Failover in cellular automata

A cellular automata (CA) configuration is constructed that exhibits emergent failover. The configuration is based on standard Game of Life rules. Gliders and glider-guns form the core messaging structure in the configuration. The blinker is represented as the basic computational unit, and it is shown how it can be recreated in case of a failure. Stateless failover using primary-backup mechanism is demonstrated. The details of the CA components used in the configuration and its working are described, and a simulation of the complete configuration is also presented.Comment: 16 pages, 15 figures and associated video at http://dl.dropbox.com/u/7553694/failover_demo.avi and simulation at http://dl.dropbox.com/u/7553694/failover_simulation.ja

Cloud Computing and Cloud Automata as A New Paradigm for Computation

Cloud computing addresses how to make right resources available to right computation to improve scaling, resiliency and efficiency of the computation. We argue that cloud computing indeed, is a new paradigm for computation with a higher order of artificial intelligence (AI), and put forward cloud automata as a new model for computation. A high-level AI requires infusing features that mimic human functioning into AI systems. One of the central features is that humans learn all the time and the learning is incremental. Consequently, for AI, we need to use computational models, which reflect incremental learning without stopping (sentience). These features are inherent in reflexive, inductive and limit Turing machines. To construct cloud automata, we use the mathematical theory of Oracles, which include Oracles of Turing machines as its special case. We develop a hierarchical approach based on Oracles with different ranks that includes Oracle AI as a special case. Discussing a named-set approach, we describe an implementation of a high-performance edge cloud using hierarchical name-oriented networking and Oracle AI-based orchestration. We demonstrate how cloud automata with a control overlay allows microservice network provisioning, monitoring and reconfiguration to address non-deterministic fluctuations affecting their behavior without interrupting the overall evolution of computation

Replicated execution of workflows

Workflows are the de facto standard for managing and optimizing business processes. Workflows allow businesses to automate interactions between business locations and partners residing anywhere on the planet. This, however, requires the workflows to be executed in a distributed and dynamic environment, where device and communication failures occur quite frequently. In case that a workflow execution becomes unavailable through such failures, the business operations that rely on the workflow might be hindered or even stopped, implying the loss of money. Consequently, availability is a key concern when using workflows in dynamic environments. In this thesis, we propose replication schemes for workflow engines to ensure the availability of the workflows that are executed by these engines. Of course, a workflow that is executed by a replicated workflow engine has to yield the same result as a non-replicated execution of that workflow. To this end, we formally define the equivalence of a replicated and a non-replicated execution called Single-Execution-Equivalence. Subsequently, we present replication schemes for both imperative and declarative workflow languages. Imperative workflow languages, such as the Web Service Business Process Execution Language (WS-BPEL), specify the execution order of activities through an ordering relation and are the predominant way of specifying workflow models. We implement a proof-of-concept for demonstrating the compatibility of our replication schemes with current (imperative) workflow technology. Declarative workflow languages provide greater flexibility by allowing the reordering of the activities within a workflow at run-time. We exploit this by executing differently ordered replicas on several nodes in the network for improving availability further

Executable clinical models for acute care

Medical errors are the third leading cause of death in the U.S., after heart disease and cancer, causing at least 250,000 deaths every year. These errors are often caused by slips and lapses, which include, but are not limited to delayed diagnosis, delayed or ineffective therapeutic interventions, and unintended deviation from the best practice guidelines. These situations may occur more often in acute care settings, where the staff are overloaded, under stress, and must make quick decisions based on the best available evidence. An \textit{integrated clinical guidance system} can reduce such medical errors by helping medical staff track and assess patient state more accurately and adapt the care plan according to the best practice guidelines. However, a main prerequisite for developing a guideline system is to create computer interpretable representations of the clinical knowledge. The main focus of this thesis is to develop executable clinical models for acute care. We propose an organ-centric pathophysiology-based modeling paradigm, in which we translate the medical text into executable interactive disease and organ state machines. We formally verify the correctness and safety of the developed models. Afterward, we integrate the models into a best practice guidance system. We study the cardiac arrest and sepsis case studies to demonstrate the applicability of proposed modeling paradigm. We validate the clinical correctness and usefulness of our model-driven cardiac arrest guidance system in an ACLS training class. We have also conducted a preliminary clinical simulation of our model-driven sepsis screening system

Embryomorphic Engineering: Emergent innovation through evolutionary development

Embryomorphic Engineering, a particular instance of Morpho-genetic Engineering, takes its inspiration directly from biological development to create new hardware, software or network architectures by decentralized self-assembly of elementary agents. At its core, it combines three key principles of multicellular embryogenesis: chemical gradient di usion (providing positional information to the agents), gene regulatory networks (triggering their diferentiation into types, thus patterning), and cell division (creating structural constraints, thus reshaping). This chapter illustrates the potential of Embryomorphic Engineering in di erent spaces: 2D/3D physical swarms, which can nd applications in collective robotics, synthetic biology or nan- otechnology; and nD graph topologies, which can nd applications in dis- tributed software and peer-to-peer techno-social networks. In all cases, the speci c genotype shared by all the agents makes the phenotype's complex architecture and function modular, programmable and reproducible

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing