Eventually consistent systems can be made more consistent by reducing the time until a write is fully replicated, thereby improving global update visibility. While gossip-based anti-entropy methods scale well, random selection of anti-entropy partners is less than efficient. Moreover, while eventual consistency may be consistent enough in a single data center, geographic replication increases visibility latency and leads to externally observable inconsistencies. In this paper, we explore an improvement to pairwise, bilateral anti-entropy; instead of uniform random selection, we introduce reinforcement learning mechanisms to assign selection probabilities to replicas most likely to have information. The result is more efficient replication, faster visibility, and stronger eventual consistency while maintaining high availability and partition tolerance.

Groups of strongly consistent devices can efficiently order events under ideal (data center) conditions, but become less effective in dynamic and heterogeneous environments. Weakly consistent devices efficiently tolerate both faults and dynamic conditions but are slow to converge on a single ordering of system events. We propose "federated consistency", which combines the strengths of both approaches into a single protocol. Federated groups use a strongly consistent inner core of devices to maintain a totally ordered, fault-tolerant sequence of events. A cloud of weakly-consistent devices disseminates orderings and enables progress despite varying connectivity and partitions. Though the constituent sub-protocols take different (nearly opposite) approaches to resolving conflicts; we show that expanding distributed version vectors with a forte component allows them to inter-operate effectively.

We introduce Hierarchical Consensus, an approach to generalizing consensus that allows us to scale groups beyond a handful of nodes, across wide areas. Hierarchical Consensus increases the availability of consensus groups by partitioning the decision space and nominating distinct leaders for each partition. Partitions eliminate distance by allowing decision-making to be co-located with replicas that are responding to accesses. A root quorum guarantees global consistency and fault tolerance. Hierarchical consensus is flexible locally, but improves upon prior approaches by balancing load, allowing fast replication across wide areas, and enabling consensus across large (> 100) systems of devices.

Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or flocks that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.

In this paper, we study an emerging type of wireless network - Hybrid Wireless Networks (HWNs). A HWN consists of an infrastructure wireless network (e.g., a cellular network) and several ad hoc nodes (such as a Mobile ad hoc network). Forming a HWN is a very cost-effective way to improve wireless coverage and the available bandwidth to users. Specifically, in this work we investigate the issue of bandwidth allocation in multi-hop HWNs. We propose three efficient bandwidth allocation schemes for HWNs: top-down, bottom-up, and auction-based allocation schemes. In order to evaluate the bandwidth allocation schemes, we develop a simulated HWN environment. Our simulation results show that the proposed schemes achieve good performance: the schemes can achieve maximum revenue/utility in many cases, while also providing fairness. We also show that each of the schemes has merit in different application scenarios.