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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2016, Vol. 10 Issue (3) : 447-461    https://doi.org/10.1007/s11704-015-4207-x
REVIEW ARTICLE
A survey of cloud resource management for complex engineering applications
Haibao CHEN1,4,Song WU1,*(),Hai JIN1,Wenguang CHEN2,Jidong ZHAI2,Yingwei LUO3,Xiaolin WANG3
1. Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology,Wuhan 430074, China
2. Institute of High Performance Computing, Tsinghua University, Beijing 100084, China
3. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
4. School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
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Abstract

Traditionally, complex engineering applications (CEAs), which consist of numerous components (software) and require a large amount of computing resources, usually run in dedicated clusters or high performance computing (HPC) centers. Nowadays, Cloud computing system with the ability of providing massive computing resources and customizable execution environment is becoming an attractive option for CEAs. As a new type on Cloud applications, CEA also brings the challenges of dealing with Cloud resources. In this paper, we provide a comprehensive survey of Cloud resource management research for CEAs. The survey puts forward two important questions: 1) what are the main challenges for CEAs to run in Clouds? and 2) what are the prior research topics addressing these challenges? We summarize and highlight the main challenges and prior research topics. Our work can be probably helpful to those scientists and engineers who are interested in running CEAs in Cloud environment.

Keywords Cloud computing      complex engineering application      resource management      virtualization     
Corresponding Author(s): Song WU   
Just Accepted Date: 07 September 2015   Online First Date: 06 April 2016    Issue Date: 16 May 2016
 Cite this article:   
Haibao CHEN,Song WU,Hai JIN, et al. A survey of cloud resource management for complex engineering applications[J]. Front. Comput. Sci., 2016, 10(3): 447-461.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4207-x
https://academic.hep.com.cn/fcs/EN/Y2016/V10/I3/447
1 Crago S, Dunn K, Eads P, Hochstein L, Kang D I, Kang M, Modium D, Singh K, Suh J, Walters J P. Heterogeneous cloud computing. In: Proceedings of 2011 IEEE International Conference on Cluster Computing (CLUSTER). 2011, 378–385
2 Arian E. On the coupling of aerodynamic and structural design. Journal of Computational Physics, 1997, 135(1): 83–96
3 Wang C, Xu L. Parameter mapping and data transformation for engineering application integration. Information Systems Frontiers, 2008, 10(5): 589–600
4 Ong M, Thompson H. Challenges for wireless sensing in complex engineering applications. In: Proceedings of the 37th Annual Conference on IEEE Industrial Electronics Society (IECON). 2011, 2106–2111
5 Bichon B, Eldred M, Swiler L, Mahadevan S, McFarland J. Multimodal reliability assessment for complex engineering applications using efficient global optimization. In: Proceedings of the 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA-2007-1946. 2007, 3029–3040
6 Chacón Rebollo T, Gómez Mármol M, Restelli M. Numerical analysis of penalty stabilized finite element discretizations of evolution navier–stokes equations. Journal of Scientific Computing, 2015, 63(3): 885–912
7 Campana E F, Liuzzi G, Lucidi S, Peri D, Piccialli V, Pinto A. New global optimization methods for ship design problems. Optimization and Engineering, 2009, 10(4): 533–555
8 Jin C, Wang Y, Zhang W, Lin Y. Study on semi-finished ship structural components assembly sequence optimization. In: Proceedings of the 6th International Conference on Natural Computation (ICNC). 2010, 2706–2709
9 Kwon S, Kim B C, Mun D, Han S. Simplification of feature-based 3D CAD assembly data of ship and offshore equipment using quantitative evaluation metrics. Computer-Aided Design, 2015, 59: 140–154
