Bi-objective heterogeneous consolidation in cloud computing

L.-A. Galaviz-Alejos; F. Armenta-Cano; A. Tchernykh; G. Radchenko; A.Y. Drozdov; O. Sergiyenko; R. Yahyapour; Mocskos E.; Nesmachnow S.

doi:10.1007/978-3-319-73353-1_27

Bi-objective heterogeneous consolidation in cloud computing

L.-A. Galaviz-Alejos, F. Armenta-Cano, A. Tchernykh, G. Radchenko, A.Y. Drozdov, O. Sergiyenko, R. Yahyapour, Mocskos E. (Redakteur), Nesmachnow S.

Publikation: Konferenzband/Beitrag in Buch/Bericht › Konferenzartikel › Begutachtung

Abstract

In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs. © Springer International Publishing AG 2018.

Originalsprache	Englisch
Titel	Latin American High Performance Computing Conference
Untertitel	CARLA 2017: High Performance Computing
Seiten	384-398
Seitenumfang	15
Band	796
DOIs	https://doi.org/10.1007/978-3-319-73353-1_27
Publikationsstatus	Veröffentlicht - 2018
Veranstaltung	4th Latin American Conference on High Performance Computing - , Argentinien Dauer: 20 Sep. 2017 → 22 Sep. 2017 http://carla2017.ccarla.org/

Konferenz

Konferenz	4th Latin American Conference on High Performance Computing
Kurztitel	CARLA 2017
Land/Gebiet	Argentinien
Zeitraum	20/09/17 → 22/09/17
Internetadresse	http://carla2017.ccarla.org/

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10.1007/978-3-319-73353-1_27

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Galaviz-Alejos, L.-A., Armenta-Cano, F., Tchernykh, A., Radchenko, G., Drozdov, A. Y., Sergiyenko, O., Yahyapour, R., E., M. (Hrsg.), & S., N. (2018). Bi-objective heterogeneous consolidation in cloud computing. in Latin American High Performance Computing Conference: CARLA 2017: High Performance Computing (Band 796, S. 384-398) https://doi.org/10.1007/978-3-319-73353-1_27

@inproceedings{d157e8e1b4b24eeda00b881f6a59b3d1,

title = "Bi-objective heterogeneous consolidation in cloud computing",

abstract = "In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs. {\textcopyright} Springer International Publishing AG 2018.",

keywords = "Consolidation, Energy aware scheduling, Green cloud, SLA violations, Virtual machine, Benchmarking, Cloud computing, Economic and social effects, Energy utilization, Network security, Pareto principle, Power management, Quality control, Quality of service, Scheduling, Virtual addresses, Critical applications, Energy-aware scheduling, Experimental evaluation, Green Clouds, Optimized performance, Pareto optimal solutions, Scheduling strategies, Green computing",

author = "L.-A. Galaviz-Alejos and F. Armenta-Cano and A. Tchernykh and G. Radchenko and A.Y. Drozdov and O. Sergiyenko and R. Yahyapour and Mocskos E. and Nesmachnow S.",

note = "Conference code: 209259 Export Date: 27 August 2021 Correspondence Address: Tchernykh, A.; CICESE Research CenterMexico; email: chernykh@cicese.mx; 4th Latin American Conference on High Performance Computing, CARLA 2017 ; Conference date: 20-09-2017 Through 22-09-2017",

year = "2018",

doi = "10.1007/978-3-319-73353-1_27",

language = "English",

isbn = "18650929 (ISSN); 9783319733524 (ISBN)",

volume = "796",

pages = "384--398",

booktitle = "Latin American High Performance Computing Conference",

url = "http://carla2017.ccarla.org/",

}

Galaviz-Alejos, L-A, Armenta-Cano, F, Tchernykh, A, Radchenko, G, Drozdov, AY, Sergiyenko, O, Yahyapour, R, E., M (Hrsg.) & S., N 2018, Bi-objective heterogeneous consolidation in cloud computing. in Latin American High Performance Computing Conference: CARLA 2017: High Performance Computing. Bd. 796, S. 384-398, 4th Latin American Conference on High Performance Computing, Argentinien, 20/09/17. https://doi.org/10.1007/978-3-319-73353-1_27

TY - GEN

T1 - Bi-objective heterogeneous consolidation in cloud computing

AU - Galaviz-Alejos, L.-A.

AU - Armenta-Cano, F.

AU - Tchernykh, A.

AU - Radchenko, G.

AU - Drozdov, A.Y.

AU - Sergiyenko, O.

AU - Yahyapour, R.

AU - S., Nesmachnow

A2 - E., Mocskos

N1 - Conference code: 209259 Export Date: 27 August 2021 Correspondence Address: Tchernykh, A.; CICESE Research CenterMexico; email: chernykh@cicese.mx

PY - 2018

Y1 - 2018

N2 - In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs. © Springer International Publishing AG 2018.

AB - In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs. © Springer International Publishing AG 2018.

KW - Consolidation

KW - Energy aware scheduling

KW - Green cloud

KW - SLA violations

KW - Virtual machine

KW - Benchmarking

KW - Cloud computing

KW - Economic and social effects

KW - Energy utilization

KW - Network security

KW - Pareto principle

KW - Power management

KW - Quality control

KW - Quality of service

KW - Scheduling

KW - Virtual addresses

KW - Critical applications

KW - Energy-aware scheduling

KW - Experimental evaluation

KW - Green Clouds

KW - Optimized performance

KW - Pareto optimal solutions

KW - Scheduling strategies

KW - Green computing

U2 - 10.1007/978-3-319-73353-1_27

DO - 10.1007/978-3-319-73353-1_27

M3 - Conference Paper

SN - 18650929 (ISSN); 9783319733524 (ISBN)

VL - 796

SP - 384

EP - 398

BT - Latin American High Performance Computing Conference

T2 - 4th Latin American Conference on High Performance Computing

Y2 - 20 September 2017 through 22 September 2017

ER -

Bi-objective heterogeneous consolidation in cloud computing

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