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Cloud Marginal Resource Allocation: A Decision Support Model


One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.

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  1. Gartner (2019) Gartner forecasts worldwide public cloud revenue to grow 17.5 percent in 2019. Gartner, Stratford

    Google Scholar 

  2. Statista (2019) Spending on public cloud IT services (SaaS/PaaS) worldwide 2015-2020, Hamburg

  3. Hussain W, Hussain FK, Hussain OK (2014) Maintaining trust in cloud computing through SLA monitoring. In: Neural information processing. Springer

  4. Son S et al (2016) Adaptive trade-off strategy for bargaining-based multi-objective SLA establishment under varying cloud workload. J Supercomput 72(4):1597–1622

    Google Scholar 

  5. Silaghi GC, ŞErban LD, Litan CM (2012) A time-constrained SLA negotiation strategy in competitive computational grids. Futur Gener Comput Syst 28(8):1303–1315

    Google Scholar 

  6. Gwak J, Sim KM (2013) A novel method for coevolving PS-optimizing negotiation strategies using improved diversity controlling EDAs. Appl Intell 38(3):384–417

    Google Scholar 

  7. Sim KM (2010) Grid resource negotiation: survey and new directions. IEEE Trans Syst Man Cybern Part C Appl Rev 40(3):245–257

    Google Scholar 

  8. Gao H et al (2018) Toward service selection for workflow reconfiguration: an interface-based computing solution. Futur Gener Comput Syst 87:298–311

    Google Scholar 

  9. Abts D, Felderman BJQ (2012) A guided tour through data-center networking. Queue 10(5):10

    Google Scholar 

  10. Luong NC et al (2017) Resource management in cloud networking using economic analysis and pricing models: a survey. IEEE Commun Surv Tutorials 19(2):954–1001

    Google Scholar 

  11. Aazam M, Huh E-N (2014) Advance resource reservation and QoS based refunding in cloud federation. In: 2014 IEEE Globecom Workshops (GC Wkshps). IEEE

  12. Shen H, Li Z (2016) New bandwidth sharing and pricing policies to achieve a win-win situation for cloud provider and tenants. IEEE Trans Parallel Distrib Syst 27(9):2682–2697

    Google Scholar 

  13. Prasad KH et al (2010) Resource allocation and SLA determination for large data processing services over cloud. In: 2010 IEEE international conference on services computing. IEEE

  14. Altmann J, Kashef MM (2014) Cost model based service placement in federated hybrid clouds. Futur Gener Comput Syst 41:79–90

    Google Scholar 

  15. Shepherd WG (1992) Ramsey pricing: its uses and limits. Util Policy 2(4):296–298

    Google Scholar 

  16. Hadji M, Zeghlache D (2017) Mathematical programming approach for revenue maximization in cloud federations. IEEE Trans Cloud Comput 5(1):99–111

    Google Scholar 

  17. Dilip KSM, Sadashiv N, Goudar R (2014) Priority based resource allocation and demand based pricing model in peer-to-peer clouds. In: 2014 international conference on advances in computing, communications and informatics (ICACCI). IEEE

  18. Hussain W et al (2016) Provider-based optimized personalized viable SLA (OPV-SLA) framework to prevent SLA violation. British Computer Society

  19. Hussain W, Hussain FK, Hussain OK (2016) SLA management framework to avoid violation in cloud. In: International conference on neural information processing. Springer

  20. Hussain W et al (2016) Provider-based optimized personalized viable SLA (OPV-SLA) framework to prevent SLA violation. Comput J 59(12):1760–1783

    Google Scholar 

  21. Hussain W, Hussain FK, Hussain O (2015) Comparative analysis of consumer profile-based methods to predict SLA violation. In: IEEE (ed) FUZZ-IEEE. IEEE, Istanbul

    Google Scholar 

  22. Emeakaroha VC et al (2010) Low level metrics to high level SLAs-LoM2HiS framework: bridging the gap between monitored metrics and SLA parameters in cloud environments. In: 2010 international conference on high performance computing and simulation (HPCS). IEEE

  23. Emeakaroha VC et al (2012) Towards autonomic detection of SLA violations in Cloud infrastructures. Futur Gener Comput Syst 28(7):1017–1029

    Google Scholar 

  24. Zhang Y, Zheng Z, Lyu MR (2011) Exploring latent features for memory-based QoS prediction in cloud computing. In: 2011 30th IEEE symposium on reliable distributed systems (SRDS). IEEE

