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Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

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Abstract

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.

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References

  1. Kumar M, Dubey K, Sharma SC (2018) Elastic and flexible deadline constraint load balancing algorithm for cloud computing. Procedia Comput Sci 125:717–724

    Article  Google Scholar 

  2. Chen H, Liu G, Yin S, Liu X, Qiu D (2018) Erect: energy-efficient reactive scheduling for real-time tasks in heterogeneous virtualized clouds. J Comput Sci 28:416–425

    Article  Google Scholar 

  3. Barroso L, Holzle U (2007) The case for energy proportional computing. IEEE Comput 40(12):33–37

    Article  Google Scholar 

  4. Frîncu ME (2012) Scheduling highly available applications on cloud environments. Future Gener Comput Syst 32(6):138–153

    Google Scholar 

  5. Ramezani F, Hussain FK (2013) Task-based system load balancing in cloud computing using particle swarm optimization. Int J Parallel Prog 42(5):739–754

    Article  Google Scholar 

  6. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948

  7. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on systems, man, and cybernetics, pp. 4104–4108

  8. Babu D, Venkata P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303

    Article  Google Scholar 

  9. Pacini E, Mateos C, Garino CG (2015) Balancing throughput and response time in online scientific clouds via ant colony optimization (SP2013/2013/00006). Adv Eng Softw 84:31–47

    Article  Google Scholar 

  10. Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055

    Article  MATH  Google Scholar 

  11. Dasgupta K, Mandal B, Dutta P, Mondal JK, Dam S (2013) A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol 10:340–347

    Article  Google Scholar 

  12. Chen H, Wang F, Helian N, Akanmu G (2013) User-priority guided min-min scheduling algorithm for load balancing in cloud computing. In: National conference on parallel computing technologies, Bangalore, KA, pp 1–8

  13. Elzeki OM, Reshad MZ, Cloud MA (2012) Improved max–min algorithm in cloud computing. Int J Comput Tasks 50:22–27

    Google Scholar 

  14. Devi DC, Uthariaraj VR (2016) Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Sci World J 2016:1–14

    Article  Google Scholar 

  15. Kumar M, Sharma SC (2017) Dynamic load balancing algorithm for balancing the workload among virtual machine in cloud computing. Procedia Comput. Sci. 115:322–329

    Article  Google Scholar 

  16. Kumar M, Sharma SC (2017) Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. J Comput Electr Eng (CAEF). https://doi.org/10.1016/j.compeleceng.2017.11.018

    Article  Google Scholar 

  17. Gill S, Channa I (2015) Q-aware: quality of service based cloud resource provisioning. Comput Electr Eng 47:138–160

    Article  Google Scholar 

  18. Khargharia B, Hariri S, Szidarovszky F, Houri M, Rewini H, Khan S, Ahmad I, Yousif M (2007) Autonomic power & performance management for large-scale data centers. In: International parallel and distributed processing symposium, pp 1–8

  19. Sheikh H, Ahamd I, Wang Z, Ranka S (2012) An overview and classification of thermal-aware scheduling techniques for multi-core processing systems. Sustain Comput Inform Syst 2(3):151–169

    Google Scholar 

  20. Sheikh H, Ahmad I, Fan D (2015) An evolutionary technique for performance-energy-temperature optimized scheduling of parallel tasks on multi-core processors. IEEE Trans Parallel Distrib Syst 27(3):668–681

    Article  Google Scholar 

  21. Zhang Y, Gong D, Ding Z (2011) Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer. Expert Syst Appl 38(11):13933–13941

    Google Scholar 

  22. Gong D, Sun J, Ji X (2013) Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems. Inf Sci 233:141–161

    Article  MathSciNet  MATH  Google Scholar 

  23. Han Y, Gong D, Sun X (2015) A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking. Eng Optim 47(7):927–946

    Article  MathSciNet  Google Scholar 

  24. Zhang Y, Gong D, Cheng J (2017) Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 14(1):64–75

    Article  Google Scholar 

  25. Meng Z, Pan J (2016) Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl-Based Syst 97:144–157

    Article  MathSciNet  Google Scholar 

  26. Meng Z, Pan J, Kong L (2018) Parameters with adaptive learning mechanism (PALM) for the enhancement of differential evolution. Knowl-Based Syst 141:92–112

