skip to main content
survey

SLA Management for Big Data Analytical Applications in Clouds: A Taxonomy Study

Published:12 June 2020Publication History
Skip Abstract Section

Abstract

Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research.

Skip Supplemental Material Section

Supplemental Material

References

  1. Amazon. 2019. Amazon Comprehend. Retrieved April 14, 2020 from https://aws.amazon.com/comprehend/.Google ScholarGoogle Scholar
  2. Google Cloud for Retail. 2020. Google Cloud for Retail: Helping retailers transform their businesses. Retrieved May 23, 2020 from https://cloud.google.com/blog/topics/retail/google-cloud-for-retail-helping-retailers-transform-their-businesses.Google ScholarGoogle Scholar
  3. Salesforce. 2019. Marketing Cloud Platform Overview. Retrieved April 14, 2020 from https://www.salesforce.com/au/products/marketing-cloud/platform.Google ScholarGoogle Scholar
  4. Apache Hadoop. 2014. Yarn Scheduler Load Simulator (SLS). Retrieved April 14, 2020 from https://hadoop.apache.org/docs/r2.4.1/hadoop-sls/SchedulerLoadSimulator.html.Google ScholarGoogle Scholar
  5. Google Cloud. 2017. Google Prediction API and Google BigQuery SLA. Retrieved April 14, 2020 from https://cloud.google.com/bigquery/sla.Google ScholarGoogle Scholar
  6. Google Cloud. 2018. Architecture: Optimizing Large-Scale Ingestion of Analytics Events and Logs. Retrieved April 14, 2020 from https://cloud.google.com/solutions/architecture/optimized-large-scale-analytics-ingestion/.Google ScholarGoogle Scholar
  7. Mohammed Alhamad, Tharam Dillon, and Elizabeth Chang. 2010. Conceptual SLA framework for cloud computing. In Proceedings of the 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST’10). IEEE, Los Alamitos, CA, 606--610.Google ScholarGoogle ScholarCross RefCross Ref
  8. Khalid Alhamazani, Rajiv Ranjan, Prem Prakash Jayaraman, Karan Mitra, Meisong Wang, Zhiqiang George Huang, Lizhe Wang, and Fethi Rabhi. 2014. Real-time QoS monitoring for cloud-based big data analytics applications in mobile environments. In Proceedings of the IEEE 15th International Conference on Mobile Data Management (MDM’14), Vol. 1. IEEE, Los Alamitos, CA, 337--340.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ahmad B. Alnafoosi and Theresa Steinbach. 2013. An integrated framework for evaluating big-data storage solutions-IDA case study. In Proceedings of the 2013 Science and Information Conference (SAI’13). IEEE, Los Alamitos, CA, 947--956.Google ScholarGoogle Scholar
  10. Mohammed Alrokayan, Amir Vahid Dastjerdi, and Rajkumar Buyya. 2014. SLA-aware provisioning and scheduling of cloud resources for big data analytics. In Proceedings of the 2014 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM’14). IEEE, Los Alamitos, CA, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  11. Mauro Andreolini, Michele Colajanni, Marcello Pietri, and Stefania Tosi. 2015. Adaptive, scalable and reliable monitoring of big data on clouds. Journal of Parallel and Distributed Computing 79 (2015), 67--79.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Zhi-Guang Ao, Ming-Hai Jiao, Ke-Ning Gao, and Xing-Wei Wang. 2016. Research on cloud resource optimization model based on users’ satisfaction. In Proceedings of the 2016 IEEE 13th Web Information Systems and Applications Conference. IEEE, Los Alamitos, CA, 99--102.Google ScholarGoogle ScholarCross RefCross Ref
  13. Marcos D. Assunção, Rodrigo N. Calheiros, Silvia Bianchi, Marco A. S. Netto, and Rajkumar Buyya. 2015. Big data computing and clouds: Trends and future directions. J.ournal of Parallel and Distributed Computing 79, 3--15.Google ScholarGoogle Scholar
  14. William H. Bell, David G. Cameron, Luigi Capozza, A. Paul Millar, Kurt Stockinger, and Floriano Zini. 2002. Simulation of dynamic grid replication strategies in OptorSim. In Proceedings of the International Workshop on Grid Computing. 46--57.Google ScholarGoogle ScholarCross RefCross Ref
  15. Paolo Bellavista, Antonio Corradi, Andrea Reale, and Nicola Ticca. 2014. Priority-based resource scheduling in distributed stream processing systems for big data applications. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing. IEEE, Los Alamitos, CA, 363--370.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Mihaly Berekmeri, Damián Serrano, Sara Bouchenak, Nicolas Marchand, and Bogdan Robu. 2014. A control approach for performance of big data systems. IFAC Proceedings Volumes 47, 3, 152--157.Google ScholarGoogle ScholarCross RefCross Ref
  17. Karin Bernsmed, Martin Gilje Jaatun, Per Hakon Meland, and Astrid Undheim. 2011. Security SLAs for federated cloud services. In Proceedings of the 2011 6th International Conference on Availability, Reliability, and Security. IEEE, Los Alamitos, CA, 202--209.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25, 6, 599--616.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Xiaojun Cai, Feng Li, Ping Li, Lei Ju, and Zhiping Jia. 2017. SLA-aware energy-efficient scheduling scheme for Hadoop YARN. Journal of Supercomputing 73, 8, 3526--3546.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Néstor Cárdenas-Benítez, Raúl Aquino-Santos, Pedro Magaña-Espinoza, José Aguilar-Velazco, Arthur Edwards-Block, and Aldo Medina Cass. 2016. Traffic congestion detection system through connected vehicles and big data. Sensors 16, 5 (2016), 599.Google ScholarGoogle ScholarCross RefCross Ref
  21. C. L. Philip Chen and Chun-Yang Zhang. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences 275 (2014), 314--347.Google ScholarGoogle ScholarCross RefCross Ref
  22. Hsinchun Chen, Roger H. L. Chiang, and Veda C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly 36, 4 (2012), 1165--1188.Google ScholarGoogle ScholarCross RefCross Ref
  23. Tao Cheng and Thomas Wicks. 2014. Event detection using Twitter: A spatio-temporal approach. PLoS One 9 (2014), 6, e97807.Google ScholarGoogle ScholarCross RefCross Ref
  24. Yeongho Choi and Yujin Lim. 2015. Resource management mechanism for SLA provisioning on cloud computing for IoT. In Proceedings of the 2015 International Conference on Information and Communication Technology Convergence (ICTC’15). IEEE, Los Alamitos, CA, 500--502.Google ScholarGoogle ScholarCross RefCross Ref
  25. Yeongho Choi and Yujin Lim. 2016. Optimization approach for resource allocation on cloud computing for IoT. International Journal of Distributed Sensor Networks 2016 (2016), 23.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. I-Hsun Chuang, Yu-Ting Huang, Wei-Tsung Su, Tung-Sheng Lin, and Yau-Hwang Kuo. 2015. S4: An SLA-aware short-secret-sharing cloud storage system. In Proceedings of the 2015 7th International Conference on Ubiquitous and Future Networks. IEEE, Los Alamitos, CA, 401--406.Google ScholarGoogle ScholarCross RefCross Ref
  27. Amit Kumar Das, Tamal Adhikary, Md Abdur Razzaque, Majed Alrubaian, Mohammad Mehedi Hassan, Md Zia Uddin, and Biao Song. 2017. Big media healthcare data processing in cloud: A collaborative resource management perspective. Cluster Computing 20 (2017), 2, 1599--1614.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Carlos André Batista De Carvalho, Rossana Maria de Castro Andrade, Miguel Franklin de Castro, Emanuel Ferreira Coutinho, and Nazim Agoulmine. 2017. State of the art and challenges of security SLA for cloud computing. Computers 8 Electrical Engineering 59 (2017), 141--152.Google ScholarGoogle Scholar
  29. Lucia De Marco, Filomena Ferrucci, and Tahar Kechadi. 2015. SLAFM: A service level agreements formal model for cloud computing. In Proceedings of the 5th International Conference on Cloud Computing and Service Science (CLOSER’15).Google ScholarGoogle ScholarCross RefCross Ref
  30. Ramon Hugo de Souza, Paulo Arion Flores, Mário Antônio Ribeiro Dantas, and Frank Siqueira. 2016. Architectural recovering model for distributed databases: A reliability, availability and serviceability approach. In Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC’16). IEEE, Los Alamitos, CA, 575--580.Google ScholarGoogle ScholarCross RefCross Ref
  31. Laouratou Diallo, Aisha-Hassan A. Hashim, Rashidah Funke Olanrewaju, Shayla Islam, and Abdullah Ahmad Zarir. 2016. Two objectives big data task scheduling using swarm intelligence in cloud computing. Indian Journal of Science and Technology 9 (2016), 28.Google ScholarGoogle ScholarCross RefCross Ref
  32. Djawida Dib, Nikos Parlavantzas, and Christine Morin. 2014. SLA-based profit optimization in cloud bursting PaaS. In Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid’14). IEEE, Los Alamitos, CA, 141--150.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Mouhamad Dieye, Mohamed Faten Zhani, and Halima Elbiaze. 2017. On achieving high data availability in heterogeneous cloud storage systems. In Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM’17). IEEE, Los Alamitos, CA, 326--334.Google ScholarGoogle ScholarCross RefCross Ref
  34. Sofia D’Souza and K. Chandrasekaran. 2015. Analysis of MapReduce scheduling and its improvements in cloud environment. In Proceedings of the IEEE International Conference on Signal Processing, Informatics, Communication, and Energy Systems (SPICES’15). IEEE, Los Alamitos, CA, 1--5.Google ScholarGoogle Scholar
  35. Todd Escalona. January 2018. Detect Sentiment from Customer Reviews Using Amazon Comprehend. Retrieved April 16, 2020 from https://aws.amazon.com/blogs/machine-learning/detect-sentiment-from-customer-reviews-using-amazon-comprehend/.Google ScholarGoogle Scholar
  36. Funmilade Faniyi and Rami Bahsoon. 2016. A systematic review of service level management in the cloud. ACM Computing Surveys 48, 3 (2016), 43.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Victor A. E. Farias, Flavio R. C. Sousa, Jose Gilvan R. Maia, Joao Paulo P. Gomes, and Javam C. Machado. 2018. Regression based performance modeling and provisioning for NoSQL cloud databases. Future Generation Computer Systems 79 (2018), 72--81.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Anshul Gandhi, Sidhartha Thota, Parijat Dube, Andrzej Kochut, and Li Zhang. 2016. Autoscaling for Hadoop clusters. In Proceedings of the 2016 IEEE International Conference on Cloud Engineering (IC2E’16). IEEE, Los Alamitos, CA, 109--118.Google ScholarGoogle ScholarCross RefCross Ref
  39. Eugenio Gianniti, Danilo Ardagna, Michele Ciavotta, and Mauro Passacantando. 2017. A game-theoretic approach for runtime capacity allocation in MapReduce. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. IEEE, Los Alamitos, CA, 1080--1089.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Adam Gregory and Shikharesh Majumdar. 2016. A configurable energy aware resource management technique for optimization of performance and energy consumption on clouds. In Proceedings of the 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom’16). IEEE, Los Alamitos, CA, 184--192.Google ScholarGoogle ScholarCross RefCross Ref
  41. Adam Gregory and Shikharesh Majumdar. 2016. A constraint programming based energy aware resource management middleware for clouds processing MapReduce jobs with deadlines. In the Companion Publication for ACM/SPEC on the International Conference on Performance Engineering (ICPE’16 Companion). ACM, New York, NY, 15--20.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Adam Gregory and Shikharesh Majumdar. 2016. Energy aware resource management for MapReduce jobs with service level agreements in cloud data centers. In Proceedings of the 2016 IEEE International Conference on Computer and Information Technology (CIT’16). IEEE, Los Alamitos, CA, 568--577.Google ScholarGoogle ScholarCross RefCross Ref
