Enhancing the diversity of self-replicating structures using active self-adapting mechanisms

Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have attracted much attention, which aim to explore the logical mechanism underlying the replication of living things. In nature, competition is a common and spontaneous resource to drive self-replications, whereas most cellular-automaton-based models merely focus on some self-protection mechanisms that may deprive the rights of other artificial life (loops) to live. Especially, Huang et al. designed a self-adaptive, self-replicating model using a greedy selection mechanism, which can increase the ability of loops to survive through an occasionally abandoning part of their own structural information, for the sake of adapting to the restricted environment. Though this passive adaptation can improve diversity, it is always limited by the loop’s original structure and is unable to evolve or mutate new genes in a way that is consistent with the adaptive evolution of natural life. Furthermore, it is essential to implement more complex self-adaptive evolutionary mechanisms not at the cost of increasing the complexity of cellular automata. To this end, this article proposes new self-adaptive mechanisms, which can change the information of structural genes and actively adapt to the environment when the arm of a self-replicating loop encounters obstacles, thereby increasing the chance of replication. Meanwhile, our mechanisms can also actively add a proper orientation to the current construction arm for the sake of breaking through the deadlock situation. Our new mechanisms enable active self-adaptations in comparison with the passive mechanism in the work of Huang et al. which is achieved by including a few rules without increasing the number of cell states as compared to the latter. Experiments demonstrate that this active self-adaptability can bring more diversity than the previous mechanism, whereby it may facilitate the emergence of various levels in self-replicating structures

Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models

OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy

Queuing analysis and performance evaluation of workflow through WFQN

Performance prediction is one of the most important research topics of workflow. To investigate the performance of workflow systems in queuing condition, this paper extends traditional WF-net into WFQN (WF queuing network), by modeling tasks as FIFS (first-in-first-service) queues and the source place as the input of tokens following poisson arrival process. Analytical methods are introduced to evaluate the queue-length, wait-time and completion-duration. The case study (especially the case of airline ticket booking application) shows that WFQN can model real-world workflow-based applications effectively. Through Montecarlo simulations in the case study, we show analytical models are verified by simulative results. We also present a sensitivity analysis technique to identify performance bottle-necks of WFQN. This paper concludes with a comparison with relate work.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000247666400017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Software EngineeringEICPCI-S(ISTP)

Performance-aware cost-effective resource provisioning for future grid iot-cloud system

The rise of the future grid (FG) largely depends on the efficient integration of Internet of Things (IoT) and Cloud computing technologies. By utilizing information and control flows, FG can deliver power more effectively and be capable to handle events occurring anywhere in the grid network. However, maintaining such functions consumes a great deal of computational resource which brings an enormous operational cost to the grid owner. In this paper, we propose an integrated task scheduling and resource provisioning model for dynamically operating an IoT-Cloud system to reduce the overall operational cost. Our proposed approach uses a bipartite graph to model the communication pattern between sensor groups and decentralized cloud data centers and a Pareto distribution-based method to estimate the required resources considering capacity limitation and failure of the system in each data center. We formulate the integrated model as a constraint optimization problem over all sensor groups and data centers. We solve the problem with genetic algorithms due to problem complexity, and our extensive computer simulations and comparisons demonstrate the correctness and effectiveness of the proposed model in minimizing operational cost while satisfying system performance requirements

A Stochastic Model for Workflow QoS Evaluation

Quality (QoS) prediction is one of the most important research topics of workflow. In this paper, we propose a stochastic model to evaluate QoS (make-span, reliability and cost) of workflow systems based on QWF-net, which extends traditional WF-net by associating tasks with firing-rate, failure-rate and cost-coefficient. Through a case study, we show that our framework is capable of modeling real-world workflow-based application. Also, Monte-carlo simulation in the case study indicates our analytical methods are consistent with simulation. We also present a sensitivity analysis technique to identify QoS bottleneck.The paper concludes with a comparison between our approach and related work

A stochastic model for workflow qos evaluation

Abstract. Quality (QoS) prediction is one of the most important research topics of workflow. In this paper, we propose a stochastic model to evaluate QoS (make-span, reliability and cost) of workflow systems based on QWF-net, which extends traditional WF-net by associating tasks with firing-rate, failure-rate and cost-coefficient. Through a case study, we show that our framework is capable of modeling real-world workflow-based application. Also, Monte-carlo simulation in the case study indicates our analytical methods are consistent with simulation. We also present a sensitivity analysis technique to identify QoS bottleneck.The paper concludes with a comparison between our approach and related work. Keywords: Workflow, QoS, homogeneous continuous-time markovian process, monte-carlo simulation, sensitivity analysis Notatio