    Article  Google Scholar 

  27. Pan J, Kong L, Sung T, Tsai P, Snasel V (2018) A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set. J Internet Technol 19(4):1111–1118

    Google Scholar 

  28. Pan J, Kong L, Sung T, Tsai P, Snasel V (2018) α-Fraction first strategy for hierarchical model in wireless sensor networks. J Internet Technol 19(6):1717–1726

    Google Scholar 

  29. Meng Z, Pan J (2019) HARD-DE: hierarchical archive based mutation strategy with depth information of evolution for the enhancement of differential evolution on numerical optimization. IEEE Access 7:12832–12854

    Article  Google Scholar 

  30. Zhang YD, Zhang Y, Lv Y, Hou X, Liu F, Jia W, Yang M, Phillips P, Wang S (2017) Alcoholism detection by medical robots based on Hu moment invariants and predator–prey adaptive-inertia chaotic particle swarm optimization. Comput Electr Eng 63:126–138

    Article  Google Scholar 

  31. Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz JM (2018) Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J Alzheimers Dis 65(3):855–869

    Article  Google Scholar 

  32. Zuo X, Zhang G, Tan W (2014) Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans Autom Sci Eng 11(2):564–573

    Article  Google Scholar 

  33. Verma A, Kaushal S (2017) A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput 62:1–19

    Article  MathSciNet  Google Scholar 

  34. Cho KM, Tsai PW, Tsai CW, Yang CS (2014) A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput Appl 26(6):1297–1309

    Article  Google Scholar 

  35. Gill SS, Buyya R, Chana I, Singh M, Abharam A (2018) BULLET: particle swarm optimization based scheduling technique for provisioned cloud resources. J Netw Syst Manag 26(2):361–400

    Article  Google Scholar 

  36. Islam MdJ, Li X, Mei Y (2017) A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Appl Soft Comput 59:182–196

    Article  Google Scholar 

  37. Naeem M, Pareek U, Lee DC (2012) Swarm intelligence for sensor selection problems. IEEE Sens J 12(8):2577–2585

    Article  Google Scholar 

  38. Lin JCW, Yang L, Viger PF, Hong TP, Voznak M (2016) A binary PSO approach to mine high-utility itemsets. Soft Comput 21(17):1–19

    Google Scholar 

  39. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. Int Conf Syst Man Cybern 5:4104–4108

    Google Scholar 

  40. Bansal JC, Deep K (2012) A modified binary particle swarm optimization for knapsack problems. Appl Math Comput 218(22):11042–11061

    MathSciNet  MATH  Google Scholar 

  41. Mirjalili S, Lewis A (2013) S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14

    Article  Google Scholar 

  42. Hua LJ, Hua YR, Hua SS (2011) The analysis of binary particle swarm optimization. J Nanjing Univ (Nat Sci) 47:504–514

    Google Scholar 

  43. Kumar M, Sharma SC (2018) PSO-COGENT: cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain Comput Inform Syst 19:147–164

    Google Scholar 

  44. Gill SS, Chana I, Singh M, Buyya R (2017) CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing. Cluster Comput 21:1203–1241

    Article  Google Scholar 

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Authors and Affiliations

  1. NIT Jalandhar, Jalandhar, India

    Mohit Kumar

  2. IIT Roorkee, Roorkee, India

    S. C. Sharma

  3. MIET Meerut, Meerut, India

    Shalini Goel

  4. SRM University, Amravati, Andhra pradesh, India

    Sambit Kumar Mishra

  5. MJPRU Bareilly, Bareilly, India

    Akhtar Husain

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  1. Mohit Kumar
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  2. S. C. Sharma
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  3. Shalini Goel
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  4. Sambit Kumar Mishra
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  5. Akhtar Husain
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Correspondence to Mohit Kumar.

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Kumar, M., Sharma, S.C., Goel, S. et al. Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm. Neural Comput & Applic 32, 18285–18303 (2020). https://doi.org/10.1007/s00521-020-04955-y

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