  43. Lin Gu, Deze Zeng, Song Guo, Yong Xiang, and Jiankun Hu. 2016. A general communication cost optimization framework for big data stream processing in geo-distributed data centers. IEEE Transactions on Computers 65, 1 (2016), 19--29.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Lin Gu, Deze Zeng, Peng Li, and Song Guo. 2014. Cost minimization for big data processing in geo-distributed data centers. IEEE Transactions on Emerging Topics in Computing 2, 3 (2014), 314--323.Google ScholarGoogle ScholarCross RefCross Ref
  45. Muhammad Hanif, Hyungduk Yoon, Sunglim Jang, and Choonhwa Lee. 2017. An adaptive SLA-based data flow mechanism for stream processing engines. In Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC’17). IEEE, Los Alamitos, CA, 81--86.Google ScholarGoogle ScholarCross RefCross Ref
  46. Ibrahim Abaker Targio Hashem, Nor Badrul Anuar, Mohsen Marjani, Abdullah Gani, Arun Kumar Sangaiah, and Adewole Kayode Sakariyah. 2018. Multi-objective scheduling of MapReduce jobs in big data processing. Multimedia Tools and Applications 77, 8 (2018), 9979--9994.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. S. Hemalatha and S. Valarmathi. 2016. Efficient hybrid framework for parallel resource and task scheduling in the Map Reduce programming. In Proceedings of the 2016 International Conference on Computer Communication and Informatics (ICCCI’16). IEEE, Los Alamitos, CA, 1--7.Google ScholarGoogle Scholar
  48. Raymond Hoare, Jiyong Ahn, and Jesse Graves. Discrete event simulator. Google Patents.Google ScholarGoogle Scholar
  49. Christoph Hochreiner, Vogler, Stefan Schulte, and Schahram Dustdar. 2017. Cost-efficient enactment of stream processing topologies. PeerJ Computer Science 3, 2 (2017), e141.Google ScholarGoogle ScholarCross RefCross Ref
  50. Chao-Wen Huang, Wan-Hsun Hu, Chia-Chun Shih, Bo-Ting Lin, and Chien-Wei Cheng. 2013. The improvement of auto-scaling mechanism for distributed database—A case study for MongoDB. In Proceedings of the 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS’13). IEEE, Los Alamitos, CA, 1--3.Google ScholarGoogle Scholar
  51. Pham Phuoc Hung, Tuan-Anh Bui, Kwon Soonil, and Eui-Nam Huh. 2016. A new technique for optimizing resource allocation and data distribution in mobile cloud computing. Elektronika ir Elektrotechnika 22, 1 (2016), 73--80.Google ScholarGoogle Scholar
  52. Walayat Hussain, Farookh Khadeer Hussain, Omar K. Hussain, Ernesto Damiani, and Elizabeth Chang. 2017. Formulating and managing viable SLAs in cloud computing from a small to medium service provider’s viewpoint: A state-of-the-art review. Information Systems 71 (2017), 240--259.Google ScholarGoogle ScholarCross RefCross Ref
  53. Eunji Hwang and Kyong Hoon Kim. 2012. Minimizing cost of virtual machines for deadline-constrained MapReduce applications in the cloud. In Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing. IEEE, Los Alamitos, CA, 130--138.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Shigeru Imai, Stacy Patterson, and Carlos A. Varela. 2017. Maximum sustainable throughput prediction for data stream processing over public clouds. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. IEEE, Los Alamitos, CA, 504--513.Google ScholarGoogle Scholar
  55. Gabriel Iuhasz and Ioan Dragan. 2015. An overview of monitoring tools for big data and cloud applications. In Proceedings of the 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC’15). IEEE, Los Alamitos, CA, 363--366.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Martin Gilje Jaatun, Karin Bernsmed, and Astrid Undheim. 2012. Security SLAs—An idea whose time has come? In Proceedings of the International Conference on Availability, Reliability, and Security. 123--130.Google ScholarGoogle ScholarCross RefCross Ref
  57. Ali Imran Jehangiri, Ramin Yahyapour, Philipp Wieder, Edwin Yaqub, and Kuan Lu. 2014. Diagnosing cloud performance anomalies using large time series dataset analysis. In Proceedings of the IEEE 7th International Conference on Cloud Computing (CLOUD’14). IEEE, Los Alamitos, CA, 930--933.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Selvi Kadirvel and Jose A. B, Fortes. 2011. Towards self-caring MapReduce: Proactively reducing fault-induced execution-time penalties. In Proceedings of the International Conference on High Performance Computing and Simulation (HPCS’11). IEEE, Los Alamitos, CA, 63--71.Google ScholarGoogle Scholar
  59. Hyejeong Kang, Jung-In Koh, Yoonhee Kim, and Jaegyoon Hahm. 2013. A SLA driven VM auto-scaling method in hybrid cloud environment. In Proceedings of the 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS’13). IEEE, Los Alamitos, CA, 1--6.Google ScholarGoogle Scholar
  60. Karim Kanoun, Cem Tekin, David Atienza, and Mihaela Van Der Schaar. 2016. Big-data streaming applications scheduling based on staged multi-armed bandits. IEEE Transactions on Computers 65, 12 (2016), 3591--3605.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Banpreet Kaur and Ankit Grover. 2016. Optimizing VM provisioning of MapReduce tasks on public cloud. In Proceedings of the International Conference on Advances in Information Communication Technology and Computing. ACM, New York, NY, 79.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Barbara Kitchenham, O. Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman. 2009. Systematic literature reviews in software engineering—A systematic literature review. Information and Software Technology 51, 1 (2009), 7--15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Panya Kittipipattanathaworn and Natawut Nupairoj. 2014. SLA guarantee real-time monitoring system with soft deadline constraint. In Proceedings of the 11th International Joint Conference on Computer Science and Software Engineering (JCSSE’14). IEEE, Los Alamitos, CA, 52--57.Google ScholarGoogle ScholarCross RefCross Ref
