On Achieving Energy Efficiency and Reducing CO₂Footprint in Cloud Computing

Abstract

With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2 emissions. To date, little is known about how to incorporate energy consumption and CO2 concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2 metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2 footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2 at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2 emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2 footprint and consequent environmental impact of cloud applications.

Efficient Data Confidentiality and Portability in Cloud Storage

“…On account of the distributed storage benefit must be adaptable, security limits and assurance hardware can't be unmistakably characterized, which builds some trouble for the usage of particular assurance measures; [8](2) the distributed storage transmits information through the system. The benefit interferences, information devastation, data stolen furthermore, altered caused by the noxious assaults in the organize represent a serious test to the security of information correspondences, get to confirmation and classification; [9](3) from the client's view, the distributed storage of information makes distributed computing specialist co-op gets the information get to control, and the client's information is looked with protection security dangers. Individuals stress over that the touchy individual information will be exposure, abuse or missing by putting the information in cloud condition [10].…”

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

“…(Kurpicz et al, 2016). The CO 2 emissions depends on the energy consumption and on the electricity mix (Wajid et al, 2015). One could compute these costs at each time and divide them proportionally to the number of resources booked by each VM.…”

“…While the live monitoring issue has already been addressed in literature (Wajid et al, 2015), the accounting issue has received little attention.…”

Characterizing Energy per Job in Cloud Applications

“…While these metrics are useful for assessing energy efficiency, they are difficult to apply in case the need of evaluating the energy consumption of an application arises, since they provide only a relative evaluation (i.e., percentage) of the consumption. To be able to assess if reserved resources for an application are being used effectively, in the ECO 2 Clouds project [3,17], new metrics have been introduced to evaluate energy efficiency not only at the infrastructure and data center levels, but also at the application level [18]. The proposed metrics are relative metrics that allow assessing the percentage of waste at the application level, but do not focus on directly measuring power or energy consumption.…”

“…Other cases considered in the study showed characteristics similar to one of the aforementioned two cases. An experimental analysis of the possible different configurations was studied through simulation and comparing the results in a federated cloud environment, provided within the ECO 2 Clouds project [17]. Within ECO 2 Clouds, the main focus was on deploying VMs so that CO 2 emission is minimized, through adaptive scheduling algorithms based on the monitored ecometrics.…”