A cluster computing implementation of a Monte Carlo simulation of a particle growth mechanism

Clusters of computers can be used together to provide a powerful computing resource. Large Monte Carlo simulations, such as those used to model particle growth, are computationally intensive and take considerable time to execute on conventional workstations. By spreading the work of the simulation across a cluster of computers, the elapsed execution time can be greatly reduced. Thus a user has apparently the performance of a supercomputer by using the spare cycles on other workstations.

Dynamic parallel job scheduling in multi-cluster computing systems

Job scheduling is a complex problem, yet it is fundamental to sustaining and improving the performance of parallel processing systems. In this paper, we address an on-line parallel job scheduling problem in heterogeneous multi-cluster computing systems. We propose a new space-sharing scheduling policy and show that it performs substantially better than the conventional policies.

Parallel I/O scheduling in the presence of data duplication on multiprogrammed cluster computing systems

The widespread adoption of cluster computing as a high performance computing platform has seen the growth of data intensive scientific, engineering and commercial applications such as digital libraries, climate modeling, computational chemistry, computational fluid dynamics and image repositories. However, I/O subsystem performance has not been keeping pace with processor and memory performance, and is fast becoming the dominant factor in overall system performance.  Thus, parallel I/O has become a necessity in the face of performance improvements in other areas of computing systems. This paper addresses the problem of parallel I/O scheduling on cluster computing systems in the presence of data replication.  We propose two new I/O scheduling algorithms and evaluate the relative performance of the proposed policies against two existing approaches.  Simulation results show that the proposed policies perform substantially better than the baseline policies.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system is often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems is very important, which is the focus of this paper. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Optimal multicast tree routing for cluster computing in hypercube interconnection networks

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

Analytical interconnection networks model for multi-cluster computing systems

This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on multi-cluster computing systems. The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. We present an analytical model that considers stochastic quantities as well as processor heterogeneity of the target system. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system sizes and under different operating conditions.

Analysis of multi-cluster computing systems with processor heterogeneity

This paper addresses the problem of performance modeling of heterogeneous multi-cluster computing systems. We present an analytical model that can be employed to explore the effectiveness of different design approaches so that one can have an intelligent choice during design and evaluation of a cost effective large-scale heterogeneous distributed computing system. The proposed model considers stochastic quantities as well as processor heterogeneity of the target system. The analysis is based on a parametric fat-tree network, the m-port n-tree, and a deterministic routing algorithm. The correctness of the proposed model is validated through comprehensive simulation of different types of clusters.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems–which is the focus of this paper–is very important. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrate that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions. On the basis of the validated model, we propose an adaptive assignment function based on the existing heterogeneity of the system to minimize multi-user environment overhead.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.