  25. Kamel A, Al-Fuqaha A, Guizani M (2015) Exploiting client-side collected measurements to perform QoS assessment of IaaS. IEEE Trans Mob Comput 14(9):1876–1887

    Google Scholar 

  26. Redl C et al (2012) Automatic SLA matching and provider selection in grid and cloud computing markets. In: Proceedings of the 2012 ACM/IEEE 13th international conference on grid computing. IEEE Computer Society

  27. Joshi KP, Pearce C (2015) Automating cloud service level agreements using semantic technologies. In: 2015 IEEE international conference on cloud engineering (IC2E). IEEE

  28. Hussain W et al (2018) Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs. Futur Gener Comput Syst 89:464–477

    Google Scholar 

  29. Dastjerdi AV et al (2015) CloudPick: a framework for QoS-aware and ontology-based service deployment across clouds. Softw Pract Exp 45(2):197–231

    Google Scholar 

  30. Gao H et al (2018) Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks. Int J Distrib Sens Netw 14(2):1550147718761583

    Google Scholar 

  31. Haq IU, Brandic I, Schikuta E (2010) Sla validation in layered cloud infrastructures. In: Economics of grids, clouds, systems, and services. Springer, pp 153–164

  32. Yin Y, Chen L, Wan J (2018) Location-aware service recommendation with enhanced probabilistic matrix factorization. IEEE Access 6:62815–62825

    Google Scholar 

  33. Yin Y et al (2017) Network location-aware service recommendation with random walk in cyber-physical systems. Sensors 17(9):2059

    Google Scholar 

  34. Yin Y et al (2016) QoS prediction for web service recommendation with network location-aware neighbor selection. Int J Softw Eng Knowl Eng 26(04):611–632

    Google Scholar 

  35. Yin Y et al (2019) QoS prediction for service recommendation with deep feature learning in edge computing environment. Mob Netw Appl

  36. Romano L et al (2011) A novel approach to QoS monitoring in the cloud. In: 2011 first international conference on data compression, communications and processing (CCP). IEEE

  37. Cicotti G et al (2015) How to monitor QoS in cloud infrastructures: the QoSMONaaS approach. Int J Comput Sci Eng 11(1):29–45

    Google Scholar 

  38. Cicotti G et al (2011) QoS monitoring in a cloud services environment: the SRT-15 approach. In: European conference on parallel processing. Springer

  39. Leitner P et al (2010) Runtime prediction of service level agreement violations for composite services. In: Service-oriented computing. ICSOC/ServiceWave 2009 workshops. Springer

  40. Ciciani B et al (2012) Automated workload characterization in cloud-based transactional data grids. In: 2012 IEEE 26th international parallel and distributed processing symposium workshops & PhD Forum (IPDPSW). IEEE

  41. Hussain W et al (2017) Formulating and managing viable SLAs in cloud computing from a small to medium service provider’s viewpoint: a state-of-the-art review. Inf Syst 71:240–259

    Google Scholar 

  42. Gao H et al (2018) Applying probabilistic model checking to financial production risk evaluation and control: a case study of Alibaba’s Yu’e Bao. IEEE Access 99:1–11

    Google Scholar 

  43. Hussain W et al (2015) Profile-based viable service level agreement (sla) violation prediction model in the cloud. In: 2015 10th international conference on P2P, parallel, grid, cloud and internet computing (3PGCIC). IEEE, Krakow, pp 268–272

    Google Scholar 

  44. Hussain W et al (2016) Provider-based optimized personalized viable SLA (OPV-SLA) framework to prevent SLA violation. Comput J 59(12):1760–1783

    Google Scholar 

  45. Mustafa S et al (2018) SLA-aware energy efficient resource management for cloud environments. IEEE Access 6:15004–15020

    Google Scholar 

  46. Cheetham W, Varma A, Goebel K (2001) Case-based reasoning at general electric. In: FLAIRS Conference

    Google Scholar 

  47. Meland PH et al (2014) Expressing cloud security requirements for slas in deontic contract languages for cloud brokers. Int J Cloud Comput 3(1):69–93

    Google Scholar 

  48. Hussain W et al (2018) Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs. Futur Gener Comput Syst 89:464–477

    Google Scholar 

  49. Hussain W et al (2018) Risk-based framework for SLA violation abatement from the cloud service provider’s perspective. Comput J 61(9):1306–1322

