Take a Break: Cloud Scheduling Optimized for Real-Time Electricity Pricing

Lučanin, Dražen; Brandić, Ivona

doi:10.1109/cgc.2013.25

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…This contribution addresses research question (RQ) 1. It was previously published in [132] and is presented in Chapter 4 of this thesis. SCIENTIFIC CONTRIBUTION 2.…”

Section: Rq2 -Sc2 Rq1 -Sc1mentioning

confidence: 99%

A Cloud Controller for Performance-Based Pricing

Lucanin

Pietri²,

Brandić³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

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Abstract-New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On the other hand, cloud providers are facing the problem of growing operational energy costs. This raises a trade-off problem between energy savings and revenue loss when performing actions such as CPU frequency scaling. Although existing cloud controllers for managing cloud resources deploy frequency scaling, they only consider fixed virtual machine (VM) pricing. In this paper we propose a performance-based pricing model adapted for VMs with different CPU-boundedness properties. We present a cloud controller that scales CPU frequencies to achieve energy cost savings that exceed service revenue losses. We evaluate the approach in a simulation based on real VM workload, electricity price and temperature traces, estimating energy cost savings up to 32% in certain scenarios. I. INTRODUCTIONWith the wide range of VM types, heterogeneous infrastructure and different computing environments including VMs and containers [1], estimating the performance of provisioned resources is becoming increasingly challenging [2]. New cloud pricing schemes are emerging where resources are priced based on the delivered performance. For example, the CPU frequency provided to a VM at runtime determines the price [3], with high CPU frequencies being more expensive. We call this model performance-based pricing and it is used in production by cloud providers, such as ElasticHosts [4]. Though this approach mainly targets users, it could also be used by cloud providers to control their energy consumption. Energy consumption of data centers is becoming a major issue, accounting for 1.5% of global electricity usage [5]. Furthermore, modern clouds may consist of geographically-distributed data centers influenced by dynamic local factors, such as real-time electricity prices [6] and temperature-dependent cooling [7], that we call geotemporal inputs.We call the subsystem of the cloud, that determines the actions to allocate and manage the VMs, a cloud controller. Adapting the cloud controller to geotemporal inputs through actions, such as CPU frequency scaling, raises a trade-off problem between the potential energy savings and service revenue losses incurred under performance-based pricing. This is the challenge at the core of this paper.Frequency scaling is a power management technique commonly used to lower the operating frequency of hardware resources in order to reduce power consumption [8]. However, frequency reduction may degrade the performance of resources. Depending on the workload characteristics, workload

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“…This contribution addresses research question (RQ) 1. It was previously published in [132] and is presented in Chapter 4 of this thesis. SCIENTIFIC CONTRIBUTION 2.…”

Section: Rq2 -Sc2 Rq1 -Sc1mentioning

confidence: 99%

A Cloud Controller for Performance-Based Pricing

Lucanin

Pietri²,

Brandić³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

Self Cite

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“…(2) peak pauser scheduler [16] -pauses the managed VMs for a predefined duration every day, choosing the hours of the day that are statistically most likely to have the highest energy cost, thus reducing VM availability, but also the average energy cost. Both schedulers depend on geotemporal inputs.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Cloud Management Through Progressive SLA Specification

Lučanin

Jrad

Brandić

et al. 2014

Economics of Grids, Clouds, Systems, and Services

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Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed virtual machine (VM) may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energyaware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel service level agreement (SLA) specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid'5000 workloads. The results show higher customer conversion and 39% average energy savings per VM.

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“…Therefore, works cite the implementation of power-aware scheduling techniques to take advantage of the prices, focusing on electrical energy consumption (HSU; LIU; WU, 2011), (REN; HE; XU, 2012), (WU; CHANG; CHAN, 2014), (KIM; CHO; SEO, 2014),(LUCA- NIN;BRANDIC, 2013), and to optimize the infrastructure deployment of renewable energy sources (MINH; SAMEJIMA, 2015),(GOIRI et al, 2011),(MÄSKER et al, 2016). These works present different approaches to managing the workload according to the electrical energy consumption perspective.…”

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confidence: 99%

“…These works present different approaches to managing the workload according to the electrical energy consumption perspective. The cited techniques apply contract requirements, electricity availability and prices, and the monitoring of the physical infrastructure, under specified operational conditions and contracted service levels Lucanin and Brandic (LUCANIN;BRANDIC, 2013). define an optimized scheduling based on the energy grid situation and the energy prices but include the renewable sources supply to establish allocation priority.…”

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Energy management for cloud computing environment.

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Take a Break: Cloud Scheduling Optimized for Real-Time Electricity Pricing

Cited by 6 publications

References 38 publications

A Cloud Controller for Performance-Based Pricing

A Cloud Controller for Performance-Based Pricing

Energy-Aware Cloud Management Through Progressive SLA Specification

Energy management for cloud computing environment.

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