Abstract
To improve the efficiency of peer-to-peer (P2P) systems while adapting to changing environmental conditions, static peer-to-peer protocols can be replaced by adaptive plans. The resulting systems are inherently complex, which makes their development and characterization a challenge for traditional methods. Here we propose the design and analysis of adaptive P2P systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. These measures allow the evaluation of adaptive P2P systems and thus can be used to guide their design. We evaluate the proposal with a P2P computing system provided with adaptation mechanisms. We show the evolution of the system with static and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, which correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less “aggressive”, the system may be more stable, but the optimal performance may not be achieved.
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References
Gershenson C, Heylighen F (2005) How can we think the complex? In: Richardson K. (ed) Managing organizational complexity: philosophy, theory and application. Information Age Publishing, Charlotte, pp 47–61
Gershenson C (2013) The implications of interactions for science and philosophy. Found Sci 18(4):781–790
Gershenson C (2007) Design and control of self-organizing systems. CopIt Arxives, Mexico
Psaier H, Dustdar S (2011) A survey on self-healing systems: approaches and systems. Computing 91(1):43–73
Dobson S, Sterritt R, Nixon P, Hinchey M (2010) Fulfilling the vision of autonomic computing. IEEE Comput Mag 43(1): 35–41
Gershenson C, Fernández N (2012) Complexity and information: measuring emergence, self-organization, and homeostasis at multiple scales. Complexity 18(2):29–44
Fernández N, Maldonado C, Gershenson C (2014) Information measures of complexity, emergence, self-organization, homeostasis, and autopoiesis. In: Prokopenko M (ed) Guided self-organization: inception. Springer, Berlin, pp 19–51
Amoretti M (2013) Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework. Genet Programm Evolvable Mach 14(2):127–153
IBM: An architectural blueprint for autonomic computing. http://www-03.ibm.com/autonomic/pdfs/AC%20Blueprint%20White%20Paper%20V7.pdf (2003). Accessed 17 Jan 2015
Huebscher MC, McCann JA (2008) A survey of autonomic computing — degrees, models, and applications. ACM Comput Surv 40(3):7
Müller-Schloer C, Schmeck H (2011) Organic computing - quo vadis? In: Müller-Schloer C, Schmeck H, Ungerer T (eds) Organic computing — a paradigm shift for complex systems. Basel, Birkhäuser Verlag, pp 615–627
Bruni R, Corradini A, Gadducci F, Lluch Lafuente A, Vandin A (2012) A conceptual framework for adaptation. In: Proc. 15th int.’l conf. on the fundamentals of software engineering (FASE’12), pp 240–254
Nakano T, Suda T (2005) Self-organizing network services with evolutionary adaptation. IEEE Trans Neural Netw 16(5):1269–1278
Hales D (2004) From selfish nodes to cooperative networks — emergent link-based incentives in peer-to-peer networks. In: Proceedings 4th IEEE international conference on peer-to-peer computing (P2P’04), pp 151–158
Tyson G, Grace P, Mauthe A, Kaune S (2008) The survival of the fittest: an evolutionary approach to deploying adaptive functionality in peer-to-peer systems. In: Proceedings 7th workshop on reflective and adaptive middleware (ARM’08), pp 23–28
Amoretti M (2009) A survey of peer-to-peer overlay schemes: effectiveness, efficiency and security. Recent Patents Comput Sci 2(3):195–213
Cooper BF, Garcia-Molina H (2006) SIL: a model for analyzing scalable peer-to-peer search networks. Comput Netw 50(13):2380–2400
Karakaya M, Korpeoglu I, Ulusoy O (2008) Counteracting free riding in peer-to-peer networks. Comput Netw 52((3) 22):675–694
Esposito F, Matta I, Bera D, Michiardi P (2011) On the impact of seed scheduling in peer-to-peer networks. Comput Netw 55(15):3303–3317
Chen Y, Le Merrer E, Li Z, Liu Y, Simon G (2012) OAZE: A network-friendly distributed zapping system for peer-to-peer IPTV. Comput Netw 56(1):365–377
Onana Alima L, El-Ansary S, Brand P, Haridi S (2003) DKS(N,k,f): a family of low communication, scalable and fault-tolerant infrastructures for p2p communications. In: Proceedings 3rd IEEE/ACM inter symposium on cluster computing and the grid (CCGRID’03), pp 344–350
Paechter B, Back T, Schoenauer M, Sebag M, Eiben AE, Merelo JJ, Fogarty TC (2000) A distributed resource evolutionary algorithm machine (DREAM). In: Proceedings IEEE congress on evolutionary computation (CEC 2000), pp 951–958
Prokopenko M, Boschetti F, Ryan AJ (2009) An information-theoretic primer on complexity, self-organisation and emergence. Complexity 15(1):11–28
Langton C (1990) Computation at the edge of chaos: Phase transitions and emergent computation. Physica D 42:12–37
Kauffman SA (1993) The origins of order. Oxford University Press
Gershenson C (2007) The World as evolving information. In: Proceedings International conference on complex systems (ICCS), pp 100–115
Gershenson C, Heylighen F (2003) When can we call a system self-organizing? In: Proceedings 7th European conference on advances in artificial life (ECAL 2003), pp 606–614
Eugster PT, Guerraoui R, Kermarrec A-M, Massoulie L (2004) From epidemics to distributed computing. IEEE Comput 37(5)
Ahi E, Caglar M, Ozkasap O (2007) Stepwise fair-share buffering underneath bio-inspired P2P data dissemination. In: Proceedings 6th international symposium on parallel and distributed computing (ISPDC’07), pp 177–184
The Gnutella Developer Forum (GDF): the annotated gnutella protocol specification v0.4. http://rfc-gnutella.sourceforge.net/developer/stable/index.html (2003). Accessed 17 Jan 2015
Laredo JL, Eiben AE, Steen M, Merelo JJ (2010) EvAg: a scalable peer-to-peer evolutionary algorithm. Genet Programm Evolvable Mach 11(2):227–246
Amoretti M (2009) A framework for evolutionary peer-to-peer overlay schemes. In: Proceedings European workshops on the applications of evolutionary computation (EvoWorkshops 2009), pp 61–70
Amoretti M, Agosti M, Zanichelli F (March 2009) DEUS: a discrete event universal simulator. In: 2nd ICST/ACM International conference on simulation tools and techniques (SIMUTools 2009). Roma
Amoretti M, Picone M, Zanichelli F, Ferrari G (2013) Simulating mobile and distributed systems with DEUS and ns-3. In: Proceedings international conference on high performance computing and simulation (HPCS 2013), pp 107–114
Montresor A, Jelasity M (2009) PeerSim: a scalable P2P simulator. In: Proceedings of the 9th international conference on peer-to-peer (P2P’09), pp 99–100
Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
Zubillaga D, Cruz G, Aguilar LD, Zapotécatl J, Fernández N, Aguilar J, Rosenblueth DA, Gershenson C (2014) Measuring the complexity of self-organizing traffic lights. Entropy 16(5):2384–2407
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Amoretti, M., Gershenson, C. Measuring the complexity of adaptive peer-to-peer systems. Peer-to-Peer Netw. Appl. 9, 1031–1046 (2016). https://doi.org/10.1007/s12083-015-0385-4
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DOI: https://doi.org/10.1007/s12083-015-0385-4