  64. K. R. Krish, M. Safdar Iqbal, M. Mustafa Rafique, and Ali R. Butt. 2014. Towards energy awareness in Hadoop. In Proceedings of the 4th International Workshop on Network-Aware Data Management (NDM’14). IEEE, Los Alamitos, CA, 16--22.Google ScholarGoogle Scholar
  65. Maria Krotsiani, Christos Kloukinas, and George Spanoudakis. 2017. Validation of service level agreements using probabilistic model checking. In Proceedings of the IEEE International Conference on Services Computing (SCC’17). IEEE, Los Alamitos, CA, 148--155.Google ScholarGoogle ScholarCross RefCross Ref
  66. Taher Labidi, Achraf Mtibaa, and Hayet Brabra. 2016. CSLAOnto: A comprehensive ontological SLA model in cloud computing. Journal on Data Semantics 5, 3 (2016), 179--193.Google ScholarGoogle ScholarCross RefCross Ref
  67. Palden Lama and Xiaobo Zhou. 2012. Aroma: Automated resource allocation and configuration of MapReduce environment in the cloud. In Proceedings of the 9th International Conference on Autonomic Computing. ACM, New York, NY, 63--72.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Jonathan Lejeune, Frederico Alvares, and Thomas Ledoux. 2017. Towards a generic autonomic model to manage cloud services. In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER’17).Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Sanping Li, Yu Cao, Simon Tao, Xiaoyan Guo, Zhe Dong, and Ricky Sun. 2015. An extensible framework for predictive analytics on cost and performance in the cloud. In Proceedings of the 2015 International Conference on Cloud Computing and Big Data (CCBD’15). IEEE, Los Alamitos, CA, 13--20.Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Norman Lim, Shikharesh Majumdar, and Peter Ashwood-Smith. 2014. A constraint programming-based resource management technique for processing MapReduce jobs with SLAs on clouds. In Proceedings of the 43rd International Conference on Parallel Processing (ICPP’14). IEEE, Los Alamitos, CA, 411--421.Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Norman Lim, Shikharesh Majumdar, and Peter Ashwood-Smith. 2014. Engineering resource management middleware for optimizing the performance of clouds processing MapReduce jobs with deadlines. In Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering. ACM, New York, NY, 161--172.Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. Norman Lim, Shikharesh Majumdar, and Peter Ashwood-Smith. 2014. Resource management techniques for handling requests with service level agreements. In Proceedings of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS’14). IEEE, Los Alamitos, CA, 618--625.Google ScholarGoogle ScholarCross RefCross Ref
  73. Norman Lim, Shikharesh Majumdar, and Peter Ashwood-Smith. 2017. MRCP-RM: A technique for resource allocation and scheduling of MapReduce jobs with deadlines. IEEE Transactions on Parallel and Distributed Systems 28, 5, 1375--1389.Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Norman Lim, Shikharesh Majumdar, and Peter Ashwood-Smith. 2017. A run time technique for handling error in user-estimated execution times on systems processing MapReduce jobs with deadlines. In Proceedings of the IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud’17). IEEE, Los Alamitos, CA, 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  75. Norman Lim, Shikharesh Majumdar, and Peter Ashwood-Smith. 2017. Techniques for handling error in user-estimated execution times during resource management on systems processing MapReduce jobs. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. IEEE, Los Alamitos, CA, 788--793.Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. Fotios K. Liotopoulos and Petros Lampsas. 2015. Energy-efficient simulation and performance evaluation of large-scale data centers. In Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT’15). IEEE, Los Alamitos, CA, 3121--3127.Google ScholarGoogle Scholar
  77. Qinghua Lu, Shanshan Li, Weishan Zhang, and Lei Zhang. 2016. A genetic algorithm-based job scheduling model for big data analytics. EURASIP Journal on Wireless Communications and Networking 2016, 1 (2016), 152.Google ScholarGoogle ScholarCross RefCross Ref
  78. Qinghua Lu, Zheng Li, Weishan Zhang, and Laurence T. Yang. 2017. Autonomic deployment decision making for big data analytics applications in the cloud. Soft Computing 21, 16 (2017), 4501--4512.Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. Yang Lu. 2017. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration 6 (2017), 1--10.Google ScholarGoogle ScholarCross RefCross Ref
  80. Mohammad-Hossein Malekloo, Nadjia Kara, and May El Barachi. 2018. An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustainable Computing: Informatics and Systems 17 (2018), 9--24.Google ScholarGoogle ScholarCross RefCross Ref
  81. Xijun Mao, Chunlin Li, Wei Yan, and Shumeng Du. 2016. Optimal scheduling algorithm of MapReduce tasks based on QoS in the hybrid cloud. In Proceedings of the 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’16). IEEE, Los Alamitos, CA, 119--124.Google ScholarGoogle ScholarCross RefCross Ref
