Simulation model of load balancing in distributed computing systems

The availability of high-performance computing, high speed data transfer over the network and widespread of software for the design and pre-production in mechanical engineering have led to the fact that at the present time the large industrial enterprises and small engineering companies implement complex computer systems for efficient solutions of production and management tasks. Such computer systems are generally built on the basis of distributed heterogeneous computer systems. The analytical problems solved by such systems are the key models of research, but the system-wide problems of efficient distribution (balancing) of the computational load and accommodation input, intermediate and output databases are no less important. The main tasks of this balancing system are load and condition monitoring of compute nodes, and the selection of a node for transition of the user's request in accordance with a predetermined algorithm. The load balancing is one of the most used methods of increasing productivity of distributed computing systems through the optimal allocation of tasks between the computer system nodes. Therefore, the development of methods and algorithms for computing optimal scheduling in a distributed system, dynamically changing its infrastructure, is an important task

A low-cost, connection aware, load-balancing solution for distributing Gigabit Ethernet traffic between two intrusion detection systems

In today\u27s world of computer networking, Gigabit Ethernet is quickly becoming the norm for connectivity in computer networks. The ease of access to information on these networks leads to new information being made available daily. Rises in both malicious users and malicious network traffic increase the need for intrusion detection systems to monitor network traffic. However, intrusion detection systems capable of processing network traffic at the rate necessary for Gigabit Ethernet are typically expensive. An alternative to purchasing one of these systems is to use multiple, cheaper intrusion detection systems and run them in parallel. This requires that traffic be distributed to these intrusion detection systems such that their traffic monitoring activity is unaffected. For typical intrusion detection systems this means that all traffic belonging to a single connection cannot be separated. This thesis presents the design and implementation of a low-cost, connection aware, load balancing solution capable of distributing traffic to two intrusion detection systems while ensuring that all traffic for a given connection is not separated

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments

A statistical mechanics approach for an effective, scalable, and reliable distributed load balancing scheme for grid networks

The advances in computer and networking technologies over the past decades produced new type of collaborative computing environment called Grid Networks. Grid network is a parallel and distributed computing network system that possesses the ability to achieve a higher computing throughput by taking advantage of many computing resources available in the network. To achieve a scalable and reliable Grid network system, the workload needs to be efficiently distributed among the resources accessible on the network. A novel distributed algorithm based on statistical mechanics that provides an efficient load-balancing paradigm without any centralised monitoring is proposed here. The resulting load-balancer would be integrated into Grid network to increase its efficiency and resources utilisation. This distributed and scalable load-balancing framework is conducted using the biased random sampling (BRS) algorithm. In this thesis, a novel statistical mechanics approach that gives a distributed loadbalancing scheme by generating almost regular networks is proposed. The generated network system is self-organised and depends only on local information for load distribution and resource discovery. The in-degree of each node refers to its free resources, and job assignment and resource updating processes required for load balancing are accomplished by using random sampling (RS). An analytical solution for the stationary degree distributions has been derived that confirms that the edge distribution of the proposed network system is compatible with ER random networks. Therefore, the generated network system can provide an effective loadbalancing paradigm for the distributed resources accessible on large-scale network 1 systems. Furthermore, it has been demonstrated that introducing a geographic awareness factor in the random walk sampling can reduce the effects of communication latency in the Grid network environment. Theoretical and simulation results prove that the proposed BRS load-balancing scheme provides an effective, scalable, and reliable distributed load-balancing scheme for the distributed resources available on Grid networks

Large structural impact localization based on multi-agent system

In practical applications of structural health monitoring, a huge amount of distributed sensors are usually used to monitor structures of large dimensions. In order to obtain fast and accurate evaluation of a structure, a multi-agent system is introduced to manage different sensor sets and to fuse distributed information. In this paper, a multi-agent system based on impact location is presented to deal with the impact load localization problem for large-scale structures. The monitoring system firstly detects whether an impact event happens in the monitored subregion, and focuses on the impact source on the sub-region boundary to obtain the sensor network data with blackboard systems. Then the collaborative evaluation of both the acoustic emission and the inverse analysis localization method is employed to obtain precise and fast localization result. Finally, a reliable assessment for the whole structure is provided by fusing evaluation results from the sub-regions. The performance of the proposed multi-agent system is illustrated by means of experimental on a large aerospace aluminum plate structure. Extensive testing of the proposed system demonstrated its effectiveness for the impact load localization in each sub-region, particularly for impacts lying next to the borders of the sub-regions