10 Palankar M R, Iamnitchi A, Ripeanu M, Garfinkel S. Amazon S3 for science grids: a viable solution? In: Proceedings of the 2008 International Workshop on Data-aware Distributed Computing. 2008, 55–64
11 Hazelhurst S. Scientific computing using virtual high-performance computing: a case study using the amazon elastic computing cloud. In: Proceedings of the 2008 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries: Riding the Wave of Technology. 2008, 94–103
12 Vöckler J S, Juve G, Deelman E, Rynge M, Berriman B. Experiences using cloud computing for a scientific workflow application. In: Proceedings of the 2nd International Workshop on Scientific Cloud Computing. 2011, 15–24
13 Juve G, Deelman E, Vahi K, Mehta G, Berriman B, Berman B P, Maechling P. Scientific workflow applications on amazon EC2. In: Proceedings of the 5th IEEE International Conference on E-ScienceWorkshops. 2009, 59–66
14 Rehr J J, Vila F D, Gardner J P, Svec L, Prange M. Scientific computing in the cloud. Computing in Science & Engineering, 2010, 12(3): 34–43
15 Lin G, Han B, Yin J, Gorton I. Exploring cloud computing for largescale scientific applications. In: Proceedings of the 9th IEEE World Congress on Services (SERVICES). 2013, 37–43
16 Ramakrishnan L, Zbiegel P T, Campbell S, Bradshaw R, Canon R S, Coghlan S, Sakrejda I, Desai N, Declerck T, Liu A. Magellan: experiences from a science cloud. In: Proceedings of the 2nd International Workshop on Scientific Cloud Computing. 2011, 49–58
17 Georgescu S, Chow P. GPU accelerated CAE using open solvers and the cloud. ACMSIGARCH Computer Architecture News, 2011, 39(4): 14–19
18 Zhai Y, Liu M, Zhai J, Ma X, Chen W. Cloud versus in-house cluster: evaluating amazon cluster compute instances for running MPI applications. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC). 2011, 11
19 Janapa Reddi V, Lee B C, Chilimbi T, Vaid K. Web search using mobile cores: quantifying and mitigating the price of efficiency. ACM SIGARCH Computer Architecture News, 2010, 38(3): 314–325
20 Lim K, Ranganathan P, Chang J, Patel C, Mudge T, Reinhardt S. Understanding and designing new server architectures for emerging warehouse-computing environments. In: Proceedings of the 35th International Symposium on Computer Architecture (ISCA). 2008, 315–326
21 Santos J R, Turner Y, Janakiraman G J, Pratt I. Bridging the gap between software and hardware techniques for I/O virtualization. In: Proceedings of the USENIX Annual Technical Conference (ATC). 2008, 29–42
22 Ram K K, Santos J R, Turner Y, Cox A L, Rixner S. Achieving 10 Gb/s using safe and transparent network interface virtualization. In: Proceedings of the ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE). 2009, 61–70
23 Chen H, Wu S, Shi X, Jin H, Fu Y. LCM: a lightweight communication mechanism in HPC cloud. In: Proceedings of the 6th International Conference on Pervasive Computing and Applications. 2011, 443–451
24 Ram K K, Santos J R, Turner Y. Redesigning Xen’s memory sharing mechanism for safe and efficient I/O virtualization. In: Proceedings of the 2nd conference on I/O virtualization. 2010
25 Jian Z, Xiaoyong L, Haibing G. The optimization of Xen network virtualization. In: Proceedings of the International Conference on Computer Science and Software Engineering. 2008, 431–436
26 Guo D, Liao G, Bhuyan L N. Performance characterization and cacheaware core scheduling in a virtualized multi-core server under 10GbE. In: Proceedings of the IEEE International Symposium on Workload Characterization. 2009, 168–177
27 Liao G, Guo D, Bhuyan L, King S R. Software techniques to improve virtualized I/O performance on multi-core systems. In: Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems. 2008, 161–170
28 Gordon A, Amit N, Har’El N, Ben-Yehuda M, Landau A, Schuster A, Tsafrir D. ELI: bare-metal performance for I/O virtualization. In: Proceedings of the 7th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 2012, 411–422
29 Abramson D. Intel virtualization technology for directed I/O. Intel Technology Journal, 2006, 10(3): 179–192
30 Rixner S. Network virtualization: breaking the performance barrier. Queue, 2008, 6(1): 37
31 Willmann P, Shafer J, Carr D,Menon A, Rixner S, Cox A L, Zwaenepoel W. Concurrent direct network access for virtual machine monitors. In: Proccedings of the 13th IEEE International Symposium on High Performance Computer Architecture (HPCA). 2007, 306–317
32 Liu J. Evaluating standard-based self-virtualizing devices: a performance study on 10 GbE NICs with SR-IOV support. In: Proceedings of the IEEE International Symposium on Parallel & Distributed Processing (IPDPS). 2010, 1–12
33 Jones S T, Arpaci-Dusseau A C, Arpaci-Dusseau R H. Antfarm: tracking processes in a virtual machine environment. In: Proceedings of the USENIX Annual Technical Conference (ATC). 2006, 1–14
34 Jin H, Ling X, Ibrahim S, Cao W, Wu S, Antoniu G. Flubber: two-level disk scheduling in virtualized environment. Future Generation Computer Systems, 2013, 29(8): 2222–2238
35 Xu Y, Jiang S. A scheduling framework that makes any disk schedulers non-work-conserving solely based on request characteristics. In: Proceedings of the USENIX Conference on File and Storage Technologies. 2011, 119–132
36 Zhang B B, Wang X L, Yang L, Lai R F, Wang Z L, Luo Y W, Li X M. Modifying guest OS to optimize I/O virtualization in KVM. Chinese Journal of Computers, 2010, 33(12): 2312–2319
37 Ling X, Ibrahim S, Jin H, Wu S, Tao S. Exploiting spatial locality to improve disk efficiency in virtualized environments. In: Proceedings of the 21st IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS). 2013
38 Wang H, Varman P. A flexible approach to efficient resource sharing in virtualized environments. In: Proceedings of the 8th ACM International Conference on Computing Frontiers. 2011, 1–10
39 Seelam S R, Teller P J. Virtual I/O scheduler: a scheduler of schedulers for performance virtualization. In: Proceedings of the 3rd International Conference on Virtual Execution Environments. 2007, 105–115
40 Kesavan M, Gavrilovska A, Schwan K. Differential virtual time (DVT): rethinking I/O service differentiation for virtual machines. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 27–38
41 Gulati A, Ahmad I, Waldspurger C A. PARDA: proportional allocation of resources for distributed storage access. In: Proccedings of the 7th Conference on File and Storage Technologies (FAST). 2009, 85–98
42 Gulati A, Merchant A, Varman P J. mClock: handling throughput variability for hypervisor I/O scheduling. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI). 2010, 1–7
43 Arunagiri S, Kwok Y, Teller P J, Portillo R A, Seelam S R. FAIRIO: a throughput-oriented algorithm for differentiated I/O performance. International Journal of Parallel Programming, 2014, 42(1): 165–197
44 Shue D, Freedman M J, Shaikh A. Performance isolation and fairness for multi-tenant cloud storage. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI). 2012, 349–362
45 Lin C, Lu S. Scheduling scientific workflows elastically for cloud computing. In: Proceedings of the IEEE International Conference on Cloud Computing (CLOUD). 2011, 746–747
46 Zhang F, Cao J, Hwang K, Wu C. Ordinal optimized scheduling of scientific workflows in elastic compute clouds. In: Proceedings of the 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom). 2011, 9–17
47 Rahman M, Hassan R, Ranjan R, Buyya R. Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency and Computation: Practice and Experience, 2013, 25(13): 1816–1842
48 Liu B, Li J, Liu C. Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. Journal of Biomedical Informatics, 2014, 49: 119–133
49 Liu S W, Kong L M, Ren K J, Song J Q, Deng K F, Leng H Z. A twostep data placement and task scheduling strategy for optimizing scientific workflow performance on cloud computing platform. Chinese Journal of Computers, 2011, 34(11): 2121–2130