  82. Lena Mashayekhy, Mahyar Movahed Nejad, Daniel Grosu, Quan Zhang, and Weisong Shi. 2015. Energy-aware scheduling of MapReduce jobs for big data applications. IEEE Transactions on Parallel and Distributed Systems 26, 10 (2015), 2720--2733.Google ScholarGoogle ScholarDigital LibraryDigital Library
  83. Michael Mattess, Rodrigo N. Calheiros, and Rajkumar Buyya. 2013. Scaling MapReduce applications across hybrid clouds to meet soft deadlines. In Proceedings of the 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA’13). IEEE, Los Alamitos, CA, 629--636.Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Rizwan Mian, Patrick Martin, and Jose Luis Vazquez-Poletti. 2013. Provisioning data analytic workloads in a cloud. Future Generation Computer Systems 29, 6, 1452--1458.Google ScholarGoogle ScholarDigital LibraryDigital Library
  85. Akram Mohamadi and Sedigheh Barani. 2015. A review on approaches in service level agreement in cloud computing environment. In Proceedings of the 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS’15). IEEE, Los Alamitos, CA, 1--4.Google ScholarGoogle ScholarCross RefCross Ref
  86. Saad Mubeen, Sara Abbaspour Asadollah, Alessandro V. Papadopoulos, Mohammad Ashjaei, Hongyu Pei-Breivold, and Moris Behnam. 2017. Management of service level agreements for cloud services in IoT: A systematic mapping study. IEEE Access PP, 99 (2017), 1.Google ScholarGoogle Scholar
  87. Lekha R. Nair, Sujala D. Shetty, and Siddhanth D. Shetty. 2018. Applying spark based machine learning model on streaming big data for health status prediction. Computers 8 Electrical Engineering 65 (2018), 393--399.Google ScholarGoogle Scholar
  88. Dimas C. Nascimento, Carlos Eduardo Pires, and Demetrio Mestre. 2015. Data quality monitoring of cloud databases based on data quality SLAs. In Big-Data Analytics and Cloud Computing. Springer, 3--20.Google ScholarGoogle Scholar
  89. Deveeshree Nayak, Venkata Swamy Martha, David Threm, Srini Ramaswamy, Summer Prince, and Günter Fahrnberger. 2015. Adaptive scheduling in the cloud-SLA for Hadoop job scheduling. In Proceedings of the 2015 Science and Information Conference (SAI’15). IEEE, Los Alamitos, CA, 832--837.Google ScholarGoogle ScholarCross RefCross Ref
  90. Mihaela-Catalina Nita, Cristian Chilipirea, Ciprian Dobre, and Florin Pop. 2013. A SLA-based method for big-data transfers with multi-criteria optimization constraints for IaaS. In Proceedings of the 2013 11th RoEduNet International Conference. IEEE, Los Alamitos, CA, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  91. Mihaela-Catalina Nita, Florin Pop, Cristiana Voicu, Ciprian Dobre, and Fatos Xhafa. 2015. MOMTH: Multi-objective scheduling algorithm of many tasks in Hadoop. Cluster Computing 18, 3, 1011--1024.Google ScholarGoogle ScholarDigital LibraryDigital Library
  92. Yoori Oh, Jieun Choi, Eunjung Song, Moonji Kim, and Yoonhee Kim. 2016. A SLA-based Spark cluster scaling method in cloud environment. In Proceedings of the 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS’16). IEEE, Los Alamitos, CA, 1--4.Google ScholarGoogle Scholar
  93. Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, and Samir Belfkih. 2017. Big data technologies: A survey. Journal of King Saud University—Computer and Information Sciences 30, 4 (2017), 431--448.Google ScholarGoogle Scholar
  94. Balaji Palanisamy, Aameek Singh, and Ling Liu. 2015. Cost-effective resource provisioning for MapReduce in a cloud. IEEE Transactions on Parallel and Distributed Systems 26, 5 (2015), 1265--1279.Google ScholarGoogle ScholarDigital LibraryDigital Library
  95. Tadeusz Pankowski. 2015. Consistency and availability of data in replicated NoSQL databases. In Proceedings of the International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE’15). IEEE, Los Alamitos, CA, 102--109.Google ScholarGoogle ScholarDigital LibraryDigital Library
  96. Adrian Paschke and Elisabeth Schnappinger-Gerull. 2006. A categorization scheme for SLA metrics. Service Oriented Electronic Commerce 80, 14 (2006), 25--40.Google ScholarGoogle Scholar
  97. Dana Petcu. 2014. SLA-based cloud security monitoring: Challenges, barriers, models and methods. In Proceedings of the European Conference on Parallel Processing. 359--370.Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. Jorda Polo, Yolanda Becerra, David Carrera, Malgorzata Steinder, Ian Whalley, Jordi Torres, and Eduard Ayguade. 2013. Deadline-based MapReduce workload management. IEEE Transactions on Network and Service Management 10, 2 (2013), 231--244.Google ScholarGoogle ScholarCross RefCross Ref
  99. K. Hima Prasad, Tanveer A. Faruquie, L. Venkata Subramaniam, Mukesh Mohania, and Girish Venkatachaliah. 2010. Resource allocation and SLA determination for large data processing services over cloud. In Proceedings of the 2010 IEEE International Conference on Services Computing. IEEE, Los Alamitos, CA, 522--529.Google ScholarGoogle ScholarDigital LibraryDigital Library
  100. Xuanjia Qiu, Wai Leong Yeow, Chuan Wu, and Francis C. M. Lau. 2013. Cost-minimizing preemptive scheduling of MapReduce workloads on hybrid clouds. In Proceedings of the 2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS’13). IEEE, Los Alamitos, CA, 1--6.Google ScholarGoogle Scholar
  101. Joy Rahman and Palden Lama. 2017. MPLEX: In-situ big data processing with compute-storage multiplexing. In Proceedings of the IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’17). IEEE, Los Alamitos, CA, 43--52.Google ScholarGoogle ScholarCross RefCross Ref
  102. Massimiliano Rak, Neeraj Suri, Jesus Luna, Dana Petcu, Valentina Casola, and Umberto Villano. 2013. Security as a service using an SLA-based approach via SPECS. In Proceedings of the 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, Vol. 2. IEEE, Los Alamitos, CA, 1--6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  103. B. Kezia Rani and A. Vinaya Babu. 2015. Scheduling of big data application workflows in cloud and inter-cloud environments. In Proceedings of the 2015 IEEE International Conference on Big Data (Big Data’15). IEEE, Los Alamitos, CA, 2862--2864.Google ScholarGoogle Scholar
  104. Rajiv Ranjan, Joanna Kolodziej, Lizhe Wang, and Albert Y. Zomaya. 2015. Cross-layer cloud resource configuration selection in the big data era. IEEE Cloud Computing 3 (2015), 16--22.Google ScholarGoogle ScholarCross RefCross Ref
  105. Marco A. T. Rojas, Nelson M. Gonzalez, Fernando V. Sbampato, Fernando F. Redígolo, Tereza Carvalho, Kazi W. Ullah, Mats Näslund, and Abu Shohel Ahmed. 2016. A framework to orchestrate security SLA lifecycle in cloud computing. In Proceedings of the 2016 11th Iberian Conference on Information Systems and Technologies (CISTI’16). IEEE, Los Alamitos, CA, 1--7.Google ScholarGoogle ScholarCross RefCross Ref
  106. Marco Antonio Torrez Rojas, Nelson Mimura Gonzalez, Fernando Sbampato, Fernando Redigolo, Tereza Cristina Melo de Brito Carvalho, Kim Koa Nguyen, and Mohamed Cheriet. 2015. Inclusion of security requirements in SLA lifecycle management for cloud computing. In Proceedings of the 2015 IEEE 2nd Workshop on Evolving Security and Privacy Requirements Engineering (ESPRE’15). IEEE, Los Alamitos, CA, 7--12.Google ScholarGoogle ScholarCross RefCross Ref
  107. Marco Antonio Torrez Rojas, Fernando Frota Redígolo, Nelson Mimura Gonzalez, Fernando Vilgino Sbampato, Tereza Cristina Melo de Brito Carvalho, Kazi Walli Ullah, Mats Näslund, and Abu Shohel Ahmed. 2018. Managing the lifecycle of security SLA requirements in cloud computing. In Developments and Advances in Intelligent Systems and Applications. Springer, 119--140.Google ScholarGoogle Scholar
  108. Radhya Sahal, Mohamed H. Khafagy, and Fatma A. Omara. 2016. A survey on SLA management for cloud computing and cloud-hosted big data analytic applications. International Journal of Database Theory and Application 9, 4 (2016), 107--118.Google ScholarGoogle ScholarCross RefCross Ref
  109. Prasan Kumar Sahoo, Suvendu Kumar Mohapatra, and Shih-Lin Wu. 2018. SLA based healthcare big data analysis and computing in cloud network. Journal of Parallel and Distributed Computing 119 (2018), 121--135.Google ScholarGoogle ScholarCross RefCross Ref
  110. Sherif Sakr and Anna Liu. 2012. SLA-based and consumer-centric dynamic provisioning for cloud databases. In Proceedings of the IEEE 5th International Conference on Cloud Computing (CLOUD’12). IEEE, Los Alamitos, CA, 360--367.Google ScholarGoogle ScholarDigital LibraryDigital Library
  111. Omran Saleh, Francis Gropengieber, Heiko Betz, Waseem Mandarawi, and Kai-Uwe Sattler. 2013. Monitoring and autoscaling IaaS clouds: A case for complex event processing on data streams. In Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. IEEE, Los Alamitos, CA, 387--392.Google ScholarGoogle ScholarDigital LibraryDigital Library
  112. Rajinder Sandhu and Sandeep K. Sood. 2015. Scheduling of big data applications on distributed cloud based on QoS parameters. Cluster Computing 18, 2 (2015), 817--828.Google ScholarGoogle ScholarDigital LibraryDigital Library
  113. Carla Sauvanaud, Mohamed Kaâniche, Karama Kanoun, Kahina Lazri, and Guthemberg Da Silva Silvestre. 2018. Anomaly detection and diagnosis for cloud services: Practical experiments and lessons learned. Journal of Systems and Software 139 (2018), 84--106.Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. Damián Serrano, Sara Bouchenak, Yousri Kouki, Frederico Alvares de Oliveira Jr., Thomas Ledoux, Jonathan Lejeune, Julien Sopena, Luciana Arantes, and Pierre Sens. 2016. SLA guarantees for cloud services. Future Generation Computer Systems 54, 233--246.Google ScholarGoogle ScholarDigital LibraryDigital Library
  115. Damián Serrano, Sara Bouchenak, Yousri Kouki, Thomas Ledoux, Jonathan Lejeune, Julien Sopena, Luciana Arantes, and Pierre Sens. 2013. Towards QoS-oriented SLA guarantees for online cloud services. In Proceedings of the 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. IEEE, Los Alamitos, CA, 50--57.Google ScholarGoogle ScholarDigital LibraryDigital Library
  116. M. Omair Shafiq and Eric Torunski. 2017. Towards MapReduce based Bayesian deep learning network for monitoring big data applications. In Proceedings of the IEEE International Conference on Big Data (Big Data’17). IEEE, Los Alamitos, CA, 2112--2121.Google ScholarGoogle Scholar
  117. Jin Shao and Qianxiang Wang. 2011. A performance guarantee approach for cloud applications based on monitoring. In Proceedings of the 2011 35th IEEE Annual Computer Software and Applications Conference Workshops. IEEE, Los Alamitos, CA, 25--30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  118. Yanling Shao, Chunlin Li, Wenyong Dong, and Yunchang Liu. 2016. Energy-aware dynamic resource allocation on Hadoop YARN cluster. In Proceedings of the 2016 IEEE 18th International Conference on High Performance Computing and Communications, the IEEE 14th International Conference on Smart City, and the IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS’16). IEEE, Los Alamitos, CA, 364--371.Google ScholarGoogle Scholar
  119. Yanling Shao, Chunlin Li, Jinguang Gu, Jing Zhang, and Youlong Luo. 2018. Efficient jobs scheduling approach for big data applications. Computers 8 Industrial Engineering 117, 249--261.Google ScholarGoogle Scholar
