Cloud Computing Adoption

Elmahouti

Achandair

Khoulji

et al. 2017

TMLAI

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Cloud computing , IT still called to order, it is a model that allows access to information resources on demand, we can consider it as a unique Technology to satisfy needs and respond to customer demands by guaranteeing an efficient quality of serviceIT management.This paper gives an analysis of cloud computing along its components, also the internal and external institutional factors that influence the adoption of cloud computing in the majority of enterprises.It aims to be innovative by answering these questions of allocation and availability of network services in an intradatacenter environment by implementing solutions based on new technologies that is OpenStack[1].The research problem addressed in this paper is the proposal of a new architecture and optimized algorithms of continuity and availability of the service. These will help identify the deployment of existing vulnerabilities. On the other hand, offer a very high level of availability, security and quality of service.

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Adding energy efficiency to Openstack

Cited by 18 publications

References 1 publication

Balancing QoS and power consumption in programmable 5G infrastructures

Balancing QoS and power consumption in programmable 5G infrastructures

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

New Cloud Economy For Resolving A Problem Cloud Computing Adoption

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