Applying self-aggregation to load balancing: experimental results
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
Biased random walks on resource network graphs for load balancing
The Journal of Supercomputing
Towards resource-aware network of networks
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Host selection through collective decision
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Autonomic nature-inspired eco-systems
Transactions on Computational Science XV
An improved biased random sampling algorithm for load balancing in cloud based systems
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Self-organizing agent communities for autonomic resource management
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive model learning for continual verification of non-functional properties
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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In this paper, we investigate the global self-aggregation dynamics arising from local decision-based rewiring of an overlay network, used as an abstraction for an autonomic service-oriented architecture. We measure the ability of a selected set of local rules to foster self-organization of what is originally a random graph into a structured network. Scalability issues with respect to the key parameters of system size and diversity are extensively discussed. Conflicting goals are introduced, in the form of a population of nodes actively seeking to acquire neighbours of a type different from their own, resulting in decreased local homogeneity. We show that a ‘secondary’ self-organization process ensues, whereby nodes spontaneously cluster according to their implicit objective. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. We demonstrate that in this context, an overlay rewiring process based purely on local decisions and interactions can result in efficient load-balancing without central planning. We conclude by discussing the implications of our findings for the design of future distributed applications, the likely influence of other factors and of extreme parameter values on the ability of the system to self-organize and the potential improvements to our framework.