    Google Scholar 

  50. Brandic I et al (2010) Laysi: a layered approach for sla-violation propagation in self-manageable cloud infrastructures. In: 2010 IEEE 34th annual computer software and applications conference workshops (COMPSACW). IEEE

  51. Emeakaroha VC et al (2012) Casvid: Application level monitoring for sla violation detection in clouds. In: 2012 IEEE 36th annual computer software and applications conference (COMPSAC). IEEE

  52. Mosallanejad A, Atan R (2013) HA-SLA: a hierarchical autonomic SLA model for SLA monitoring in cloud computing. J Softw Eng Appl 6(03):114

    Google Scholar 

  53. Katsaros G et al (2012) A self-adaptive hierarchical monitoring mechanism for Clouds. J Syst Softw 85(5):1029–1041

    Google Scholar 

  54. Sun Y et al (2013) SLA detective control model for workflow composition of cloud services. In: 2013 IEEE 17th international conference on computer supported cooperative work in design (CSCWD). IEEE

  55. Cardellini V et al (2011) Sla-aware resource management for application service providers in the cloud. In: 2011 first international symposium on network cloud computing and applications (NCCA). IEEE

  56. Schmieders, E., et al., Combining SLA prediction and cross layer adaptation for preventing SLA violations. 2011

    Google Scholar 

  57. Noor TH, Sheng QZ (2011) Trust as a service: a framework for trust management in cloud environments. In: Web information system engineering–WISE 2011. Springer, pp 314–321

  58. Fan W, Perros H (2013) A reliability-based trust management mechanism for cloud services. In: 2013 12th IEEE international conference on trust, security and privacy in computing and communications (TrustCom). IEEE

  59. Hussain W, Hussain FK, Hussain OK (2015) Comparative analysis of consumer profile-based methods to predict SLA violation. In: 2015 IEEE international conference on Fuzzy systems (FUZZ-IEEE). IEEE

  60. Hussain W, Hussain F, Hussain O (2016) Allocating optimized resources in the cloud by a viable SLA model. In: 2016 IEEE international conference on Fuzzy systems (FUZZ-IEEE). IEEE

  61. Hussain W, Hussain FK, Hussain OK (2016) Risk management framework to avoid SLA violation in cloud from a provider’s perspective. In: International conference on P2P, parallel, grid, cloud and internet computing. Springer

  62. Naderpour M, Lu J, Zhang G (2014) An intelligent situation awareness support system for safety-critical environments. Decis Support Syst 59:325–340

    Google Scholar 

  63. Markowski AS et al (2011) Application of fuzzy logic to explosion risk assessment. J Loss Prev Process Ind 24(6):780–790

    Google Scholar 

  64. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc

  65. Herlocker JL et al (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM

  66. Wang J, De Vries AP, Reinders MJ (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. ACM

  67. Zhou H et al (2016) A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing 216:208–215

    Google Scholar 

  68. Lin LI-K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics:255–268

  69. Adler J, Parmryd I (2010) Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander’s overlap coefficient. Cytometry A 77(8):733–742

    Google Scholar 

  70. Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput C-26(12):1182–1191

    MATH  Google Scholar 

  71. Nazir S, Colombo S, Manca D (2013) Testing and analyzing different training methods for industrial operators: an experimental approach. In: Andrzej K, Ilkka T (eds) Computer aided chemical engineering. Elsevier, pp 667–672

  72. Kaur A, Kaur A (2012) Comparison of mamdani-type and sugeno-type fuzzy inference systems for air conditioning system. Int J Soft Comput Eng 2(2):323–325

    Google Scholar 

  73. Zhang Y, Zheng Z, Lyu MR (2011) WSPred: A time-aware personalized QoS prediction framework for Web services. In: 2011 IEEE 22nd international symposium on software reliability engineering (ISSRE). IEEE

  74. Sohaib O et al (2019) Cloud computing model selection for E-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method. Com Ind Eng 132:47–58

    Google Scholar 

  75. Tzeng G-H, Shen K-Y (2017) New concepts and trends of hybrid multiple criteria decision making. CRC Press, Boca Raton

    MATH  Google Scholar 

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Correspondence to Honghao Gao.

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Hussain, W., Sohaib, O., Naderpour, M. et al. Cloud Marginal Resource Allocation: A Decision Support Model. Mobile Netw Appl 25, 1418–1433 (2020).

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  • Cloud computing
  • Cloud resource management
  • Decision support system
  • Marginal resources
  • Fuzzy systems
  • SLA violation
  • Violation penalty