Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments
Load balancing, in Cloud Computing (CC) environment, is defined as the method of splitting workloads and computing properties. It enables the enterprises to manage workload demands or application demands by distributing the resources among computers, networks or servers. In this research article, a...
Saved in:
Published in | Journal of parallel and distributed computing Vol. 142; pp. 36 - 45 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Inc
01.08.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Load balancing, in Cloud Computing (CC) environment, is defined as the method of splitting workloads and computing properties. It enables the enterprises to manage workload demands or application demands by distributing the resources among computers, networks or servers. In this research article, a new load balancing algorithm is proposed as a hybrid of firefly and Improved Multi-Objective Particle Swarm Optimization (IMPSO) technique, abbreviated as FIMPSO. This technique deploys Firefly (FF) algorithm to minimize the search space where as the IMPSO technique is implemented to identify the enhanced response. The IMPSO algorithm works by selecting the global best (gbest) particle with a small distance of point to a line. With the application of minimum distance from a point to a line, the gbest particle candidates could be elected. The proposed FIMPSO algorithm achieved effective average load for making and enhanced the essential measures like proper resource usage and response time of the tasks. The simulation outcome showed that the proposed FIMPSO model exhibited an effective performance when compared with other methods. From the simulation outcome, it is understood that the FIMPSO algorithm yielded an effective result with the least average response time of 13.58ms, maximum CPU utilization of 98%, memory utilization of 93%, reliability of 67% and throughput of 72% along with a make span of 148, which was superior to all the other compared methods.
•Develop an Energy Efficient Load Balancing Technique in Cloud Computing Environment.•Propose a Hybrid firefly with Improved MultiObjective PSO called FIMPSO algorithm.•Incorporate IMPSO technique in the starting phase of FF algorithm.•FIMPSO algorithm offers effective load balancing, resource usage and response time. |
---|---|
AbstractList | Load balancing, in Cloud Computing (CC) environment, is defined as the method of splitting workloads and computing properties. It enables the enterprises to manage workload demands or application demands by distributing the resources among computers, networks or servers. In this research article, a new load balancing algorithm is proposed as a hybrid of firefly and Improved Multi-Objective Particle Swarm Optimization (IMPSO) technique, abbreviated as FIMPSO. This technique deploys Firefly (FF) algorithm to minimize the search space where as the IMPSO technique is implemented to identify the enhanced response. The IMPSO algorithm works by selecting the global best (gbest) particle with a small distance of point to a line. With the application of minimum distance from a point to a line, the gbest particle candidates could be elected. The proposed FIMPSO algorithm achieved effective average load for making and enhanced the essential measures like proper resource usage and response time of the tasks. The simulation outcome showed that the proposed FIMPSO model exhibited an effective performance when compared with other methods. From the simulation outcome, it is understood that the FIMPSO algorithm yielded an effective result with the least average response time of 13.58ms, maximum CPU utilization of 98%, memory utilization of 93%, reliability of 67% and throughput of 72% along with a make span of 148, which was superior to all the other compared methods.
•Develop an Energy Efficient Load Balancing Technique in Cloud Computing Environment.•Propose a Hybrid firefly with Improved MultiObjective PSO called FIMPSO algorithm.•Incorporate IMPSO technique in the starting phase of FF algorithm.•FIMPSO algorithm offers effective load balancing, resource usage and response time. |
Author | Devaraj, A. Francis Saviour Lydia, E. Laxmi Elhoseny, Mohamed Shankar, K. Dhanasekaran, S. |
Author_xml | – sequence: 1 givenname: A. Francis Saviour surname: Devaraj fullname: Devaraj, A. Francis Saviour email: saviodev@gmail.com organization: Department of Computer Science and Engineering, Kalasalingam Academy of Research and education, India – sequence: 2 givenname: Mohamed surname: Elhoseny fullname: Elhoseny, Mohamed email: mohamed_elhoseny@mans.edu.eg organization: Faculty of Computers and Information, Mansoura University, Egypt – sequence: 3 givenname: S. surname: Dhanasekaran fullname: Dhanasekaran, S. email: srividhans@gmail.com organization: Department of Computer Science and Engineering, Kalasalingam Academy of Research and education, India – sequence: 4 givenname: E. Laxmi surname: Lydia fullname: Lydia, E. Laxmi email: elaxmi2002@yahoo.com organization: Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), India – sequence: 5 givenname: K. surname: Shankar fullname: Shankar, K. email: drkshankar@ieee.org organization: Department of Computer Applications, Alagappa University, Karaikudi, India |
BookMark | eNp9kNGK1DAUhoOs4OzqC3iVF2g9SdqZFryRQd2FlVlQr0OanIyntElJMyPjy_iqtq7e7MVeHfg53w__d82uQgzI2FsBpQCxfdeX_eRsKUFCCaoEKV-wjYB2W0BTNVdsA7tKFTsl6lfsep57ACHqXbNhv28vXSJHv0ymGHj03FNCP1y4CY7fjVOKZ3T8y2nIVBy6Hm2mM_IHkzLZAfnXnyaN_DBlGv93mOEYE-UfI_cxcQyYjheO3pMlDJkP0TjemcEES-HIKfD9EE-O7-M4nfIaYThTimFcvufX7KU3w4xv_t0b9v3Tx2_72-L-8Plu_-G-sAogF7ZprKmqbWe9BHACobbKd60yXi6JQRTGtko4iXLXqbZqocMaF1811LIW6oY1j702xXleDGhL-e-enAwNWoBeReter6L1KlqD0ovoBZVP0CnRaNLleej9I4TLqDNh0vOqx6Jb9NusXaTn8D8QAp5L |
CitedBy_id | crossref_primary_10_1007_s11227_021_03810_8 crossref_primary_10_1007_s10586_021_03322_3 crossref_primary_10_1016_j_apor_2021_102837 crossref_primary_10_1016_j_ijcce_2024_05_002 crossref_primary_10_1155_2022_3434646 crossref_primary_10_1016_j_aej_2024_01_067 crossref_primary_10_1007_s13198_021_01244_2 crossref_primary_10_3233_JIFS_234054 crossref_primary_10_3390_s23073488 crossref_primary_10_1016_j_jksuci_2020_11_002 crossref_primary_10_1002_cpe_6484 crossref_primary_10_1109_ACCESS_2020_3015541 crossref_primary_10_1016_j_comcom_2023_06_018 crossref_primary_10_3390_info13020092 crossref_primary_10_1371_journal_pone_0252756 crossref_primary_10_1142_S1793962322500428 crossref_primary_10_1155_2022_7372450 crossref_primary_10_1155_2022_4931374 crossref_primary_10_1002_ett_4902 crossref_primary_10_1109_ACCESS_2020_3020054 crossref_primary_10_1007_s10586_024_04625_x crossref_primary_10_3390_electronics13132578 crossref_primary_10_1016_j_egyr_2021_02_064 crossref_primary_10_1080_10584587_2021_1911269 crossref_primary_10_32604_cmc_2022_021859 crossref_primary_10_3233_MGS_210343 crossref_primary_10_32604_iasc_2023_032942 crossref_primary_10_3390_en15239164 crossref_primary_10_1016_j_matpr_2020_06_564 crossref_primary_10_1016_j_jksuci_2022_03_016 crossref_primary_10_1007_s12530_024_09586_5 crossref_primary_10_32604_cmc_2021_018719 crossref_primary_10_1016_j_future_2024_02_025 crossref_primary_10_4018_IJCAC_318698 crossref_primary_10_3390_pr12030519 crossref_primary_10_32604_cmc_2022_022063 crossref_primary_10_2139_ssrn_4140548 crossref_primary_10_1007_s11227_021_03695_7 crossref_primary_10_1080_1206212X_2023_2260616 crossref_primary_10_1111_exsy_13150 crossref_primary_10_1016_j_suscom_2020_100453 crossref_primary_10_1007_s00366_021_01455_y crossref_primary_10_1007_s42979_024_03577_8 crossref_primary_10_1155_2021_6693810 crossref_primary_10_3390_en14217036 crossref_primary_10_1007_s10586_024_04718_7 crossref_primary_10_1155_2021_2592451 crossref_primary_10_1007_s00366_021_01450_3 crossref_primary_10_1016_j_jpdc_2021_02_020 crossref_primary_10_1109_ACCESS_2021_3070344 crossref_primary_10_32604_csse_2023_029854 crossref_primary_10_1080_0952813X_2022_2153280 crossref_primary_10_1007_s11277_022_09592_3 crossref_primary_10_1109_ACCESS_2020_3033774 crossref_primary_10_1007_s11277_021_08400_8 crossref_primary_10_1007_s41060_025_00718_x crossref_primary_10_3390_en16062900 crossref_primary_10_32604_csse_2023_031582 crossref_primary_10_3390_app11135849 crossref_primary_10_1007_s00607_021_00920_2 crossref_primary_10_32604_iasc_2024_050681 crossref_primary_10_1109_ACCESS_2025_3529839 crossref_primary_10_1155_2022_5604141 crossref_primary_10_1007_s00521_023_08208_6 crossref_primary_10_1109_TPDS_2023_3334519 crossref_primary_10_1002_2050_7038_13189 crossref_primary_10_1016_j_heliyon_2024_e37912 crossref_primary_10_1007_s41870_023_01549_4 crossref_primary_10_1109_ACCESS_2023_3265954 crossref_primary_10_1016_j_aej_2021_06_008 crossref_primary_10_1016_j_jksuci_2021_12_003 crossref_primary_10_1007_s11334_022_00508_9 crossref_primary_10_1155_2021_9967531 crossref_primary_10_1002_cpe_7136 crossref_primary_10_1007_s11277_023_10250_5 crossref_primary_10_1155_2021_6670534 crossref_primary_10_32604_csse_2022_019622 crossref_primary_10_1002_cpe_6722 crossref_primary_10_1080_00150193_2021_1902779 crossref_primary_10_1080_17455030_2021_1998729 crossref_primary_10_3390_biomimetics8050396 crossref_primary_10_1109_ACCESS_2024_3420173 crossref_primary_10_1007_s11277_021_09410_2 crossref_primary_10_1016_j_suscom_2020_100502 crossref_primary_10_1007_s12065_024_00986_9 crossref_primary_10_1109_ACCESS_2020_3047830 crossref_primary_10_1007_s11042_024_19559_0 crossref_primary_10_1016_j_future_2022_11_031 crossref_primary_10_4018_IJCAC_311503 crossref_primary_10_1016_j_asoc_2022_108794 crossref_primary_10_3390_info15060317 crossref_primary_10_1016_j_asoc_2024_111391 crossref_primary_10_1016_j_future_2024_107561 crossref_primary_10_2478_acss_2023_0017 crossref_primary_10_1007_s13369_021_06279_y crossref_primary_10_1155_2021_8081618 crossref_primary_10_3390_math8112008 crossref_primary_10_1016_j_future_2022_06_005 crossref_primary_10_1002_spe_2954 crossref_primary_10_1007_s11277_021_09022_w crossref_primary_10_1007_s11277_023_10520_2 crossref_primary_10_1016_j_eswa_2023_121450 crossref_primary_10_3390_app122111115 crossref_primary_10_1002_cpe_8127 crossref_primary_10_1155_2022_9226647 crossref_primary_10_1016_j_jpdc_2022_09_009 crossref_primary_10_1088_2631_8695_ad4233 crossref_primary_10_1109_ACCESS_2024_3352078 crossref_primary_10_1007_s12652_020_02670_z crossref_primary_10_1016_j_eswa_2021_115713 crossref_primary_10_1016_j_procs_2022_12_058 crossref_primary_10_1016_j_measurement_2020_108771 crossref_primary_10_1080_1448837X_2024_2309424 crossref_primary_10_1155_2021_6685456 crossref_primary_10_1016_j_jnca_2023_103788 crossref_primary_10_1007_s11277_024_11311_z crossref_primary_10_1002_cpe_7839 crossref_primary_10_1007_s10489_020_01887_x |
Cites_doi | 10.1016/j.ins.2016.05.007 10.1016/j.jpdc.2011.04.007 10.1007/s10723-019-09485-z 10.1016/j.compeleceng.2013.11.023 10.1016/j.future.2013.01.010 10.1109/ICSPCT.2014.6884903 10.1108/17563780810893482 10.1504/IJBET.2018.094122 10.1109/ACCESS.2019.2948704 10.1016/j.comnet.2019.106860 10.1016/j.future.2019.11.012 10.1145/1721654.1721672 10.1016/j.jnca.2017.03.008 10.1016/j.asoc.2013.12.008 10.1016/j.future.2019.11.003 10.1109/IranianCIS.2014.6802559 |
ContentType | Journal Article |
Copyright | 2020 Elsevier Inc. |
Copyright_xml | – notice: 2020 Elsevier Inc. |
DBID | AAYXX CITATION |
DOI | 10.1016/j.jpdc.2020.03.022 |
DatabaseName | CrossRef |
DatabaseTitle | CrossRef |
DatabaseTitleList | |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Computer Science |
EISSN | 1096-0848 |
EndPage | 45 |
ExternalDocumentID | 10_1016_j_jpdc_2020_03_022 S0743731520300459 |
GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 1~5 29L 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFSI ABJNI ABMAC ABTAH ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADFGL ADHUB ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CAG COF CS3 DM4 DU5 E.L EBS EFBJH EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA K-O KOM LG5 LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TN5 TWZ WUQ XJT XOL XPP ZMT ZU3 ZY4 ~G- ~G0 AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH |
ID | FETCH-LOGICAL-c300t-c88ca446bcf200d1e05c3fb93af2f20aee1ac931d2e27b39490be5e2025052513 |
IEDL.DBID | .~1 |
ISSN | 0743-7315 |
IngestDate | Thu Apr 24 23:16:06 EDT 2025 Tue Jul 01 03:20:49 EDT 2025 Fri Feb 23 02:47:59 EST 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | IMPSO Task scheduling Cloud computing Firefly Load balancing |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c300t-c88ca446bcf200d1e05c3fb93af2f20aee1ac931d2e27b39490be5e2025052513 |
PageCount | 10 |
ParticipantIDs | crossref_citationtrail_10_1016_j_jpdc_2020_03_022 crossref_primary_10_1016_j_jpdc_2020_03_022 elsevier_sciencedirect_doi_10_1016_j_jpdc_2020_03_022 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | August 2020 2020-08-00 |
PublicationDateYYYYMMDD | 2020-08-01 |
PublicationDate_xml | – month: 08 year: 2020 text: August 2020 |
PublicationDecade | 2020 |
PublicationTitle | Journal of parallel and distributed computing |
PublicationYear | 2020 |
Publisher | Elsevier Inc |
Publisher_xml | – name: Elsevier Inc |
References | E. Rashedi, A. Zarezadeh, Noise filtering in ultrasound images using Gravitational Search Algorithm, in: Iranian Conference on Intelligent Systems, ICIS, 2014. Kendrick, Baker, Maamar, Hussain, Buyya, Al-Jumeily (b12) 2018 Mezmaz, Melab, Kessaci, Lee, Talbi, Zomaya, Tuyttens (b17) 2011; 71 Wang, Guo, Guo, Baker, Liu (b25) 2019 Garg, Buyya (b8) 2011 Buyya, Pandey, Vecchiola (b5) 2009 Belguith, Kaaniche, Hammoudeh, Dargahi (b4) 2019 Chaudhary, Kumar (b6) 2014 Golchi, Saraeian, Heydari (b9) 2019; 162 Zavala, Aguirre, Diharce, Rionda (b27) 2008; 1 Al-Maytami, Fan, Hussain, Baker, Liatsis (b1) 2019; 7 Khiyaita, El, Zbakh, Kettani (b15) 2012 C. Gupta, S. Jain, Multilevel fuzzy partition segmentation of satellite images using GSA, in: International Conference on Signal Propagation and Computer Technology, ICSPCT, 2014. Gubbi, Buyya, Marusic, Palaniswami (b10) 2013; 29 Reddy, Khare (b21) 2017; 10 Pacini, Mateos, Garino (b18) 2013; 40 Kotb, Al Ridhawi, Aloqaily, Baker, Jararweh, Tawfik (b16) 2019; 17 Kennedy, Eberhart (b13) 1995; vol. 4 Pandey, Buyya (b19) 2010 Sun, Zhang, Jia, Li, Ji, Wang (b23) 2016; 363 Khatibinia, Khosravi (b14) 2014; 16 Wang, Guo, Guo, Baker, Liu (b24) 2019 Reddy, Khare (b22) 2018; 27 Fan, Wang, Cheng, Li, Gu (b7) 2017 Armbrust, Fox, Griffith, Joseph, Katz, Konwinski, Lee, Patterson, Rabkin, Stoica (b2) 2010; 53 Baker, Asim, Tawfik, Aldawsari, Buyya (b3) 2017; 89 Yu, Buyya, Ramamohanarao (b26) 2008; vol. 146 Buyya (10.1016/j.jpdc.2020.03.022_b5) 2009 Reddy (10.1016/j.jpdc.2020.03.022_b22) 2018; 27 Kotb (10.1016/j.jpdc.2020.03.022_b16) 2019; 17 10.1016/j.jpdc.2020.03.022_b20 Khiyaita (10.1016/j.jpdc.2020.03.022_b15) 2012 Armbrust (10.1016/j.jpdc.2020.03.022_b2) 2010; 53 Belguith (10.1016/j.jpdc.2020.03.022_b4) 2019 Golchi (10.1016/j.jpdc.2020.03.022_b9) 2019; 162 Mezmaz (10.1016/j.jpdc.2020.03.022_b17) 2011; 71 Reddy (10.1016/j.jpdc.2020.03.022_b21) 2017; 10 Pandey (10.1016/j.jpdc.2020.03.022_b19) 2010 Yu (10.1016/j.jpdc.2020.03.022_b26) 2008; vol. 146 Khatibinia (10.1016/j.jpdc.2020.03.022_b14) 2014; 16 Wang (10.1016/j.jpdc.2020.03.022_b24) 2019 Zavala (10.1016/j.jpdc.2020.03.022_b27) 2008; 1 Baker (10.1016/j.jpdc.2020.03.022_b3) 2017; 89 Chaudhary (10.1016/j.jpdc.2020.03.022_b6) 2014 Kendrick (10.1016/j.jpdc.2020.03.022_b12) 2018 Sun (10.1016/j.jpdc.2020.03.022_b23) 2016; 363 Al-Maytami (10.1016/j.jpdc.2020.03.022_b1) 2019; 7 Fan (10.1016/j.jpdc.2020.03.022_b7) 2017 Pacini (10.1016/j.jpdc.2020.03.022_b18) 2013; 40 Kennedy (10.1016/j.jpdc.2020.03.022_b13) 1995; vol. 4 10.1016/j.jpdc.2020.03.022_b11 Gubbi (10.1016/j.jpdc.2020.03.022_b10) 2013; 29 Wang (10.1016/j.jpdc.2020.03.022_b25) 2019 Garg (10.1016/j.jpdc.2020.03.022_b8) 2011 |
References_xml | – volume: vol. 4 start-page: 1942 year: 1995 end-page: 1948 ident: b13 article-title: Particle swarm optimization publication-title: IEEE International Conference on Neural Networks – start-page: 105 year: 2011 end-page: 113 ident: b8 article-title: Network cloudsim: Modelling parallel applications in cloud simulations publication-title: 4th IEEE/ACM International Conference on Utility and Cloud Computing – volume: 1 start-page: 425 year: 2008 end-page: 453 ident: b27 article-title: Constrained optimisation with an improved particle swarm optimisation algorithm publication-title: Int. J. Intell. Comput. Cybern. – volume: 17 start-page: 625 year: 2019 end-page: 650 ident: b16 article-title: Cloud-based multi-agent cooperation for IoT devices using workflow-nets publication-title: J. Grid Comput. – year: 2018 ident: b12 article-title: An efficient multi-cloud service composition using a distributed multiagent-based, memory-driven approach publication-title: IEEE Trans. Sustain. Comput. – volume: 89 start-page: 96 year: 2017 end-page: 108 ident: b3 article-title: An energy-aware service composition algorithm for multiple cloud-based IoT applications publication-title: J. Netw. Comput. Appl. – volume: 40 start-page: 252 year: 2013 end-page: 269 ident: b18 article-title: Distributed job scheduling based on swarm intelligence: A survey publication-title: Comput. Electr. Eng. – volume: 29 start-page: 1645 year: 2013 end-page: 1660 ident: b10 article-title: Internet of Things (IoT): a vision, architectural elements, and future directions publication-title: Future Gener. Comput. Syst. – year: 2019 ident: b24 article-title: CLOSURE: A cloud scientific workflow scheduling algorithm based on attack–defense game model publication-title: Future Gener. Comput. Syst. – reference: E. Rashedi, A. Zarezadeh, Noise filtering in ultrasound images using Gravitational Search Algorithm, in: Iranian Conference on Intelligent Systems, ICIS, 2014. – year: 2017 ident: b7 article-title: An improved multiobjective particle swarm optimization algorithm using minimum distance of point to line publication-title: Shock Vib. – volume: 53 start-page: 50 year: 2010 end-page: 58 ident: b2 article-title: A view of cloud computing publication-title: Commun. ACM – reference: C. Gupta, S. Jain, Multilevel fuzzy partition segmentation of satellite images using GSA, in: International Conference on Signal Propagation and Computer Technology, ICSPCT, 2014. – volume: 162 year: 2019 ident: b9 article-title: A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation publication-title: Comput. Netw. – start-page: 400 year: 2010 end-page: 407 ident: b19 article-title: A particle swarm optimization based heuristic for scheduling workflow applications in cloud computing environments publication-title: 24th IEEE International Conference on Advanced Information Networking and Applications – year: 2019 ident: b25 article-title: CLOSURE: A cloud scientific workflow scheduling algorithm based on attack–defense game model publication-title: Future Gener. Comput. Syst. – volume: vol. 146 start-page: 173 year: 2008 end-page: 214 ident: b26 article-title: Workflow scheduling algorithms for grid computing publication-title: Meta-Heuristics for Scheduling in Distributed Computing Environments – year: 2019 ident: b4 article-title: PROUD: verifiable privacy-preserving outsourced attribute based signcryption supporting access policy update for cloud assisted IoT applications publication-title: Future Gener. Comput. Syst. – volume: 16 start-page: 223 year: 2014 end-page: 233 ident: b14 article-title: A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams publication-title: Appl. Soft Comput. J. – volume: 71 start-page: 1497 year: 2011 end-page: 1508 ident: b17 article-title: A parallel bi-objective hybrid meta heuristic for energy-aware scheduling for cloud computing systems publication-title: J. Parallel Distrib. Comput. – start-page: 24 year: 2009 end-page: 44 ident: b5 article-title: Cloudbus toolkit for market-oriented cloud computing publication-title: CloudCom 09: Proceedings of the 1st International Conference on Cloud Computing, vol. 5931 – volume: 27 start-page: 183 year: 2018 end-page: 202 ident: b22 article-title: Heart disease classification system using optimised fuzzy rule based algorithm publication-title: Int. J. Biomed. Eng. Technol. – start-page: 1 year: 2014 end-page: 6 ident: b6 article-title: An analysis of the load scheduling algorithms in the cloud computing environment: A survey publication-title: IEEE 9th International Conference on Industrial and Information Systems, ICIIS – volume: 363 start-page: 52 year: 2016 end-page: 71 ident: b23 article-title: DMMOGSA: Diversity-enhanced and memory-based multiobjective gravitational search algorithm publication-title: Inform. Sci. – volume: 10 start-page: 18 year: 2017 end-page: 27 ident: b21 article-title: Hybrid firefly-bat optimized fuzzy artificial neural network based classifier for diabetes diagnosis publication-title: Int. J. Intell. Eng. Syst. – volume: 7 start-page: 160916 year: 2019 end-page: 160926 ident: b1 article-title: A task scheduling algorithm with improved makespan based on prediction of tasks computation time algorithm for cloud computing publication-title: IEEE Access – start-page: 106 year: 2012 end-page: 109 ident: b15 article-title: Load balancing cloud computing: State of art publication-title: IEEE National Days of Network Security and Systems – start-page: 24 year: 2009 ident: 10.1016/j.jpdc.2020.03.022_b5 article-title: Cloudbus toolkit for market-oriented cloud computing – issue: 2017 year: 2017 ident: 10.1016/j.jpdc.2020.03.022_b7 article-title: An improved multiobjective particle swarm optimization algorithm using minimum distance of point to line publication-title: Shock Vib. – volume: 363 start-page: 52 year: 2016 ident: 10.1016/j.jpdc.2020.03.022_b23 article-title: DMMOGSA: Diversity-enhanced and memory-based multiobjective gravitational search algorithm publication-title: Inform. Sci. doi: 10.1016/j.ins.2016.05.007 – start-page: 105 year: 2011 ident: 10.1016/j.jpdc.2020.03.022_b8 article-title: Network cloudsim: Modelling parallel applications in cloud simulations – volume: 71 start-page: 1497 issue: 11 year: 2011 ident: 10.1016/j.jpdc.2020.03.022_b17 article-title: A parallel bi-objective hybrid meta heuristic for energy-aware scheduling for cloud computing systems publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2011.04.007 – start-page: 106 year: 2012 ident: 10.1016/j.jpdc.2020.03.022_b15 article-title: Load balancing cloud computing: State of art – volume: vol. 146 start-page: 173 year: 2008 ident: 10.1016/j.jpdc.2020.03.022_b26 article-title: Workflow scheduling algorithms for grid computing – start-page: 1 year: 2014 ident: 10.1016/j.jpdc.2020.03.022_b6 article-title: An analysis of the load scheduling algorithms in the cloud computing environment: A survey – volume: 17 start-page: 625 issue: 4 year: 2019 ident: 10.1016/j.jpdc.2020.03.022_b16 article-title: Cloud-based multi-agent cooperation for IoT devices using workflow-nets publication-title: J. Grid Comput. doi: 10.1007/s10723-019-09485-z – year: 2018 ident: 10.1016/j.jpdc.2020.03.022_b12 article-title: An efficient multi-cloud service composition using a distributed multiagent-based, memory-driven approach publication-title: IEEE Trans. Sustain. Comput. – volume: 40 start-page: 252 year: 2013 ident: 10.1016/j.jpdc.2020.03.022_b18 article-title: Distributed job scheduling based on swarm intelligence: A survey publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2013.11.023 – volume: 29 start-page: 1645 issue: 7 year: 2013 ident: 10.1016/j.jpdc.2020.03.022_b10 article-title: Internet of Things (IoT): a vision, architectural elements, and future directions publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2013.01.010 – ident: 10.1016/j.jpdc.2020.03.022_b11 doi: 10.1109/ICSPCT.2014.6884903 – volume: 1 start-page: 425 issue: 3 year: 2008 ident: 10.1016/j.jpdc.2020.03.022_b27 article-title: Constrained optimisation with an improved particle swarm optimisation algorithm publication-title: Int. J. Intell. Comput. Cybern. doi: 10.1108/17563780810893482 – volume: 27 start-page: 183 issue: 3 year: 2018 ident: 10.1016/j.jpdc.2020.03.022_b22 article-title: Heart disease classification system using optimised fuzzy rule based algorithm publication-title: Int. J. Biomed. Eng. Technol. doi: 10.1504/IJBET.2018.094122 – volume: 7 start-page: 160916 year: 2019 ident: 10.1016/j.jpdc.2020.03.022_b1 article-title: A task scheduling algorithm with improved makespan based on prediction of tasks computation time algorithm for cloud computing publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2948704 – volume: 162 year: 2019 ident: 10.1016/j.jpdc.2020.03.022_b9 article-title: A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation publication-title: Comput. Netw. doi: 10.1016/j.comnet.2019.106860 – year: 2019 ident: 10.1016/j.jpdc.2020.03.022_b4 article-title: PROUD: verifiable privacy-preserving outsourced attribute based signcryption supporting access policy update for cloud assisted IoT applications publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.11.012 – volume: 53 start-page: 50 issue: 4 year: 2010 ident: 10.1016/j.jpdc.2020.03.022_b2 article-title: A view of cloud computing publication-title: Commun. ACM doi: 10.1145/1721654.1721672 – volume: 89 start-page: 96 year: 2017 ident: 10.1016/j.jpdc.2020.03.022_b3 article-title: An energy-aware service composition algorithm for multiple cloud-based IoT applications publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2017.03.008 – volume: 16 start-page: 223 year: 2014 ident: 10.1016/j.jpdc.2020.03.022_b14 article-title: A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2013.12.008 – year: 2019 ident: 10.1016/j.jpdc.2020.03.022_b25 article-title: CLOSURE: A cloud scientific workflow scheduling algorithm based on attack–defense game model publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.11.003 – year: 2019 ident: 10.1016/j.jpdc.2020.03.022_b24 article-title: CLOSURE: A cloud scientific workflow scheduling algorithm based on attack–defense game model publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.11.003 – start-page: 400 year: 2010 ident: 10.1016/j.jpdc.2020.03.022_b19 article-title: A particle swarm optimization based heuristic for scheduling workflow applications in cloud computing environments – volume: vol. 4 start-page: 1942 year: 1995 ident: 10.1016/j.jpdc.2020.03.022_b13 article-title: Particle swarm optimization – ident: 10.1016/j.jpdc.2020.03.022_b20 doi: 10.1109/IranianCIS.2014.6802559 – volume: 10 start-page: 18 issue: 4 year: 2017 ident: 10.1016/j.jpdc.2020.03.022_b21 article-title: Hybrid firefly-bat optimized fuzzy artificial neural network based classifier for diabetes diagnosis publication-title: Int. J. Intell. Eng. Syst. |
SSID | ssj0011578 |
Score | 2.6251433 |
Snippet | Load balancing, in Cloud Computing (CC) environment, is defined as the method of splitting workloads and computing properties. It enables the enterprises to... |
SourceID | crossref elsevier |
SourceType | Enrichment Source Index Database Publisher |
StartPage | 36 |
SubjectTerms | Cloud computing Firefly IMPSO Load balancing Task scheduling |
Title | Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments |
URI | https://dx.doi.org/10.1016/j.jpdc.2020.03.022 |
Volume | 142 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEF5KvXjxLdYXc_AmsckmaZKjFCUqtkIVegv71JY0KaUiXvwr_lVnk40oSA8es2TD7s7sfDNh5htCziK0-4GmzJEYC2CAwqnDg4jivdJKIFwrt-oScT_opU_B7Tgct0i_qYUxaZXW9tc2vbLWdqRrT7M7n0y6IwN-kY_44xrWqNAU8QVBZLT84uM7zcNwycQNFad52xbO1Dle07k0NIbUrYhOKf0bnH4AzvUW2bCeIlzWi9kmLVXskM2mCwPYS7lLPtN3U3Vl6ymh1KBxSzp_B1ZIqH8aKAlVpa0z5NPawsGD3SWM3thiBkM0HbPmGyx_LheT5csM0KUFVZUHgqrIJhCjIC-ZBG5yIgUCH0wK6Oflq4R6bWboZ_ncHnm6vnrsp45tu-AIPMilI-JYMIwSudB4haSn3FD4mic-0xRHmFIeE4nvSapoxP0kSFyuQkUrbwrdJX-ftIuyUAcE0BpIjL5dmoQiQGWJeyJgsdKhiuJeInsd4jXnnQnLSW5aY-RZk3w2zYyMMiOjzPUzlFGHnH_PmdeMHCvfDhsxZr_0KkPIWDHv8J_zjsi6eapTBI9Je7l4VSfotiz5aaWXp2Tt8uYuHXwBfo7vew |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDI54HODCG_HGB26orEvbtT2iCTRgA6SBxC3KEzZ17TQNIS78Ff4qTpuiISEOXNOmSuLYn13Znwk5idHuh4ZyT2EsgAGKoJ4IY4p6ZbREuNZ-2SWid9vqPIbXT9HTHGnXtTA2rdLZ_sqml9bajTTcaTbGg0Gjb8EvDhB_fMsaFaXzZDFE9bVtDM4-vvM8LJlMUnNx2tdd5UyV5DUcK8tjSP2S6ZTS39FpBnEu18iKcxXhvFrNOpnT-QZZrdswgNPKTfLZebdlV66gEgoDBvdksnfguYLqr4FWUJbaendiWJk4uHfbhP4bn4zgDm3HqP4Gz56LyWD6MgL0aUGX9YGgS7YJBCnICq5A2KRIicgHgxzaWfGqoFqbHZqtn9sij5cXD-2O5_oueBJPcurJJJEcw0QhDeqQamo_koERacANxRGudZPLNGgqqmksgjRMfaEjTUt3Cv2lYJss5EWudwigOVAYfvs0jWSItyVpyZAn2kQ6Tlqpau2SZn3eTDpSctsbI2N19tmQWRkxKyPmBwxltEtOv-eMK0qOP9-OajGyHxeLIWb8MW_vn_OOyVLnoddl3avbm32ybJ9U-YIHZGE6edWH6MNMxVF5R78A-vXxCQ |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Hybridization+of+firefly+and+Improved+Multi-Objective+Particle+Swarm+Optimization+algorithm+for+energy+efficient+load+balancing+in+Cloud+Computing+environments&rft.jtitle=Journal+of+parallel+and+distributed+computing&rft.au=Devaraj%2C+A.+Francis+Saviour&rft.au=Elhoseny%2C+Mohamed&rft.au=Dhanasekaran%2C+S.&rft.au=Lydia%2C+E.+Laxmi&rft.date=2020-08-01&rft.issn=0743-7315&rft.volume=142&rft.spage=36&rft.epage=45&rft_id=info:doi/10.1016%2Fj.jpdc.2020.03.022&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jpdc_2020_03_022 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0743-7315&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0743-7315&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0743-7315&client=summon |