  120. Bikash Sharma, Timothy Wood, and Chita R. Das. 2013. HybridMR: A hierarchical MapReduce scheduler for hybrid data centers. In Proceedings of the 2013 IEEE 33rd International Conference on Distributed Computing Systems. IEEE, Los Alamitos, CA, 102--111.Google ScholarGoogle Scholar
  121. Mingruo Shi and Ruiping Yuan. 2015. Mad: A monitor system for big data applications. In Proceedings of the International Conference on Intelligent Science and Big Data Engineering. 308--315.Google ScholarGoogle ScholarDigital LibraryDigital Library
  122. Ming-Hung Shih and J. Morris Chang. 2017. Design and analysis of high performance crypt-NoSQL. In Proceedings of the 2017 IEEE Conference on Dependable and Secure Computing. IEEE, Los Alamitos, CA, 52--59.Google ScholarGoogle Scholar
  123. Kwang Mong Sim. 2006. A survey of bargaining models for grid resource allocation. ACM SIGecom Exchanges 5, 5 (2006), 22--32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  124. Kwang Mong Sim. 2010. Grid resource negotiation: Survey and new directions. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40, 3 (2010), 245--257.Google ScholarGoogle ScholarDigital LibraryDigital Library
  125. Mbarka Soualhia, Foutse Khomh, and Sofiène Tahar. 2017. Task scheduling in big data platforms: A systematic literature review. Journal of Systems and Software 134 (2017), 170--189.Google ScholarGoogle ScholarDigital LibraryDigital Library
  126. Andre Abrantes D. P. Souza and Marco A. S. Netto. 2015. Using application data for SLA-aware auto-scaling in cloud environments. In Proceedings of the IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’15). IEEE, Los Alamitos, CA, 252--255.Google ScholarGoogle Scholar
  127. Xi Sun, Bo Gao, Liya Fan, and Wenhao An. 2012. A cost-effective approach to delivering analytics as a service. In Proceedings of the IEEE 19th International Conference on Web Services (ICWS’12). IEEE, Los Alamitos, CA, 512--519.Google ScholarGoogle ScholarDigital LibraryDigital Library
  128. Yangyang Tao, Shucheng Yu, and Junxiu Zhou. 2018. Information flow queue optimization in EC cloud. In Proceedings of the 2018 International Conference on Computing, Networking, and Communications (ICNC’18). IEEE, Los Alamitos, CA, 888--892.Google ScholarGoogle ScholarCross RefCross Ref
  129. Fengguang Tian and Keke Chen. 2011. Towards optimal resource provisioning for running MapReduce programs in public clouds. In Proceedings of the IEEE International Conference on Cloud Computing (CLOUD’11). IEEE, Los Alamitos, CA, 155--162.Google ScholarGoogle ScholarDigital LibraryDigital Library
  130. Rafael Tolosana-Calasanz, José Ángel Bañares, Congduc Pham, and Omer F. Rana. 2016. Resource management for bursty streams on multi-tenancy cloud environments. Future Generation Computer Systems 55 (2016), 444--459.Google ScholarGoogle ScholarDigital LibraryDigital Library
  131. Linjiun Tsai, Hubertus Franke, Chung-Sheng Li, and Wanjiun Liao. 2018. Learning-based memory allocation optimization for delay-sensitive big data processing. IEEE Transactions on Parallel and Distributed Systems 29, 6 (2018), 1332--1341.Google ScholarGoogle ScholarCross RefCross Ref
  132. Radu Tudoran, Olivier Nano, Ivo Santos, Alexandru Costan, Hakan Soncu, Luc Bouge, and Gabriel Antoniu. 2014. JetStream: Enabling high performance event streaming across cloud data-centers. In Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems. ACM, New York, NY, 23--34.Google ScholarGoogle ScholarDigital LibraryDigital Library
  133. Abhishek Verma, Ludmila Cherkasova, and Roy H. Campbell. 2011. ARIA: Automatic resource inference and allocation for MapReduce environments. In Proceedings of the 8th ACM International Conference on Autonomic Computing. ACM, New York, NY, 235--244.Google ScholarGoogle Scholar
  134. Chen Wang, Junliang Chen, Bing Bing Zhou, and Albert Y. Zomaya. 2012. Just satisfactory resource provisioning for parallel applications in the cloud. In Proceedings of the 2012 IEEE 8th World Congress on Services. IEEE, Los Alamitos, CA, 285--292.Google ScholarGoogle Scholar
  135. Guanying Wang, Ali R. Butt, Prashant Pandey, and Karan Gupta. 2009. Using realistic simulation for performance analysis of MapReduce setups. In Proceedings of the 1st ACM Workshop on Large-Scale System and Application Performance. ACM, New York, NY, 19--26.Google ScholarGoogle ScholarDigital LibraryDigital Library
  136. Meisong Wang, Rajiv Ranjan, Prem Prakash Jayaraman, Peter Strazdins, Pete Burnap, Omer Rana, and Dimitrios Georgakopulos. 2015. A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud. In Proceedings of the International Internet of Things Summit. 315--325.Google ScholarGoogle Scholar
  137. Yang Wang and Wei Shi. 2013. On Optimal Budget-Driven Scheduling Algorithms for MapReduce Jobs in the Hetereogeneous Cloud. Technical Report TR-13--02. Carleton University, Ottowa, Ontario.Google ScholarGoogle Scholar
  138. Yang Wang and Wei Shi. 2014. Budget-driven scheduling algorithms for batches of MapReduce jobs in heterogeneous clouds. IEEE Transactions on Cloud Computing 2, 3 (2004), 306--319.Google ScholarGoogle ScholarCross RefCross Ref
  139. Jonathan Stuart Ward and Adam Barker. 2014. Observing the clouds: A survey and taxonomy of cloud monitoring. Journal of Cloud Computing 3, 1 (2014), 24.Google ScholarGoogle ScholarCross RefCross Ref
  140. Md Whaiduzzaman, Mohammad Nazmul Haque, Md Rejaul Karim Chowdhury, and Abdullah Gani. 2014. A study on strategic provisioning of cloud computing services. Scientific World Journal 2014 (2014), 894362.Google ScholarGoogle ScholarCross RefCross Ref
  141. Philipp Wieder, Jan Seidel, Oliver Wäldrich, Wolfgang Ziegler, and Ramin Yahyapour. 2008. Using SLA for resource management and scheduling—A survey. In Grid Middleware and Services. Springer, 335--347.Google ScholarGoogle Scholar
