A novel and vibrant mobile computing application has been designed and developed that facilitates a well-organized far-off active interaction amongst its stakeholders constantly, mainly a parent and babysitter/nanny who heeds on updating about the current situation of a child. Basically, the key idea to provide an online childcare service aims towards constructing an app that supports the mothers to virtually monitor, track, manage and timely take sound care of their children from a distant place. On duty parents or guardians feel connected on having up-to-date information on the child status literally in the palm of a hand and can do the job tasks with utmost focus, contentment and peace of mind. Performance evaluation of the embedded features and functionality of the proposed strategy confirms its ease and utility on a regular day-to-day basis towards fostering quality parenthood for little kids. Furthermore, this research work serves as a usable reference in varied domains for the students engineering their own software applications.

The key intent of this work is to present a comprehensive comparative literature survey of the state-of-art in software agent-based computing technology and its incorporation within the modelling and simulation domain. The original contribution of this survey is two-fold: (1) Present a concise characterization of almost the entire spectrum of agent-based modelling and simulation tools, thereby highlighting the salient features, merits, and shortcomings of such multi-faceted application software; this article covers eighty five agent-based toolkits that may assist the system designers and developers with common tasks, such as constructing agent-based models and portraying the real-time simulation outputs in tabular/graphical formats and visual recordings. (2) Provide a usable reference that aids engineers, researchers, learners and academicians in readily selecting an appropriate agent-based modelling and simulation toolkit for designing and developing their system models and prototypes, cognizant of both their expertise and those requirements of their application domain. In a nutshell, a significant synthesis of Agent Based Modelling and Simulation (ABMS) resources has been performed in this review that stimulates further investigation into this topic.

Infrastructure as a Service (IaaS) is a pay-as-you go based cloud provision model which on demand outsources the physical servers, guest virtual machine (VM) instances, storage resources, and networking connections. This article reports the design and development of our proposed innovative symbiotic simulation based system to support the automated management of IaaS-based distributed virtualized data enter. To make the ideas work in practice, we have implemented an Open Stack based open source cloud computing platform. A smart benchmarking application "Cloud Rapid Experimentation and Analysis Tool (aka CBTool)" is utilized to mark the resource allocation potential of our test cloud system. The real-time benchmarking metrics of cloud are fed to a distributed multi-agent based intelligence middleware layer. To optimally control the dynamic operation of prototype data enter, we predefine some custom policies for VM provisioning and application performance profiling within a versatile cloud modeling and simulation toolkit "CloudSim". Both tools for our prototypes' implementation can scale up to thousands of VMs, therefore, our devised mechanism is highly scalable and flexibly be interpolated at large-scale level. Autonomic characteristics of agents aid in streamlining symbiosis among the simulation system and IaaS cloud in a closed feedback control loop. The practical worth and applicability of the multiagent-based technology lies in the fact that this technique is inherently scalable hence can efficiently be implemented within the complex cloud computing environment. To demonstrate the efficacy of our approach, we have deployed an intelligible lightweight representative scenario in the context of monitoring and provisioning virtual machines within the test-bed. Experimental results indicate notable improvement in the resource provision profile of virtualized data enter on incorporating our proposed strategy.

The focus of our work is the elicitation of communication network systems’ knowledge resources in a generic and reusable manner for providing the automated support to network management tasks. Key features of the proposed knowledge model are: ontological representation of static domain-content and management-expertise encoded as the core knowledge of distributed multi-agent architecture. Our emphasis has primarily been on the modularization of resource knowledge to facilitate its reuse in a flexible manner. To demonstrate the effectiveness of proposed scheme, we have implemented an experimental network in our laboratory, and the devised knowledge model has been deployed through multi-agent based middleware layer in the prototype system. A couple of application scenarios have been designed for testing with the prototype system. Experimental results confirm a marked reduction in the workloads of the network operator with our system providing the automated support to network management functions. Validation of the reusability/modifiability aspects of our system illustrates the flexible manipulation of knowledge fragments within diverse application contexts. We envisage our knowledge modeling approach as the first step towards the comprehensive knowledge acquisition, representation, and dissemination in the communication network management domain.

This paper presents a domain-ontology driven multi-agent based scheme for representing the knowledge of the communication network management system In the proposed knowledge-intensive It framework, the static domain-related concepts are articulated as the domain knowledge ontology The experiential knowledge for managing the network is represented as the fault-case reasoning models, and it is explicitly encoded as the core knowledge of multi-agent middleware layer as heuristic production-type rules These task-oriented management expertise manipulates the domain content and structure during the diagnostic sessions The agents' rules along with the embedded generic Java-based problem-solving algorithms and run-time log information. perform the automated management tasks For the proof of concept. an experimental network system has been implemented in our laboratory, and the deployment of some test-bed scenarios is performed Experimental results confirm a marked reduction in the management-overhead of the network administrator, as compared to the manual network management techniques. in terms of the time-taken and effort-done during a particular fault-diagnosis session Validation of the reusability/modifiability aspects of our system, illustrates the flexible manipulation of the knowledge fragments within diverse application contexts The proposed approach can be regarded as one of the pioneered steps towards representing the network knowledge via reusable domain ontology and intelligent agents for the automated network management support systems.

Summary The growing complexity of communication networks and their associated information overhead have made network management considerably difficult. This paper presents a novel Network Management Scheme based on the novel concept of Active Information Resources (AIRs). Many types of information are distributed in the complex network, and they are changed dynamically. Under the AIR scheme, each piece of information in a network is activated as an intelligent agent: an I-AIR. An I-AIR has knowledge and functionality related to its information. The I-AIRs autonomously detect run-time operational obstacles occurring in the network system and specify the failures' causes to the network administrator with their cooperation. Thereby, some network management tasks are supported. The proposed prototype system (AIRNMS) was implemented. Experimental results indicate that it markedly reduces the network administrator workload, compared to conventional network management methods.

This paper presents a domain-ontology driven multi-agent based scheme for representing the knowledge of the communication Network Management System (NMS). The scope of this work is focussed on the performance analysis and fault detection functional areas which are of prime importance as far as the management of the communication network systems is considered. The proposed network knowledge model has been constructed in accordance with the CommonKADS methodology, to facilitate its reusability and shareability. In the proposed knowledge-intensive framework, the static domain-related concepts are articulated as the domain knowledge ontology. The empirical knowledge for managing the network is represented as the fault-state causal reasoning models, and it is explicitly encoded as the core knowledge of multi-agent middleware layer as heuristic production-type rules. This task-oriented experiential knowledge manipulates the domain content and structure during the diagnostic sessions. The inference chains during the agents’ cooperative problem solving are supported by the java-based networking routines in conjunction with the run-time log information. The proposed approach can be regarded as one of the pioneered steps towards representing the network knowledge via reusable domain ontology and intelligent agents for the automated network management support systems.

A network system is a kind of large and complex systems and the network administrators are required exhaustive work to maintain the quality and functions of the network system. To reduce the load of administrators, systematic and intelligent facilities for the network management tasks should be realized and provided for administrators. In this paper, we propose a knowledge-based support method of the network management tasks using the active information resource (AIR) which has knowledges and functions for its information resource. Furthermore, a novel network management support system based on this method, called AIR-NMS, is also proposed by using the agent-based computing technologies. In the AIR-NMS, a lot of AIRs are defined and utilized in order to monitor and collect the status information of the network automatically. The AIRs are collaborated each other and inspect the behavior of the network. The network administrator can obtain useful supports for management in responses of AIR-NMS. Moreover, a prototype system is implemented to demonstrate and evaluate the essential functions of the AIR-NMS.

To cope with the complexity and broad scope of areas and functions that are involved with a network management system (NMS), a knowledge model should be provided for the system. The aim of this paper is to propose the design of a multi-agent based network management support system with well-organized representation of the formal semantics of networks' experiential knowledge related to NMS fault diagnosis functional area. The modeling of the network knowledge is performed using domain ontology, fault-state causal reasoning models, and problem solving methods (PSMs) in a generic and reusable manner. Hence, knowledge acquisition in the NMS domain can be done by using these reusable structures, thereby eliminating the need to build the new knowledge-based systems from scratch. This paper presents an approach towards the agent-mediated ontology-driven automated NMS to benefit the network administrators through the automatic provision of just-in-time and context-dependent knowledge for resolving network failures.

Key to automated network management lies in the leveraging of the network knowledge resources to reduce the communication complexity. This paper proposes a generic framework for representing the knowledge semantics of multi-agent based data communication Network Management Systems (NMS). We focus our attention to the fault diagnosis functional area which is considered as the most crucial regarding the management of networked systems. The proposed network knowledge model adopts the holistic approach based on the CommonKADS methodology [2] for modularizing the elusive structures of network knowledge to facilitate its reusability and shareability. The network static configurational knowledge is identified as domain knowledge ontology, whereas the static status inference knowledge is represented as multi-agent based fault-state causal reasoning models. The complex experiential knowledge of network management is formalized as task knowledge ontology. We envisage this model-based diagnosis approach as the first step towards the comprehensive knowledge acquisition, representation and dissemination in the network management domain.

The explosive growth in online information is making it harder for large, globally distributed organizations to foster collaboration and leverage their intellectual assets. Recently, there has been a growing interest in the development of next generation knowledge management systems focussing on the artificial intelligence based technologies. We propose a generic knowledge management system architecture based on ADIPS (Agent-based Distributed Information Processing System) framework. This contributes to the stream of research on intelligent KM system to supports the creation, acquisition, management, and sharing of information that is widely distributed over a network system. It will benefit the users through the automatic provision of timely and relevant information with minimal effort to search for that information. Ontologies which stand out as a keystone of new generation of multi-agent information systems, are used for the purpose of structuring the resources. This framework provides personalized information delivery, identifies items of interest to user proactively and enables unwavering management of distributed intellectual assets.