Agent Based Modelling and Simulation tools: A review of the state-of-art software

Knowledge sharing visibility KSV is a critical environmental factor which can reduce social loafing in knowledge sharing KS. This is especially true in ICT-based KS in learning organisations. As such, it is imperative that we better understand how to design technology enabled knowledge management systems KMS to support high KSV. This article examines the impact of knowledge management technology functions e.g. tracking, knowledge storing on KSV through qualitative analysis of 16 semi-structured interviews with participants in a Chinese company. Impact and implications of use for their existing KMS are examined. This article also examined the effects of department characteristics i.e. group size and task characteristics and individual roles i.e. employee positions on the IT–KSV relationship. Results encourage applied statistical, tracking, knowledge distribution and knowledge storing functions for monitoring explicit KS, and suggest integration of visualised knowledge maps with communication tools e.g. Instant Messenger IM to support visibility for implicit KS. Findings also suggest that KM technologies are more salient on improving KSV in large department with routine tasks, and that low-level employees may have more positive attitude on accepting communication tools on sharing knowledge. Extension to use of Web 2.0 technologies e.g. weblogs in KMS is also explored.

Effects of information technologies, department characteristics and individual roles on improving knowledge sharing visibility: a qualitative case study

A Data center comprises of servers, storage devices, cooling and power delivery equipment to support other components, exchange data and information to provide general services such as software-as-a-service (SaaS), platform-as-a-service (PaaS), and Internet-as-a-service (IaaS). Data centers require massive amount of computational power to drive complex systems. In return these massive systems bring many challenges and concerns including power dissipation and environmental sustainability. Higher power demand in data centers and changes in computing technology together to maximize data center performance has led to deploying multitude methods to estimate power intensity. Energy cost increment, global economic downturn, and global warming and other concerns have resulted in new research in achieving power efficient data centers.

The research proposed in this paper evaluates three task scheduling algorithms RASA, TPPC, and PALB to get the most energy efficient task scheduling algorithm to be used in data centers for measuring their performance and efficiency. The three algorithms are evaluated for performance using three parameters; power efficiency, cost effectiveness, and amount of CO2 emissions. On top of that data center location and climate conditions are also considered and analyzed as parameters as they directly effect the operating costs, the amount of power consumption and CO2 emission. To minimize the power wasted by data center cooling systems is directly related to data center location and climate change. CloudSim simulator is used to implement the algorithms on an IaaS cloud infrastructure, to calculate the power consumption, and to analyze each algorithm׳s behavior for different parameters. The results generated clearly shows that TPPC is the most efficient algorithm due to less amount of power consumption and low volume of CO2 emission; however its implementation cost is bit higher compare to PALB and RASA.

Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review

Symbiotic simulation is one of Industry 4.0 technologies that enables interaction between a physical system and the simulation model that represents it as its digital twin. Symbiotic simulation is designed to support decision making at the operational levels by making use of real- or near real- time data that is generated by the physical system, which is used as an input to the simulation model. From the modeling perspective, a symbiotic simulation system comprises a hybrid systems model that combines simulation, optimization and machine learning models as well as a data acquisition module and an actuator. The actuator is needed when the symbiotic simulation system is designed to directly control the physical system without human intervention. This paper reviews the components of a symbiotic simulation system from the perspective of hybrid systems modeling and highlights research questions needed to advance symbiotic simulation study.

Symbiotic simulation system: hybrid systems model meets big data analytics

Knowledge contributing behaviour as well as knowledge collecting behaviour is essential and necessary in optimising knowledge sharing behaviour. By integrating social cognitive theory SCT model and IS success model theory, this research identified and analysed three dimensions of influential factors of knowledge sharing behaviour, namely individual factors, technological factors and contextual factors. Data were collected from virtual community members of information technology in Indonesia and subsequently analysed by using partial least squares PLS method. The result showed that the information quality, the norm of reciprocity, the expectation outcomes, the enjoyment in helping others and the interpersonal trust have significant influence in knowledge sharing behaviour. In addition, the result also concluded that knowledge contributing behaviour and knowledge utilisation are particularly important for community promotion activities.

Investigating knowledge sharing behaviour on virtual community members: integration of technological, individual and contextual factors

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.

/pdf/automated-dynamic-resource-provisioning-and-monitoring-in-3njilmyj6g.pdf

Automated Dynamic Resource Provisioning and Monitoring in Virtualized Large-Scale Datacenter

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.

Exploiting domain ontologies and intelligent agents: an automated network management support paradigm

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.

/pdf/autonomous-network-monitoring-system-based-on-agent-mediated-4pq28uhs5e.pdf

Autonomous Network Monitoring System based on Agent-mediated Network Information

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.

Agent-based knowledge acquisition in 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 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.

A Knowledge-Based Strategy for the Automated Support to Network Management Tasks

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