Security Supportive Energy Aware Scheduling And Scaling For Cloud Environments

Jakóbik, Agnieszka; Grzonka, Daniel; Kołodziej, Joanna

doi:10.7148/2017-0583

Cited by 12 publications

(14 citation statements)

References 26 publications

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“…Several scheduling strategies have been developed. These strategies include: those based on the ETC-matrix genetic process [22] , such as ETC minimising makespan [23] , and ETC minimising energy [24] , Random, Spread tasks the maximum, Greedy minimising energy, Greedy minimising makespan, Spread tasks the minimum, Spread tasks the minimum with randomness .…”

Section: Scheduling Strategiesmentioning

confidence: 99%

SCORE: Simulator for cloud optimization of resources and energy consumption

Fernández-Cerero

Fernández-Montes

Jakóbik

et al. 2018

Simulation Modelling Practice and Theory

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Achieving efficiency both in terms of resource utilisation and energy consumption is a complex challenge, especially in large-scale wide-purpose data centers that serve cloudcomputing services. Simulation presents an appropriate solution for the development and testing of strategies that aim to improve efficiency problems before their applications in production environments. Various cloud simulators have been proposed to cover different aspects of the operation environment of cloud-computing systems. In this paper, we define the SCORE tool, which is dedicated to the simulation of energy-efficient monolithic and parallel-scheduling models and for the execution of heterogeneous, realistic and synthetic workloads. The simulator has been evaluated through empirical tests. The results of the experiments confirm that SCORE is a performant and reliable tool for testing energyefficiency, security, and scheduling strategies in cloud-computing environments.

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Section: Scheduling Strategiesmentioning

confidence: 99%

SCORE: Simulator for cloud optimization of resources and energy consumption

Fernández-Cerero

Fernández-Montes

Jakóbik

et al. 2018

Simulation Modelling Practice and Theory

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“…The authors propose an energy-aware scheduling policy based on Dynamic Voltage and Frequency Scaling (DVFS) in [32]. Moreover, various approaches look for the reduction of the energy consumption by applying energyproportionality models based on power-proportional distributed file systems in [33]- [35] which generally aim to switch storage-servers off when the replicated data is not needed.…”

Section: Energy Efficiency In Data Centresmentioning

confidence: 99%

Limiting Global Warming by Improving Data-Centre Software

2020

Self Cite

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Carbon emissions, greenhouse gases and pollution in general are usually related to traditional factories, so the most modern computing factories have gone unnoticed for the general-public opinion. We empirically show through extensive and realistic simulation that: 1) energy consumption, and consequently CO 2 emissions, could be reduced from ∼15% to ∼60% if the correct energy-efficiency policies are applied; and 2) such energy-consumption reduction can be achieved without negatively impacting the correct operation of these infrastructures. To this end, this work is focused on the proposal and analysis of a set of energy-efficiency policies which are applied to traditional and hyper-scale data centres, as well as numerous operation environments, including: 1) the top resource managers used in industry; 2) eight energy-efficiency policies, including aggressive, fine-tuned and adaptive models; and 3) three types of workload-arrival patterns. Finally, we present a realistic analysis of the environmental impact of the application of such energy-efficiency policies on USA data centres. The presented results estimate that 11.5 million of tons of CO 2 could be saved, which is equivalent to the removal of 4.79 million of combustion cars, that is, the total car fleet of countries such as Portugal, Austria and Sweden.INDEX TERMS Energy efficiency, data centres, scheduling.

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“…Experiments presented simulate a medium sized data center that runs homogeneous workload that is intended to respond to enduser requests. Energy-aware scheduling policies combined with Dynamic Voltage and Frequency Scaling (DVFS) is presented in [100]. In [77], a multi-objective scheduling algorithm is proposed, based on genetic algorithms, which takes into account energy efficiency, performance and security constraints.…”

Section: Software Solutions For Infrastructure Efficiencymentioning

confidence: 99%

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Kilanioti

Fernández-Montes

Fernández-Cerero

et al. 2019

Lecture Notes in Computer Science

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This chapter presents the authors' work for the Case Study entitled "Delivering Social Media with Scalability" within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with

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Security Supportive Energy Aware Scheduling And Scaling For Cloud Environments

Cited by 12 publications

References 26 publications

SCORE: Simulator for cloud optimization of resources and energy consumption

SCORE: Simulator for cloud optimization of resources and energy consumption

Limiting Global Warming by Improving Data-Centre Software

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

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