Current Cloud Computing is primarily based on proprietary data centers, where hundreds of thousands of dedicated servers are setup to host the cloud services. In addition to the huge number of dedicated servers deployed in data centers, there are billions of underutilized Personal Computers (PCs), usually used only for a few hours per day, owned by individuals and organizations worldwide. The vast untapped compute and storage capacities of the underutilized PCs can be consolidated as alternative cloud fabrics to provision broad cloud services, primarily infrastructure as a service. This approach, thus referred to as "no data center" approach, complements the data center based cloud provision model. In this paper, we present our opportunistic Cloud Computing system, called cuCloud, that runs on scavenged resources of underutilized PCs within an organization/community. Our system demonstrates that the "no data center" solution indeed works. Besides proving our concept, model, and philosophy, our experimental results are highly encouraging.
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Received 10 Jan, 2020, Accepted 06 Apr, 2020, Published 02 Apr, 2020
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37. Zamli, K.Z., et al.: An experimental study of hyper-heuristic selection and acceptance
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2. Kirkpatrick, S.: Optimization by simulated annealing: quantitative studies. J. Stat. Phys. 34
(6), 975–986 (1984)
3. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)
4. Dorigo, M., et al.: Ant colony optimization and swarm intelligence. In: 6th International
Conference Ants - Theoretical Computer Science and General Issues. Springer, Berlin (2008)
5. Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia
of Machine Learning, pp. 760–766. Springer, Berlin (2011)
6. Feoktistov, V.: Differential Evolution. Springer, Berlin (2006)
7. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering
optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194 (36), 3902–3933 (2005)
8. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based
Syst. 96, 120–133 (2016)
9. Pham, D., et al:. The bees algorithm–a novel tool for complex optimisation. In: International
Conference on Intelligent Production Machines and Systems (2011)
10. Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature and
Biologically Inspired Computing. IEEE (2009)
11. Yang, X.-S.: Flower pollination algorithm for global optimization. In: International
Conference on Unconventional Computing and Natural Computation. Springer (2012)
12. Yang, X.-S.: Firefly algorithm, Lévy flights and global optimization. In: Ellis, R., Petridis,
M. (eds.) Research and Development in Intelligent Systems, vol. 26, pp. 209–218. Springer,
Berlin (2010)
13. Nasser, A.B., Alsewari, A.R.A., Zamli, K.Z.: Parameter free flower algorithm based strategy
for pairwise testing. In: 7th International Conference on Software and Computer
Applications, Kuantan, Malaysia (2018)
14. Kuhn, D.R., Wallace, D.R., Gallo, J.A.M.: Software fault interactions and implications for
software testing. IEEE Trans. Softw. Eng. 30(6), 418–421 (2004)
15. Chen, X., et al.: Applying particle swarm optimization to pairwise testing. In: 2010 IEEE
34th Annual Computer Software and Applications Conference (COMPSAC). IEEE (2010)
16. Williams, A.W.: Determination of test configurations for pair-wise interaction coverage. In:
Testing of Communicating Systems, pp. 59–74. Springer, Berlin (2000)
17. Hartman, A., Raskin, L.: Problems and algorithms for covering arrays. Discrete Math. 284
(1), 149–156 (2004)
18. Mandl, R.: Orthogonal latin squares: an application of experiment design to compiler testing.
Commun. ACM 28(10), 1054–1058 (1985)
19. Bush, K.A.: Orthogonal arrays of index unity. Ann. Math. Stat. 23(3), 426–434 (1952)
20. Lei, Y., et al.: IPOG/IPOG-D: efficient test generation for multi-way combinatorial testing.
Softw. Test. Verif. Reliab. 18(3), 125–148 (2008)
21. Lei, Y., et al.: IPOG: a general strategy for t-way software testing. In: 14th Annual IEEE
International Conference and Workshops on the Engineering of Computer-Based Systems,
ECBS 2007. IEEE (2007)
22. Forbes, M., et al.: Refining the in-parameter-order strategy for constructing covering arrays.
J. Res. Nat. Inst. Stand. Technol. 113(5), 287 (2008)
23. Williams, A.: TConfig download page (2008)
24. Cohen, D.M., et al.: The AETG system: an approach to testing based on combinatorial
design. IEEE Trans. Softw. Eng. 23(7), 437–444 (1997)
25. Hartman, A., Klinger, T., Raskin, L.: IBM intelligent test case handler. Discrete Math. 284
(1), 149–156 (2010)
26. Jenkins, B.: Jenny Tool download page (2003). http://www.burtleburtle.net/bob/math.
Accessed 16 Dec 2014
27. Nie, C., Leung, H.: A survey of combinatorial testing. ACM Comput. Surv. (CSUR) 43(2),
11 (2011)
28. Shiba, T., Tsuchiya, T., Kikuno, T.: Using artificial life techniques to generate test cases for
combinatorial testing. In: Proceedings of the 28th Annual International Computer Software
and Applications Conference, COMPSAC 2004. IEEE (2004)
29. Colbourn, C.J., Cohen, M.B., Turban, R.: A deterministic density algorithm for pairwise
interaction coverage. In: IASTED Conference on Software Engineering. Citeseer (2004)
30. Ahmed, B.S., Zamli, K.Z.: A variable strength interaction test suites generation strategy
using particle swarm optimization. J. Syst. Softw. 84(12), 2171–2185 (2011)
31. Alsewari, A.R.A., Zamli, K.Z.: Design and implementation of a harmony-search-based
variable-strength tway testing strategy with constraints support. Inf. Softw. Technol. 54(6),
553–568 (2012)
32. Nasser, A.B., et al.: Pairwise test data generation based on flower pollination algorithm.
Malay. J. Comput. Sci. 30(3), 242–257 (2017)
33. Nasser, A.B., et al.: Assessing optimization based strategies for t-way test suite generation:
the case for flower-based strategy. In: 5th IEEE International Conference on Control
Systems, Computing and Engineering, Pinang, Malaysia (2015)
34. Ahmed, B.S., Abdulsamad, T.S., Potrus, M.Y.: Achievement of minimized combinatorial
test suite for configuration-aware software functional testing using the cuckoo search
algorithm. Inf. Softw. Technol. 66, 13–29 (2015)
35. Nasser, A.B., et al.: Hybrid flower pollination algorithm strategies for t-way test suite
generation. PLoS ONE 13(5), e0195187 (2018)
36. Zamli, K.Z., Alkazemi, B.Y., Kendall, G.: A tabu search hyper-heuristic strategy for t-way
test suite generation. Appl. Soft Comput. 44, 57–74 (2016)
37. Zamli, K.Z., et al.: An experimental study of hyper-heuristic selection and acceptance
mechanism for combinatorial t-way test suite generation. Inf. Sci. 399, 121–153 (2017)
38. Zamli, K.Z., et al.: A hybrid Q-learning sine-cosine-based strategy for addressing the
combinatorial test suite minimization problem. PLoS ONE 13(5), e0195675 (2018)
39. Zamli, K.Z., et al.: Fuzzy adaptive teaching learning-based optimization strategy for the
problem of generating mixed strength t-way test suites. Eng. Appl. Artif. Intell. 59, 35–50
(2017)