Dynamic cluster head routing protocol in wireless sensor network

Wireless distributed based microsensor systems will have reliable monitoring in variety of environments for both civil and military applications. In this research work, we look at communication protocols, which can have significant impact on the overall energy dissipation of the networks in these systems. Based on the findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks.Therefore we propose DCHRP (Dynamic Cluster Routing Protocol), a cluster-based protocol that utilizes instance cluster creation to evenly distribute the energy load among the sensors in the network. DCHRP uses instance clusters to enable scalability and strength for dynamic networks. In addition to this, DCHRP is able to reduce the energy wastage evenly among the sensors, and allowing easy dynamicity in WSNs

Experimental evaluation of IEEE 802.15.4/ZigBee for multi-patient ECG monitoring

IEEE 802.15.4/ZigBee wireless sensor networks (WSNs) are a promising alternative to cabled systems for patient monitoring in hospitals. Some areas where monitoring systems based on WSNs can be successfuly used are ambulatory, waiting and triage rooms, post-op, and emergency rooms. The low power and small size ZigBee devices have the ability to form self-configuring networks that can extend themselves through a hospital wing or floor. Using spatially distributed networks, it is possible to cover an extended area and serve several patients. However, the low data rate protocols provided by IEEE 802.15.4 poses several challenges, mainly because its protocols were primarily designed to operate in low traffic load scenarios but some vital signs sensors generate a large volume of data. This work presents an experimental evaluation of the performance of multi-hop ZigBee networks comprised of several nodes that carry the traffic of wearable electrocardiogram (ECG) sensors. The results indicate that star networks can relay 100% of the traffic generated by at least 12 ECG nodes. In tree topologies, the increase of the network traffic load reduces the performance but even these networks can reliably relay the traffic of a considerable number of ECG nodes.Fundação para a Ciência e a Tecnologia (FCT)Grupo AMI – Assistência Médica Integral (Casa de Saúde Guimarães, SA

Description of the SSF PMAD DC testbed control system data acquisition function

The NASA LeRC in Cleveland, Ohio has completed the development and integration of a Power Management and Distribution (PMAD) DC Testbed. This testbed is a reduced scale representation of the end to end, sources to loads, Space Station Freedom Electrical Power System (SSF EPS). This unique facility is being used to demonstrate DC power generation and distribution, power management and control, and system operation techniques considered to be prime candidates for the Space Station Freedom. A key capability of the testbed is its ability to be configured to address system level issues in support of critical SSF program design milestones. Electrical power system control and operation issues like source control, source regulation, system fault protection, end-to-end system stability, health monitoring, resource allocation, and resource management are being evaluated in the testbed. The SSF EPS control functional allocation between on-board computers and ground based systems is evolving. Initially, ground based systems will perform the bulk of power system control and operation. The EPS control system is required to continuously monitor and determine the current state of the power system. The DC Testbed Control System consists of standard controllers arranged in a hierarchical and distributed architecture. These controllers provide all the monitoring and control functions for the DC Testbed Electrical Power System. Higher level controllers include the Power Management Controller, Load Management Controller, Operator Interface System, and a network of computer systems that perform some of the SSF Ground based Control Center Operation. The lower level controllers include Main Bus Switch Controllers and Photovoltaic Controllers. Power system status information is periodically provided to the higher level controllers to perform system control and operation. The data acquisition function of the control system is distributed among the various levels of the hierarchy. Data requirements are dictated by the control system algorithms being implemented at each level. A functional description of the various levels of the testbed control system architecture, the data acquisition function, and the status of its implementationis presented

Performance evaluation of hierarchical clustering protocols with fuzzy C-means

The longevity of the network and the lack of resources are the main problems within the WSN. Minimizing energy dissipation and optimizing the lifespan of the WSN network are real challenges in the design of WSN routing protocols. Load balanced clustering increases the reliability of the system and enhances coordination between different nodes within the network. WSN is one of the main technologies dedicated to the detection, sensing, and monitoring of physical phenomena of the environment. For illustration, detection, and measurement of vibration, pressure, temperature, and sound. The WSN can be integrated into many domains, like street parking systems, smart roads, and industrial. This paper examines the efficiency of our two proposed clustering algorithms: Fuzzy C-means based hierarchical routing approach for homogeneous WSN (F-LEACH) and fuzzy distributed energy efficient clustering algorithm (F-DEEC) through a detailed comparison of WSN performance parameters such as the instability and stability duration, lifetime of the network, number of cluster heads per round and the number of alive nodes. The fuzzy C-means based on hierarchical routing approach is based on fuzzy C-means and low-energy adaptive clustering hierarchy (LEACH) protocol. The fuzzy distributed energy efficient clustering algorithm is based on fuzzy C-means and design of a distributed energy efficient clustering (DEEC) protocol. The technical capability of each protocol is measured according to the studied parameters