50 Deelman E, Chervenak A. Data management challenges of dataintensive scientific workflows. In: Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGRID). 2008, 687–692
51 Yuan D, Yang Y, Liu X, Chen J. A cost-effective strategy for intermediate data storage in scientific cloud workflow systems. In: Proceedings of 2010 IEEE International Symposium on Parallel and Distributed Processing (IPDPS). 2010, 1–12
52 Zheng P, Cui L Z, Wang H Y, Xu M. A data placement strategy for dataintensive applications in cloud. Chinese Journal of Computers, 2010, 33(8): 1472–1480
53 He B, Fang W, Luo Q, Govindaraju N K, Wang T. Mars: a MapReduce framework on graphics processors. In: Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques (PACT). 2008, 260–269
54 Linderman M D, Collins J D, Wang H, Meng T H. Merge: a programming model for heterogeneous multi-core systems. In: Proceedings of the 13th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 2008, 287–296
55 Boob S, Gonzalez-Velez H, Popescu A M. Automated instantiation of heterogeneous fast flow CPU/GPU parallel pattern applications in clouds. In: Proceedings of the 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). 2014, 162–169
56 Campa S, Danelutto M, Goli M, González-Vélez H, Popescu A M, Torquati M. Parallel patterns for heterogeneous CPU/GPU architectures: structured parallelism from cluster to cloud. Future Generation Computer Systems, 2014, 37: 354–366
57 Luk C K, Hong S, Kim H. Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In: Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 2009, 45–55
58 Ravi V T, Ma W, Chiu D, Agrawal G. Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations. In: Proceedings of the 24th ACM International Conference on Supercomputing (ICS). 2010, 137–146
59 Grewe D, O’Boyle M F. A static task partitioning approach for heterogeneous systems using OpenCL. Lecture Notes in Computer Science. 2011, 6601: 286–305
60 Gupta V, Gavrilovska A, Schwan K, Kharche H, Tolia N, Talwar V, Ranganathan P. GViM: GPU-accelerated virtual machines. In: Proceedings of the 3rd ACM Workshop on System-level Virtualization for High Performance Computing. 2009, 17–24
61 Shi L, Chen H, Sun J, Li K. vCUDA: GPU-accelerated high performance computing in virtual machines. IEEE Transactions on Computers, 2012, 61(6): 804–816
62 Giunta G, Montella R, Agrillo G, Coviello G. A GPGPU transparent virtualization component for high performance computing clouds. In: Proceedings of Euro-Par 2010-Parallel Processing. 2010, 379–391
63 Jo H, Jeong J, Lee M, Choi D H. Exploiting GPUs in virtual machine for BioCloud. BioMed Research International, 2013
64 Shih C S, Wei J W, Hung S H, Chen J, Chang N. Fairness scheduler for virtual machines on heterogonous multi-core platforms. ACMSIGAPP Applied Computing Review, 2013, 13(1): 28–40
65 Hu L, Che X, Xie Z. GPGPU cloud: a paradigm for general purpose computing. Tsinghua Science and Technology, 2013, 18(1): 22–23
66 Chen H, Shi L, Sun J. VMRPC: a high efficiency and light weight RPC system for virtual machines. In: Proceedings of the 18th IEEE International Workshop on Quality of Service (IWQoS). 2010
67 Montella R, Giunta G, Laccetti G. Virtualizing high-end GPGPUs on ARM clusters for the next generation of high performance cloud computing. Cluster Computing, 2014, 17(1): 139–152
68 Cai Y, Li G, Wang H, Zheng G, Lin S. Development of parallel explicit finite element sheet forming simulation system based on GPU architecture. Advances in Engineering Software, 2012, 45(1): 370–379
69 Ari I, Muhtaroglu N. Design and implementation of a cloud computing service for finite element analysis. Advances in Engineering Software, 2013, 60: 122–135
70 Negrut D, Tasora A, Anitescu M, Mazhar H, Heyn T, Pazouki A. Solving large multi-body dynamics problems on the GPU. GPU Gems, 2011, 4: 269–280
71 Hanniel I, Haller K. Direct rendering of solid CAD models on the GPU. In: Proceedings of the 12th International Conference on Computer-Aided Design and Computer Graphics (CAD/Graphics). 2011, 25–32