  142. Xiaoyong Xu, Maolin Tang, and Yu-Chu Tian. 2016. Theoretical results of QoS-guaranteed resource scaling for cloud-based MapReduce. IEEE Transactions on Cloud Computing 6, 3 (2016), 879--889.Google ScholarGoogle ScholarCross RefCross Ref
  143. Xiaoyong Xu, Maolin Tang, and Yu-Chu Tian. 2018. QoS-guaranteed resource provisioning for cloud-based MapReduce in dynamical environments. Future Generation Computer Systems 78 (2018), 18--30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  144. Chaowei Yang, Qunying Huang, Zhenlong Li, Kai Liu, and Fei Hu. 2017. Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth 10, 1 (2017), 13--53.Google ScholarGoogle ScholarCross RefCross Ref
  145. Zhihao Yao, Ioannis Papapanagiotou, and Robert D. Callaway. 2014. SLA-aware resource scheduling for cloud storage. In Proceedings of the IEEE 3rd International Conference on Cloud Networking (CloudNet’14). IEEE, Los Alamitos, CA, 14--19.Google ScholarGoogle Scholar
  146. S. Yasmin and S. Jessica Sritha. 2017. A constraint programming-based resource allocation and scheduling of Map Reduce jobs with service level agreement. In Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics, and Soft Computing (ICECDS’17). IEEE, Los Alamitos, CA, 3589--3594.Google ScholarGoogle Scholar
  147. Abdulsalam Yassine, Ali Asghar Nazari Shirehjini, and Shervin Shirmohammadi. 2016. Bandwidth on-demand for multimedia big data transfer across geo-distributed cloud data centers. IEEE Transactions on Cloud Computing PP, 99 (2016), 1.Google ScholarGoogle Scholar
  148. Xiaoqun Yuan, Geyong Min, Laurence T. Yang, Yi Ding, and Qing Fang. 2017. A game theory-based dynamic resource allocation strategy in geo-distributed datacenter clouds. Future Generation Computer Systems 76 (2017), 63--72.Google ScholarGoogle ScholarDigital LibraryDigital Library
  149. Bernard P. Zeigler, Tag Gon Kim, and Herbert Praehofer. 2000. Theory of Modeling and Simulation. Academic Press.Google ScholarGoogle Scholar
  150. Xuezhi Zeng, Saurabh Garg, Zhenyu Wen, Peter Strazdins, Lizhe Wang, and Rajiv Ranjan. 2016. SLA-aware scheduling of Map-Reduce applications on public clouds. In Proceedings of the 2016 IEEE 18th International Conference on High Performance Computing and Communications, the IEEE 14th International Conference on Smart City, and the IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS’16). IEEE, Los Alamitos, CA, 655--662.Google ScholarGoogle Scholar
  151. Xuezhi Zeng, Saurabh Kumar Garg, Zhenyu Wen, Peter Strazdins, Albert Y. Zomaya, and Rajiv Ranjan. 2017. Cost efficient scheduling of MapReduce applications on public clouds. Journal of Computational Science 26 (2018), 375--388.Google ScholarGoogle Scholar
  152. Rui Zhang, Reshu Jain, Prasenjit Sarkar, and Lukas Rupprecht. 2014. Getting your big data priorities straight: A demonstration of priority-based QoS using social-network-driven stock recommendation. Proceedings of the VLDB Endowment 7, 13 (2014), 1665--1668.Google ScholarGoogle ScholarDigital LibraryDigital Library
  153. Liang Zhao, Sherif Sakr, and Anna Liu. 2015. A framework for consumer-centric SLA management of cloud-hosted databases. IEEE Transactions on Services Computing 8, 4 (2015), 534--549.Google ScholarGoogle ScholarCross RefCross Ref
  154. Yali Zhao, Rodrigo N. Calheiros, James Bailey, and Richard Sinnott. 2016. SLA-based profit optimization for resource management of big data analytics-as-a-service platforms in cloud computing environments. In Proceedings of the 2016 IEEE International Conference on Big Data (Big Data’16). IEEE, Los Alamitos, CA, 432--441.Google ScholarGoogle ScholarCross RefCross Ref
  155. Yali Zhao, Rodrigo N. Calheiros, Graeme Gange, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2015. SLA-based resource scheduling for big data analytics as a service in cloud computing environments. In Proceedings of the 2015 44th International Conference on Parallel Processing. IEEE, Los Alamitos, CA, 510--519.Google ScholarGoogle ScholarDigital LibraryDigital Library
  156. Qin Zheng. 2010. Improving MapReduce fault tolerance in the cloud. In Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops, and PhD Forum (IPDPSW’10). IEEE, Los Alamitos, CA, 1--6.Google ScholarGoogle Scholar
  157. Liudong Zuo and Michelle M. Zhu. 2015. Concurrent bandwidth reservation strategies for big data transfers in high-performance networks. IEEE Transactions on Network and Service Management 12, 2 (2015), 232--247.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. SLA Management for Big Data Analytical Applications in Clouds: A Taxonomy Study

          Recommendations

          Comments

          Login options

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

          Sign in

          Full Access

          • Published in

            cover image ACM Computing Surveys
            ACM Computing Surveys  Volume 53, Issue 3
            May 2021
            787 pages
            ISSN:0360-0300
            EISSN:1557-7341
            DOI:10.1145/3403423
            Issue’s Table of Contents

            Copyright © 2020 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]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 12 June 2020
            • Online AM: 7 May 2020
            • Accepted: 1 February 2020
            • Revised: 1 January 2020
            • Received: 1 July 2019
            Published in csur Volume 53, Issue 3

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • survey
            • Research
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format