A Distributed Approach for Fault Mitigation in Large Scale Distributed Systems

In a large scale real-time distributed system, a large number of components and the time criticality of tasks can contribute to complex situations. Providing predictable and reliable service is a paramount interest in such a system. For example, a single point failure in an electric grid system may lead to a widespread power outage like the Northeast Blackout of 2003. System design and implementation address fault avoidance and mitigation. However, not all faults and failures can be removed during these phases, and therefore run-time fault avoidance and mitigation are needed during the operation. Timing constraints and predictability of the system behavior are important concerns in a large scale system as well. This dissertation proposes several distributed fault tolerance mechanisms using multi-agent technologies to predict and mitigate faults with various frequencies and severities. Some faults are frequently observed over time and some are not. In general, frequent fault types often cause relatively less severe consequences. Rare faults, however, are extremely difficult to predict, yet the consequences can be catastrophic. A rare fault -- often indicated by repeated doses of common faults -- causes severe harm. In our preliminary study, we design distributed rational agents using a probabilistic prediction mechanism to discover faults in the CMS experiments at CERN. All fault-mitigating activities of the agents and application tasks are guaranteed by the urgency-based priority scheduling policy with multiple steps of feasibility tests. The experiment shows that the distributed approach provides 15% more system availability than centralized approaches. This dissertation also explores the problem of predicting rare events. Many adaptive fault tolerant mechanisms attempt to predict faults through learning from data. However, in order to train the system, we need a significant amount of training data, which is not easily available for rare fault events. We use the PNNL (Pacific Northwest National Laboratory) system failure data collected from about 1,000 nodes over 4 years. We find that the severity of observed fault events is power-law distributed and there are certain associations among these events. Based on the power-law observation, we generate training data for the machine learning algorithm developed in this dissertation. The algorithm incorporates the power-law distribution principle, Bayesian inference, and logistic regression to predict rare events as well as common ones. The logistic regression is used to predict the probability of each type of events and the Bayesian inference is used for finding associations among events. A new learning algorithm is deployed with fully distributed agents using a rational decision model. The simulation study based on the PNNL data shows that the new prediction algorithm provides 15$\%$ better system availability than the prediction using the simple update method that was used in our preliminary study; and it achieves more than 10 times less system loss caused by rare faults. Finally, we developed a comprehensive simulation library, named SWARM-eTOSSIM for cyber-physical systems research. The library provides a framework suitable for simulating power-aware real-time distributed networked systems with powerful simulation controls and graphical interface. We downsized the new fault-mitigation mechanism so that it can be ported to devices with limited resources, such as sensor network elements

Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Agent-based Social Psychology: from Neurocognitive Processes to Social Data

Moral Foundation Theory states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations. The use of functional imaging techniques has revealed a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information that is correlated to political affiliation. Here we characterize the collective behavior of an agent-based model whose inter individual interactions due to information exchange in the form of opinions are in qualitative agreement with experimental neuroscience data. The main conclusion derived connects the existence of diversity in the cognitive strategies and statistics of the sets of moral foundations and suggests that this connection arises from interactions between agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data on conformity and learning processes, presents statistical signatures consistent with moral judgment patterns of conservatives and liberals as obtained by survey studies of social psychology.Comment: 11 pages, 4 figures, 2 C codes, to appear in Advances in Complex System

Correlated adaptation of agents in a simple market: a statistical physics perspective

We discuss recent work in the study of a simple model for the collective behaviour of diverse speculative agents in an idealized stockmarket, considered from the perspective of the statistical physics of many-body systems. The only information about other agents available to any one is the total trade at time steps. Evidence is presented for correlated adaptation and phase transitions/crossovers in the global volatility of the system as a function of appropriate information scaling dimension. Stochastically controlled irrationally of individual agents is shown to be globally advantageous. We describe the derivation of the underlying effective stochastic differential equations which govern the dynamics, and make an interpretation of the results from the point of view of the statistical physics of disordered systems.Comment: 15 Pages. 5 figure