72 Hsieh H T, Chu C H. Particle swarm optimisation (PSO)-based tool path planning for 5-axis flank milling accelerated by graphics processing unit (GPU). International Journal of Computer Integrated Manufacturing, 2011, 24(7): 676–687
73 Hung Y, Wang W. Accelerating parallel particle swarm optimization via GPU. Optimization Methods and Software, 2012, 27(1): 33–51
74 Jung H Y, Jun C W, Sohn J H. GPU-based collision analysis between a multi-body system and numerous particles. Journal of Mechanical Science and Technology, 2013, 27(4): 973–980
75 Nguyen Van H, Dang Tran F, Menaud J M. Autonomic virtual resource management for service hosting platforms. In: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing. 2009, 1–8
76 Mehta H K, Kanungo P, Chandwani M. Performance enhancement of scheduling algorithms in clusters and grids using improved dynamic load balancing techniques. In: Proceedings of the 20th International Conference Companion on World Wide Web. 2011, 385–390
77 Chapman C, Emmerich W, Márquez F G, Clayman S, Galis A. Software architecture definition for on-demand cloud provisioning. Cluster Computing, 2012, 15(2): 79–100
78 Ardagna D, Panicucci B, Passacantando M. A game theoretic formulation of the service provisioning problem in cloud systems. In: Proceedings of the 20th International Conference Companion on World Wide Web (WWW). 2011, 177–186
79 Qiang L, Qin-Fen H, Li-Min X, Zhou-Jun L. Adaptive management and multi-objective optimization for virtual machine placement in cloud computing. Chinese Journal of Computers, 2011, 34(12): 2253–2264
80 Kaur P D, Chana I. A resource elasticity framework for QoS-aware execution of cloud applications. Future Generation Computer Systems, 2014, 37: 14–25
81 Son S, Jun S C. Negotiation-based flexible SLA establishment with SLA-driven resource allocation in cloud computing. In: Proceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). 2013, 168–171
82 García A G, Espert I B, García V H. SLA-driven dynamic cloud resource management. Future Generation Computer Systems, 2014, 31: 1–11
83 Wang L, Zhan J, Shi W, Liang Y, Yuan L. In cloud, do MTC or HTC service providers benefit from the economies of scale? In: Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers. 2009, 7
84 Jin H, Qin H, Wu S, Guo X. CCAP: a cache contention-aware virtual machine placement approach for HPC cloud. International Journal of Parallel Programming, 2015, 43(3): 403–420
85 Chen H, Wu S, Di S, Zhou B, Xie Z, Jin H, Shi X. Communicationdriven scheduling for virtual clusters in cloud. In: Proceedings of the 23rd International Symposium on High-performance Parallel and Distributed Computing (HPDC). 2014, 125–128
86 Wu S, Chen H, Di S, Zhou B, Xie Z, Jin H, Shi X. Synchronizationaware scheduling for virtual clusters in cloud. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(10): 2890–2901
87 Eldred M S. Optimization strategies for complex engineering applications. Technical Report, Sandia National Labs., Albuquerque, NM (United States), 1998
88 Keahey K. Cloud computing for science. In: Proceedings of the 21st International Conference on Scientific and Statistical Database Management. 2009, 478
89 Deelman E, Singh G, Livny M, Berriman B, Good J. The cost of doing science on the cloud: the montage example. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing (ICS). 2008, 50
90 Wang L, Tao J, Kunze M, Castellanos A C, Kramer D, Karl W. Scientific cloud computing: Early definition and experience. In: Proceedings of the IEEE International Conference on High Performance Computing and Communications. 2008, 825–830
91 Ludäscher B, Altintas I, Berkley C, Higgins D, Jaeger E, Jones M, Lee E A, Tao J, Zhao Y. Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience, 2006, 18(10): 1039–1065
92 Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock M R, Wipat A, Li P. Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics, 2004, 20(17): 3045–3054
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