research-article

StorNIR, a multi-objective replica placement strategy for cloud federations

Authors:

Amina Chikhaoui,

Laurent Lemarchand,

Kamel Boukhalfa,

Jalil BoukhobzaAuthors Info & Claims

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

Pages 50 - 59

https://doi.org/10.1145/3412841.3441886

Published: 22 April 2021 Publication History

Abstract

Federation of clouds makes it possible to transparently extend the resources of Cloud Service Providers (CSPs). For storage services several metrics need to be considered to satisfy customers QoS, that is storage performance, network latency and data availability. Data replication is a key strategy to optimize such metrics. For a CSP, member of a Federation, an effective placement of customers data object replicas is crucial to satisfy QoS demands. In this paper, we modeled the replica placement problem as a multi-objective optimization problem (MOOP) taking into account the local storage classes, other federation CSPs (external) storage services, and customers requirements. To solve this problem, we propose StorNIR a cost-efficient data object Storing scheme based on NSGAII upgraded with Injection and Reparation operators. StorNIR is a matheuristic that consists in hybridizing an exact method with NSGAII meta-heuristic. A repair operator was designed to make the solutions feasible with regards to the system constraints (storage volume, IOPs, etc). StorNIR performed better than both NSGAII meta-heuristic and the exact method in terms of quality of solutions and scalability. The repair function improves the NSGAII meta-heuristic up to 7 times with 7.4% more extra time execution. On average, StorNIR enhances by 17 times the quality of the initial solutions calculated by CPLEX in terms of Hypervolume. In addition, the designed matheuristic approach can be generalized to other meta-heuristics than NSGAII such as MOPSO meta-heuristic.

References

[1]

[n. d.]. Cloud Storage Market:Forecasts from 2017 to 2022. https://www.researchandmarkets.com/reports/4306260/cloud-storage-market-forecasts-from-2017-to-2022.

[2]

accessed May, 2020. Amazon Data Transfer. "https://aws.amazon.com/s3/pricing/".

[3]

accessed September, 2020. HDFS Architecture Guide. "https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html".

[4]

Brunelle Alan D. 2008. blktrace User Guide. (2008).

[5]

Masoud Saeida Ardekani and Douglas B Terry. 2014. A self-configurable geo-replicated cloud storage system. In 11th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 14). 367--381.

[6]

Marcio RM Assis and Luiz Fernando Bittencourt. 2016. A survey on cloud federation architectures: identifying functional and non-functional properties. Journal of Network and Computer Applications 72 (2016), 51--71.

Digital Library

[7]

Djillali Boukhelef, Jalil Boukhobza, Kamel Boukhalfa, Hamza Ouarnoughi, and Laurent Lemarchand. 2019. Optimizing the cost of DBaaS object placement in hybrid storage systems. Future Generation Computer Systems 93 (2019), 176--187.

Digital Library

[8]

Jalil Boukhobza and Pierre Olivier. 2017. Flash Memory Integration: Performance and Energy Issues. Elsevier. https://www.sciencedirect.com/book/9781785481246/flash-memory-integration.

[9]

Joel Chacón and Carlos Segura. 2018. Analysis and Enhancement of Simulated Binary Crossover. In 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 1--8.

[10]

Amina Chikhaoui, Kamel Boukhalfa, and Jalil Boukhobza. 2018. A Cost Model for Hybrid Storage Systems in a Cloud Federations. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 1025--1034.

[11]

Carlos A Coello Coello, Gregorio Toscano Pulido, and M Salazar Lechuga. 2004. Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation 8, 3 (2004), 256--279.

Digital Library

[12]

Brian F Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. 2010. Benchmarking cloud serving systems with YCSB. In Proceedings of the 1st ACM symposium on Cloud computing. ACM, 143--154.

Digital Library

[13]

Jean-Emile Dartois, Heverson B Ribeiro, Jalil Boukhobza, and Olivier Barais. 2019. Cuckoo: Opportunistic mapreduce on ephemeral and heterogeneous cloud resources. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 396--403.

[14]

Kalyanmoy Deb, Ram Bhushan Agrawal, et al. 1995. Simulated binary crossover for continuous search space. Complex systems 9, 2 (1995), 115--148.

[15]

Yu Gu, Dongsheng Wang, and Chuanyi Liu. 2014. DR-Cloud: Multi-cloud based disaster recovery service. Tsinghua Science and Technology 19, 1 (2014), 13--23.

[16]

Carlos Guerrero, Isaac Lera, and Carlos Juiz. 2018. Migration-aware genetic optimization for mapreduce scheduling and replica placement in hadoop. Journal of Grid Computing 16, 2 (2018), 265--284.

Digital Library

[17]

Mohammad Hamdan. 2012. On the disruption-level of polynomial mutation for evolutionary multi-objective optimisation algorithms. Computing and Informatics 29, 5 (2012), 783--800.

[18]

Chunlin Li, YaPing Wang, Hengliang Tang, and Youlong Luo. 2019. Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud. Future Generation Computer Systems 100 (2019), 921--937.

Digital Library

[19]

Zhichao Li, Ming Chen, Amanpreet Mukker, and Erez Zadok. 2015. On the trade-offs among performance, energy, and endurance in a versatile hybrid drive. ACM Transactions on Storage (TOS) 11, 3 (2015), 1--27.

Digital Library

[20]

Sai-Qin Long, Yue-Long Zhao, and Wei Chen. 2014. MORM: A Multi-objective Optimized Replication Management strategy for cloud storage cluster. Journal of Systems Architecture 60, 2 (2014), 234--244.

Digital Library

[21]

Najme Mansouri and Mohammad Masoud Javidi. 2020. A review of data replication based on meta-heuristics approach in cloud computing and data grid. Soft Computing (2020), 1--28.

[22]

Yaser Mansouri and Rajkumar Buyya. 2016. To move or not to move: Cost optimization in a dual cloud-based storage architecture. Journal of Network and Computer Applications 75 (2016), 223--235.

Digital Library

[23]

Yaser Mansouri, Adel Nadjaran Toosi, and Rajkumar Buyya. 2017. Data storage management in cloud environments: Taxonomy, survey, and future directions. ACM Computing Surveys (CSUR) 50, 6 (2017), 1--51.

Digital Library

[24]

Nima Jafari Navimipour and Bahareh Alami Milani. 2016. Replica selection in the cloud environments using an ant colony algorithm. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC). IEEE, 105--110.

[25]

Kwangsung Oh, Abhishek Chandra, and Jon Weissman. 2017. TripS: Automated multi-tiered data placement in a geo-distributed cloud environment. In Proceedings of the 10th ACM International Systems and Storage Conference. 1--11.

Digital Library

[26]

Hamza Ouarnoughi, Jalil Boukhobza, Frank Singhoff, and Stéphane Rubini. 2014. A multi-level I/O tracer for timing and performance storage systems in IaaS cloud. In REACTION.

Digital Library

[27]

Seonyeong Park, Youngjae Kim, Bhuvan Urgaonkar, Joonwon Lee, and Euiseong Seo. 2011. A comprehensive study of energy efficiency and performance of flash-based SSD. Journal of Systems Architecture 57, 4 (2011), 354--365.

Digital Library

[28]

Ansar Rafique, Dimitri Van Landuyt, Vincent Reniers, and Wouter Joosen. 2017. Towards an adaptive middleware for efficient multi-cloud data storage. In Proceedings of the 4th Workshop on CrossCloud Infrastructures & Platforms. 1--6.

Digital Library

[29]

Salma Rebai, Makhlouf Hadji, and Djamal Zeghlache. 2015. Improving profit through cloud federation. In Consumer Communications and Networking Conference (CCNC), 2015 12th Annual IEEE. IEEE, 732--739.

[30]

Rashed Salem, Mustafa Abdul Salam, Hatem Abdelkader, and Ahmed Awad Mohamed. 2019. An artificial bee colony algorithm for data replication optimization in cloud environments. IEEE Access 8 (2019), 51841--51852.

[31]

Amir Taherkordi, Feroz Zahid, Yiannis Verginadis, and Geir Horn. 2018. Future cloud systems design: challenges and research directions. IEEE Access 6 (2018), 74120--74150.

[32]

Douglas B Terry, Vijayan Prabhakaran, Ramakrishna Kotla, Mahesh Balakrishnan, Marcos K Aguilera, and Hussam Abu-Libdeh. 2013. Consistency-based service level agreements for cloud storage. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. ACM, 309--324.

Digital Library

[33]

Adel Nadjaran Toosi, Rodrigo N Calheiros, Ruppa K Thulasiram, and Rajkumar Buyya. 2011. Resource provisioning policies to increase iaas provider's profit in a federated cloud environment. In High Performance Computing and Communications (HPCC), 2011 IEEE 13th International Conference on. IEEE, 279--287.

Digital Library

[34]

Pengwei Wang, Caihui Zhao, Wenqiang Liu, Zhen Chen, and Zhaohui Zhang. 2020. Optimizing data placement for cost effective and high available multi-cloud storage. Computing and Informatics 39, 1--2 (2020), 51--82.

[35]

Pengwei Wang, Caihui Zhao, and Zhaohui Zhang. 2018. An ant colony algorithm-based approach for cost-effective data hosting with high availability in multi-cloud environments. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 1--6.

[36]

Zhe Wu, Michael Butkiewicz, Dorian Perkins, Ethan Katz-Bassett, and Harsha V Madhyastha. 2013. Spanstore: Cost-effective geo-replicated storage spanning multiple cloud services. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. 292--308.

Digital Library

[37]

Wanhao Yang and Yan Hu. 2018. A replica management strategy based on MOEA/D. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2154--2159.

[38]

Miao Zhang, Huiqi Li, Li Liu, and Rajkumar Buyya. 2018. An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds. Distributed and Parallel Databases 36, 2 (2018), 339--368.

Digital Library

[39]

Quanlu Zhang, Shenglong Li, Zhenhua Li, Yuanjian Xing, Zhi Yang, and Yafei Dai. 2015. CHARM: A cost-efficient multi-cloud data hosting scheme with high availability. IEEE Transactions on Cloud computing 3, 3 (2015), 372--386.

Cited By

Ait-Oucheggou LRubini SBattou ABoukhobza J(2025)QM-ARC: QoS-aware Multi-tier Adaptive Cache Replacement StrategyFuture Generation Computer Systems10.1016/j.future.2024.107548163(107548)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107548
Sundara Kumar MMohan H(2024)Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniquesJournal of Intelligent & Fuzzy Systems10.3233/JIFS-24006946:4(10863-10882)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-240069
Bang TDouglas CCrooks NHellerstein J(2024)SkyPIE: A Fast & Accurate Oracle for Object PlacementProceedings of the ACM on Management of Data10.1145/36393102:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639310
Show More Cited By

Recommendations

Single-objective and multi-objective formulations of solution selection for hypervolume maximization
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

A new trend in evolutionary multi-objective optimization (EMO) is the handling of a multi-objective problem as an optimization problem of an indicator function. A number of approaches have been proposed under the name of indicator-based evolutionary ...
Hybrid strategy of multi-objective differential evolution H-MODE for multi-objective optimisation

Evolutionary multi-objective optimisation EMO algorithms are preferred for solving the multi-objective optimisation MOO problems due to their ability of producing multiple solutions in a single run. In this study, hybridisation of the traditional ...
An r-dominance-based preference multi-objective optimization for many-objective optimization

Evolutionary multi-objective optimization (EMO) algorithms have been used in finding a representative set of Pareto-optimal solutions in the past decade and beyond. However, most of Pareto domination-based multi-objective optimization evolutionary ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

March 2021

2075 pages

ISBN:9781450381048

DOI:10.1145/3412841

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Jiman Hong
Soongsil University, South Korea
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Eunjee Song
Baylor University

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

SAC '21

Sponsor:

SIGAPP

SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing

March 22 - 26, 2021

Virtual Event, Republic of Korea

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
88
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 11 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ait-Oucheggou LRubini SBattou ABoukhobza J(2025)QM-ARC: QoS-aware Multi-tier Adaptive Cache Replacement StrategyFuture Generation Computer Systems10.1016/j.future.2024.107548163(107548)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107548
Sundara Kumar MMohan H(2024)Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniquesJournal of Intelligent & Fuzzy Systems10.3233/JIFS-24006946:4(10863-10882)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-240069
Bang TDouglas CCrooks NHellerstein J(2024)SkyPIE: A Fast & Accurate Oracle for Object PlacementProceedings of the ACM on Management of Data10.1145/36393102:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639310
Bensalah HAli AKhelaifa A(2024)The KD-GATS algorithm: A way for Optimizing Data Replication with Kruskal-Dijkstra and Genetic Tabu Strategy2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS62114.2024.10541255(1-8)Online publication date: 24-Apr-2024
https://doi.org/10.1109/PAIS62114.2024.10541255
Hussain MAzar AAhmed RUmar Amin SQureshi BDinesh Reddy VAlam IKhan Z(2023)SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog NetworksSensors10.3390/s2302066723:2(667)Online publication date: 6-Jan-2023
https://doi.org/10.3390/s23020667
Liu MPan LLiu S(2023)Cost Optimization for Cloud Storage from User Perspectives: Recent Advances, Taxonomy, and SurveyACM Computing Surveys10.1145/358288355:13s(1-37)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582883
Ait-Oucheggou LRubini SBattou ABoukhobza J(2023)Investigating Multi-Tier and QoS-Aware Caching Based on ARC2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS59514.2023.10387601(1-4)Online publication date: 16-Oct-2023
https://doi.org/10.1109/MASCOTS59514.2023.10387601
Jia JWang P(2022)Low Latency Deployment of Service-based Data-intensive Applications in Cloud-Edge Environment2022 IEEE International Conference on Web Services (ICWS)10.1109/ICWS55610.2022.00023(57-66)Online publication date: Jul-2022
https://doi.org/10.1109/ICWS55610.2022.00023
Ait-Oucheggou LNaas MHadjadj-Aoul YBoukhobza J(2022)When IoT Data Meets Streaming in the Fog2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC)10.1109/ICFEC54809.2022.00014(50-57)Online publication date: May-2022
https://doi.org/10.1109/ICFEC54809.2022.00014
Chirkova RDoyle JReutter J(2021)Ensuring Data Readiness for Quality Requirements with Help from Procedure ReuseJournal of Data and Information Quality10.1145/342815413:3(1-15)Online publication date: 27-Apr-2021
https://dl.acm.org/doi/10.1145/3428154

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten