MapReduce is a widespread programming model used for overcoming processing limits of current hardware resources. In the MapReduce paradigm, large amount of data distributed into multiple parts and each part processed on different processing units and finally the results of all units combined to obtain the ultimate result. Scheduling is an important aspect that affects overall processing quality, hence finding suitable algorithm based on properties of jobs is essential to acquire maximum performance. The basic FIFO algorithm for job scheduling does not meet maximum efficiency. Therefore, many of new schedulers proposed so far to improve quality metrics of the MapReduce system. The scheduling methods can be designed for job scheduling, map or reduce task scheduling or both. In this paper, we select high quality studies that concern the MapReduce scheduling problem and then classify them based on their main concerning quality measure and subsequently review selected studies. Finally, we discuss research trends and provide a roadmap of the scheduling problem for researchers.
ZhangQChengLBoutabaR. Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications2010; 1(1): 7-18.
2.
MellPGranceT, et al. The NIST definition of cloud computing 2011; Available from: http://facultywinthrop.edu/domanm/csci411/Handouts/NIST.pdf.
3.
ArmbrustMFoxAGriffithRJosephADKatzRKonwinskiA, et al. A view of cloud computing. Communications of the ACM2010; 53(4): 50-58.
4.
HayesB. Cloud computing. Communications of the ACM2008; 51(7): 9-11.
5.
ZikopoulosPEatonC, et al. Understanding big data: Analytics for enterprise class hadoop and streaming data [Internet]. McGraw-Hill Osborne Media; 2011. Available from: http://dl.acm.org/citation.cfm?id=2132803.
6.
ShvachkoKKuangHRadiaSChanslerR. The hadoop distributed file system. In: Mass storage systems and technologies (MSST), 2010 IEEE 26th symposium on [Internet]. IEEE; 2010. p. 1-10. Available from: http://ieeexplore.ieee.org/abstract/document/5496972/.
7.
DeanJGhemawatS. MapReduce: Simplified data processing on large clusters. Communications of the ACM2008; 51(1): 107-113.
8.
DeanJGhemawatS. MapReduce: A flexible data processing tool. Communications of the ACM2010; 53(1): 72-77.
9.
ZahariaMBorthakurDSen SarmaJElmeleegyKShenkerSStoicaI. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In: Proceedings of the 5th European conference on Computer systems [Internet]. ACM; 2010. p. 265-278. Available from: http://dl.acm.org/citation.cfm?id=1755940.
10.
BorthakurD. The hadoop distributed file system: Architecture and design. Hadoop Project Website2007; 11(2007): 21.
11.
PoloJCastilloCCarreraDBecerraYWhalleyISteinderM, et al. Resource-aware adaptive scheduling for mapreduce clusters. In: ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing [Internet]. Springer; 2011. p. 187-207. Available from: http://link.springer.com/chapter/10.1007/978-3-642-25821-3_10.
12.
WhiteT. Hadoop: The definitive guide. O’Reilly Media, Inc.; 2012.
13.
LamC. Hadoop in action [Internet]. Manning Publications Co.; 2010. Available from: http://dl.acm.org/citation.cfm?id=1965594.
14.
VavilapalliVKMurthyACDouglasCAgarwalSKonarMEvansR, et al. Apache hadoop yarn: Yet another resource negotiator. In: Proceedings of the 4th annual Symposium on Cloud Computing [Internet]. ACM; 2013. p. 5. Available from: http://dl.acm.org/citation.cfm?id=2523633.
15.
IdrisMHussainSAliMAbdulaliASiddiqiMHKangBH, et al. Context-aware scheduling in MapReduce: A compact review. Concurrency and Computation: Practice and Experience2015; 27(17): 5332-5349.
16.
BudgenDBreretonP. Performing systematic literature reviews in software engineering. In: Proceedings of the 28th International Conference on Software engineering. ACM; 2006. p. 1051-1052.
17.
ZhangXFengYFengSFanJMingZ. An effective data locality aware task scheduling method for MapReduce framework in heterogeneous environments. In: Cloud and Service Computing (CSC), 2011 International Conference on. IEEE; 2011. p. 235-242.
18.
PalanisamyBSinghALiuLJainB. Purlieus: Locality-aware resource allocation for MapReduce in a cloud. In: High Performance Computing, Networking, Storage and Analysis (SC), 2011 International Conference for. IEEE; 2011. p. 1-11.
19.
NguyenPSimonTHalemMChapmanDLeQ. A hybrid scheduling algorithm for data intensive workloads in a mapreduce environment. In: Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing. IEEE Computer Society; 2012. p. 161-167.
20.
ChenT-YWeiH-WWeiM-FChenY-JHsuTShihW-K. LaSA: A locality-aware scheduling algorithm for Hadoop-MapReduce resource assignment. In: Collaboration Technologies and Systems (CTS), 2013 International Conference on. IEEE; 2013. p. 342-346.
21.
AnjosJCCarreraIKolbergWTibolaALArantesLBGeyerCR. MRA++: Scheduling and data placement on MapReduce for heterogeneous environments. Future Generation Computer Systems2015; 42: 22-35.
22.
LiXJiangTRuizR. Heuristics for periodical batch job scheduling in a mapreduce computing framework. Information Sciences2016; 326: 119-133.
23.
XieQPundirMLuYAbadCLCampbellRH. Pandas: Robust locality-aware scheduling with stochastic delay optimality. IEEE/ACM Transactions on Networking (TON)2017; 25(2): 662-675.
24.
WangWZhuKYingLTanJZhangL. Maptask scheduling in mapreduce with data locality: Throughput and heavy-traffic optimality. IEEE/ACM Transactions on Networking (TON)2016; 24(1): 190-203.
25.
XieQYekkehkhanyALuY. Scheduling with multi-level data locality: Throughput and heavy-traffic optimality. In: INFOCOM 2016 – The 35th Annual IEEE International Conference on Computer Communications, IEEE. IEEE; 2016. p. 1-9.
26.
YekkehkhanyAHojjatiAHajiesmailiMH. GB-PANDAS: Throughput and heavy-traffic optimality analysis for affinity scheduling. ACM SIGMETRICS Performance Evaluation Review2018; 45(2): 2-14.
27.
JinJLuoJSongADongFXiongR. Bar: An efficient data locality driven task scheduling algorithm for cloud computing. In: Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE Computer Society; 2011. p. 295-304.
28.
ShenHSarkerAYuLDengF. Probabilistic network-aware task placement for mapreduce scheduling. In: Cluster Computing (CLUSTER), 2016 IEEE International Conference on. IEEE; 2016. p. 241-250.
29.
SunMZhuangHLiCLuKZhouX. Scheduling algorithm based on prefetching in MapReduce clusters. Applied Soft Computing2016; 38: 1109-1118.
30.
LiPGuoSMiyazakiTLiaoXJinHZomayaAY, et al. Traffic-aware geo-distributed big data analytics with predictable job completion time. IEEE Transactions on Parallel and Distributed Systems2017; 28(6): 1785-1796.
31.
DaiXBensaouB. Scheduling for response time in Hadoop MapReduce. In: Communications (ICC), 2016 IEEE International Conference on. IEEE; 2016. p. 1-6.
32.
ZhangXZhongZFengSTuBFanJ. Improving data locality of mapreduce by scheduling in homogeneous computing environments. In: Parallel and Distributed Processing with Applications (ISPA), 2011 IEEE 9th International Symposium on. IEEE; 2011. p. 120-126.
33.
KondikoppaPChiuC-HCuiCXueLParkS-J. Network-aware scheduling of mapreduce framework ondistributed clusters over high speed networks. In: Proceedings of the 2012 workshop on Cloud services, federation, and the 8th open cirrus summit. ACM; 2012. p. 39-44.
34.
JinSYangSJiaY. Optimization of task assignment strategy for map-reduce. In: Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on. IEEE; 2012. p. 57-61.
35.
HammoudMSakrMF. Locality-aware reduce task scheduling for MapReduce. In: Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on. IEEE; 2011. p. 570-576.
36.
HammoudMRehmanMSSakrMF. Center-of-gravity reduce task scheduling to lower mapreduce network traffic. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. IEEE; 2012. p. 49-58.
37.
IbrahimSJinHLuLHeBAntoniuGWuS. Maestro: Replica-aware map scheduling for mapreduce. In: Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on [Internet]. IEEE; 2012. p. 435-442. Available from: http://ieeexplore.ieee.org/abstract/document/6217451/.
38.
TianCZhouHHeYZhaL. A dynamic mapreduce scheduler for heterogeneous workloads. In: Grid and Cooperative Computing, 2009 GCC’09 Eighth International Conference on. IEEE; 2009. p. 218-224.
39.
YangS-JChenY-R. Design adaptive task allocation scheduler to improve MapReduce performance in heterogeneous clouds. Journal of Network and Computer Applications2015; 57: 61-70.
40.
ZahariaMKonwinskiAJosephADKatzRHStoicaI. Improving MapReduce performance in heterogeneous environments. In: Osdi [Internet]2008. p. 7. Available from: http://static.usenix.org/legacy/events/osdi08/tech/full_papers/zaharia/zaharia_html/.
41.
ChenQZhangDGuoMDengQGuoS. Samr: A self-adaptive mapreduce scheduling algorithm in heterogeneous environment. In: Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on [Internet]. IEEE; 2010. p. 2736-2743. Available from: http://ieeexplore.ieee.org/abstract/document/5578538/.
42.
SunXHeCLuY. ESAMR: An enhanced self-adaptive mapreduce scheduling algorithm. In: Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on. IEEE; 2012. p. 148-155.
43.
YildizOIbrahimSAntoniuG. Enabling fast failure recovery in shared Hadoop clusters: Towards failure-aware scheduling. Future Generation Computer Systems2017; 74: 208-219.
44.
SoualhiaMKhomhFTaharS. A Dynamic and Failure-aware Task Scheduling Framework for Hadoop. IEEE Transactions on Cloud Computing2018;
45.
LiuZZhangQAhmedRBoutabaRLiuYGongZ. Dynamic resource allocation for MapReduce with partitioning skew. IEEE Transactions on Computers2016; 65(11): 3304-3317.
46.
LiJLiuYPanJZhangPChenWWangL. Map-Balance-Reduce: An improved parallel programming model for load balancing of MapReduce. Future Generation Computer Systems [Internet]2017 Mar 18. [cited 2018 Jun 11]; Available from: http://www.sciencedirect.com/science/article/pii/S0167739X17303710.
TangZJiangLZhouJLiKLiK. A self-adaptive scheduling algorithm for reduce start time. Future Generation Computer Systems2015; 43: 51-60.
49.
NanduriRMaheshwariNReddyrajaAVarmaV. Job aware scheduling algorithm for mapreduce framework. In: Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on [Internet]. IEEE; 2011. p. 724-729. Available from: http://ieeexplore.ieeeorg/abstract/document/6133221/.
50.
KumarKAKonishettyVKVorugantiKRaoGV. CASH: Context aware scheduler for Hadoop. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics. ACM; 2012. p. 52-61.
51.
AhmadFChakradharSTRaghunathanAVijaykumarTN. Tarazu: Optimizing mapreduce on heterogeneous clusters. In: ACM SIGARCH Computer Architecture News. ACM; 2012. p. 61-74.
52.
VermaACherkasovaLCampbellRH. Orchestrating an ensemble of MapReduce jobs for minimizing their makespan. IEEE Transactions on dependable and secure Computing2013; 10(5): 314-327.
53.
GeYWeiG. GA-based task scheduler for the cloud computing systems. In: Web Information Systems and Mining (WISM), 2010 International Conference on. IEEE; 2010. p. 181-186.
54.
ChangHKodialamMKompellaRRLakshmanTVLeeMMukherjeeS. Scheduling in mapreduce-like systems for fast completion time. In: INFOCOM, 2011 Proceedings IEEE. IEEE; 2011. p. 3074-3082.
55.
ZhuYJiangYWuWDingLTeredesaiALiD, et al. Minimizing makespan and total completion time in mapreduce-like systems. In: INFOCOM, 2014 Proceedings IEEE. IEEE; 2014. p. 2166-2174.
56.
PruhsKSgallJTorngE. Online Scheduling [Internet]. 2004. Available from: http://www.cse.yorku.ca/∼jeff/research/schedule/leungfinal.pdf.
57.
ChenCXuYZhuYSunC. Online MapReduce scheduling problem of minimizing the makespan. Journal of Combinatorial Optimization2017; 33(2): 590-608.
58.
ChenBVestjensAP. Scheduling on identical machines: How good is LPT in an on-line setting? Operations Research Letters1997; 21(4): 165-169.
59.
JiangYZhuYWuWLiD. Makespan minimization for MapReduce systems with different servers. Future Generation Computer Systems2017; 67: 13-21.
60.
YaoYTaiJShengBMiN. LsPS: A job size-based scheduler for efficient task assignments in Hadoop. IEEE Transactions on Cloud Computing2015; 3(4): 411-424.
61.
YouH-HYangC-CHuangJ-L. A load-aware scheduler for MapReduce framework in heterogeneous cloud environments. In: Proceedings of the 2011 ACM Symposium on Applied Computing. ACM; 2011. p. 127-132.
62.
MaoYQiHPingPLiX. FiGMR: A fine-grained MapReduce scheduler in the heterogeneous cloud. In: Information and Automation (ICIA), 2016 IEEE International Conference on [Internet]. IEEE; 2016. p. 1956-1963. Available from: http://ieeexplore.ieee.org/abstract/document/7832139/.
63.
ChenFKodialamMLakshmanTV. Joint scheduling of processing and shuffle phases in mapreduce systems. In: INFOCOM, 2012 Proceedings IEEE. IEEE; 2012. p. 1143-1151.
64.
LinMZhangLWiermanATanJ. Joint optimization of overlapping phases in MapReduce. Performance Evaluation2013; 70(10): 720-735.
65.
FotakisDMilisIPapadigenopoulosOVassalosVZoisG. Scheduling MapReduce Jobs Under Multi-round Precedences. In: European Conference on Parallel Processing [Internet]. Springer; 2016. p. 209-222. Available from: http://linkspringer.com/chapter/10.1007/978-3-319-43659-3_16.
66.
FotakisDMilisIPapadigenopoulosOZampetakisEZoisG. Scheduling mapreduce jobs and data shuffle on unrelated processors. In: International Symposium on Experimental Algorithms [Internet]. Springer; 2015. p. 137-150. Available from: http://link.springer.com/chapter/10.1007/978-3-319-20086-6_11.
67.
MoseleyBDasguptaAKumarRSarlAssT. On scheduling in map-reduce and flow-shops. In: Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures [Internet]. ACM; 2011. p. 289-298. Available from: http://dl.acm.org/citation.cfm?id=1989540.
68.
RasooliADownDG. An adaptive scheduling algorithm for dynamic heterogeneous Hadoop systems. In: Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research. IBM Corp.; 2011. p. 30-44.
69.
TangSLeeB-SHeB. Dynamic job ordering and slot configurations for MapReduce workloads. IEEE Transactions on Services Computing2016; 9(1): 4-17.
70.
ChengDRaoJGuoYJiangCZhouX. Improving performance of heterogeneous mapreduce clusters with adaptive task tuning. IEEE Transactions on Parallel and Distributed Systems2017; 28(3): 774-786.
71.
ShiYZhangKCuiLLiuLZhengYZhangS, et al. MapReduce short jobs optimization based on resource reuse. Microprocessors and Microsystems2016; 47: 178-187.
72.
GhitBEpemaD. Tyrex: Size-based resource allocation in mapreduce frameworks. In: Cluster, Cloud and Grid Computing (CCGrid), 2016 16th IEEE/ACM International Symposium on. IEEE; 2016. p. 11-20.
73.
ZahariaMBorthakurDSarmaJSElmeleegyKShenkerSStoicaI. Job scheduling for multi-user mapreduce clusters. Technical Report UCB/EECS-2009-55, EECS Department, University of California, Berkeley; 2009.
74.
SandholmTLaiK. Dynamic proportional share scheduling in hadoop. In: Job scheduling strategies for parallel processing [Internet]. Springer; 2010. p. 110-131. Available from: http://link.springer.com/content/pdf/10.1007/978-3-642-16505-4.pdf#page=119.
75.
IsardMPrabhakaranVCurreyJWiederUTalwarKGoldbergA. Quincy: fair scheduling for distributed computing clusters. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles. ACM; 2009. p. 261-276.
76.
PastorelliMCarraDDell’AmicoMMichiardiP. HFSP: bringing size-based scheduling to Hadoop. IEEE Transactions on Cloud Computing2017; 5(1): 43-56.
PoloJCarreraDBecerraYSteinderMWhalleyI. Performance-driven task co-scheduling for mapreduce environments. In: Network Operations and Management Symposium (NOMS), 2010 IEEE [Internet]. IEEE; 2010. p. 373-380. Available from: http://ieeexplore.ieee.org/abstract/document/5488494/.
79.
RasooliADownDG. A hybrid scheduling approach for scalable heterogeneous hadoop systems. In: High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion. IEEE; 2012. p. 1284-1291.
80.
YeBDongXZhengPZhuZLiuQWangZ. A delay scheduling algorithm based on history time in heterogeneous environments. In: ChinaGrid Annual Conference (ChinaGrid), 2013 8th. IEEE; 2013. p. 86-91.
81.
TangSLeeB-SHeB. Dynamicmr: A dynamic slot allocation optimization framework for mapreduce clusters. IEEE Transactions on Cloud Computing2014; 2(3): 333-347.
82.
WangJYaoYMaoYShengBMiN. Fresh: Fair and efficient slot configuration and scheduling for hadoop clusters. In: Cloud Computing (CLOUD), 2014 IEEE 7th International Conference on. IEEE; 2014. p. 761-768.
83.
KcKAnyanwuK. Scheduling hadoop jobs to meet deadlines. In: Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on [Internet]. IEEE; 2010. p. 388-392. Available from: http://ieeexploreieee.org/abstract/document/5708475/.
84.
VermaACherkasovaLCampbellRH. ARIA: Automatic resource inference and allocation for mapreduce environments. In: Proceedings of the 8th ACM international conference on Autonomic computing. ACM; 2011. p. 235-244.
85.
PoloJBecerraYCarreraDSteinderMWhalleyITorresJ, et al. Deadline-based mapreduce workload management. IEEE Transactions on Network and Service Management2013; 10(2): 231-244.
86.
LiuLZhouYLiuMXuGChenXFanD, et al. Preemptive hadoop jobs scheduling under a deadline. In: Semantics, Knowledge and Grids (SKG), 2012 Eighth International Conference on. IEEE; 2012. p. 72-79.
87.
ChenC-HLinJ-WKuoS-Y. MapReduce scheduling for deadline-constrained jobs in heterogeneous cloud computing systems. IEEE Transactions on Cloud Computing2015.
88.
LimNMajumdarSAshwood-SmithP. MRCP-RM: A technique for resource allocation and scheduling of MapReduce jobs with deadlines. IEEE Transactions on Parallel and Distributed Systems2017; 28(5): 1375-1389.
89.
DongXWangYLiaoH. Scheduling mixed real-time and non-real-time applications in mapreduce environment. In: Parallel and Distributed Systems (ICPADS), 2011 IEEE 17th International Conference on. IEEE; 2011. p. 9-16.
90.
LiSHuSWangSSuLAbdelzaherTGuptaI, et al. Woha: Deadline-aware map-reduce workflow scheduling framework over hadoop clusters. In: Distributed Computing Systems (ICDCS), 2014 IEEE 34th International Conference on. IEEE; 2014. p. 93-103.
91.
TengFMagoulèsFYuLLiT. A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud. J Supercomput [Internet]2014 Aug [cited 2018 May 25]; 69(2): 739-765. Available from: http://dx.doi.org/10.1007/s11227-014-1115-z.
92.
KaoY-CChenY-S. Data-locality-aware mapreduce real-time scheduling framework. Journal of Systems and Software2016; 112: 65-77.
93.
YaoYWangJShengBTanCCMiN. Self-adjusting slot configurations for homogeneous and heterogeneous hadoop clusters. IEEE Transactions on Cloud Computing2017; 5(2): 344-357.
94.
SharmaBPrabhakarRLimS-HKandemirMTDasCR. Mrorchestrator: A fine-grained resource orchestration framework for mapreduce clusters. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. IEEE; 2012. p. 1-8.
95.
LiSHuSAbdelzaherT. The packing server for real-time scheduling of mapreduce workflows. In: Real-Time and Embedded Technology and Applications Symposium (RTAS), 2015 IEEE. IEEE; 2015. p. 51-62.
96.
ZhangQZhaniMFYangYBoutabaRWongB. PRISM: fine-grained resource-aware scheduling for MapReduce. IEEE transactions on Cloud Computing2015; 3(2): 182-194.
97.
HsiehS-YChenC-TChenC-HYenT-HHsiaoH-CBuyyaR. Novel scheduling algorithms for efficient deployment of MapReduce applications in heterogeneous computing environments. IEEE Transactions on Cloud Computing [Internet]2016; Available from: http://ieeexplore.ieee.org/abstract/document/7450666/computer.org/tcc.
98.
GuoZFoxGZhouMRuanY. Improving resource utilization in mapreduce. In: Cluster Computing (CLUSTER), 2012 IEEE International Conference on. IEEE; 2012. p. 402-410.
99.
TanJMengXZhangL. Coupling task progress for mapreduce resource-aware scheduling. In: INFOCOM, 2013 Proceedings IEEE. IEEE; 2013. p. 1618-1626.
100.
MashayekhyLNejadMMGrosuDZhangQShiW. Energy-aware scheduling of mapreduce jobs for big data applications. IEEE transactions on Parallel and Distributed Systems2015; 26(10): 2720-2733.
101.
WirtzTGeR. Improving mapreduce energy efficiency for computation intensive workloads. In: Green Computing Conference and Workshops (IGCC), 2011 International. IEEE; 2011. p. 1-8.
102.
WangXWangYCuiY. A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing. Future Generation Computer Systems2014; 36: 91-101.
103.
ZhangQLiH. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation2007; 11(6): 712-731.
104.
LiYZhangHKimKH. A power-aware scheduling of mapreduce applications in the cloud. In: Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on. IEEE; 2011. p. 613-620.
105.
MashayekhyLNejadMMGrosuDLuDShiW. Energy-aware scheduling of MapReduce jobs. In: Big Data (BigData Congress), 2014 IEEE International Congress on. IEEE; 2014. p. 32-39.
106.
GregoryAMajumdarS. Energy aware resource management for MapReduce jobs with service level agreements in cloud data centers. In: Computer and Information Technology (CIT), 2016 IEEE International Conference on. IEEE; 2016. p. 568-577.
107.
ShuTWuCQ. Energy-efficient dynamic scheduling of deadline-constrained MapReduce workflows. In: e-Science (e-Science), 2017 IEEE 13th International Conference on. IEEE; 2017. p. 393-402.
108.
ZengXGargSKWenZStrazdinsPZomayaAYRanjanR. Cost efficient scheduling of MapReduce applications on public clouds. Journal of Computational Science2017;
109.
WangYShiW. Budget-driven scheduling algorithms for batches of MapReduce jobs in heterogeneous clouds. IEEE Transactions on Cloud Computing2014; 2(3): 306-319.
110.
PalanisamyBSinghALiuL. Cost-effective resource provisioning for mapreduce in a cloud. IEEE Transactions on Parallel and Distributed Systems2015; 26(5): 1265-1279.
111.
ZacheilasNKalogerakiV. Chess: Cost-effective scheduling across multiple heterogeneous mapreduce clusters. In: Autonomic Computing (ICAC), 2016 IEEE International Conference on. IEEE; 2016. p. 65-74.
112.
HashemIATAnuarNBMarjaniMGaniASangaiahAKSakariyahAK. Multi-objective scheduling of MapReduce jobs in big data processing. Multimedia Tools and Applications2018; 77(8): 9979-9994.
113.
HammoudSLiMLiuYAlhamNKLiuZ. MRSim: A discrete event based MapReduce simulator. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery 2010. p. 2993-7.
114.
LiuYLiMAlhamNKHammoudS. HSim: A MapReduce simulator in enabling Cloud Computing. Future Generation Computer Systems [Internet]2013 Jan 1 [cited 2018 Jun 18]; 29(1): 300-8. Available from: http://www.sciencedirect.com/science/article/pii/S0167739X11000884.
115.
AhmadFLeeSThottethodiMVijaykumarTN. Puma: Purdue mapreduce benchmarks suite 2012; Available from: http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1438&context=ecetr.
116.
HuangSHuangJDaiJXieTHuangB. The HiBench benchmark suite: Characterization of the MapReduce-based data analysis. In: Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on [Internet]. IEEE; 2010. p. 41-51. Available from: http://ieeexplore.ieee.org/abstract/document/5452747/.
117.
ChenYGanapathiAGriffithRKatzR. The case for evaluating mapreduce performance using workload suites. In: Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 2011 IEEE 19th International Symposium on [Internet]. IEEE; 2011. p. 390-399. Available from: http://ieeexplore.ieee.org/abstract/document/6005383/.
