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Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), classification and regression trees (CART) and linear discriminant analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This finding implies that our method can effectively predict all possible future system and application failures within the system. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) technology has gained in importance due to its potential support for a wide range of applications. Most of the WSN applications consist of a large number of distributed nodes that work together to achieve common objectives. Running a large number of nodes requires an efficient mechanism to bring them all together in order to form a multi-hop wireless network that can accomplish specific tasks. Even with the recent developments made in WSN technology, a number of important challenges still create vulnerabilities for WSNs, including: energy waste sources; synchronisation leaks; low network capacity; and self-configuration difficulties. However, energy efficiency perhaps remains both the most challenging and highest priority problem due to the scarce energy resources available in sensor nodes. Synchronization by means of scheduling clusters allows the nodes to cooperate and transmit traffic in a scheduled manner under the duty cycle mechanism. This paper aims to make further advances in this area of work by achieving higher accuracy and precision in time synchronisation through controlling the network topology, self-configuration and estimation of the clock errors between the nodes and finally correcting the nodes’ clock to the estimated value. Furthermore, the target in designing energy efficient protocol relies on synchronized duty cycle mechanism and requires a precise synchronisation algorithm that can schedule a group of nodes to cooperate by communicating together in a scheduled manner. These techniques are considered as parameters in the proposed OLS-MAC algorithm. This algorithm has been designed with the objective of ensuring the schedules of the clusters overlap by introducing a small shift in time between the adjacent clusters’ schedules to compensate for the clock drift. The OLS-MAC algorithm is simulated in NS-2 and compared to some S-MAC derived protocols. The simulation results verified that the proposed algorithm outperforms previous protocols in number of performance criterion. [ABSTRACT FROM AUTHOR]

Avoiding collision among contending customer premise equipments (CPEs) attempting to associate with a base station (BS) in a wireless regional area network (WRAN) is a challenging issue. The collision probability is highly dependent upon the size of the initial contention window and the number of contending CPEs. To reduce the collision probability among CPEs in order to start the ranging process in an IEEE 802.22 network, the BS needs to adjust the initial contention window size. This paper provides an analytical framework to estimate the ranging request collision probability depending upon the size of the initial contention window and the number of CPEs attempting to join the IEEE WRAN cell. The accuracy of the estimated curve is analyzed for various numbers of contention CPEs on the basis of the relative errors. The numerical results confirm that the approximation works reasonably well for finding the ranging request collision probability for any number of contention CPEs at a particular value of initial contention window size. Moreover, this approximation provides the threshold size for a contention window to start the initial ranging process for any number of CPEs in an IEEE 802.22 network. [ABSTRACT FROM AUTHOR]

Researchers have used the simulation technique to develop new networks and test, modify, and optimize existing ones. The scientific community has developed a wide range of network simulators to fulfil these objectives and facilitate this creative process. However, selecting a suitable simulator appropriate for a given purpose requires a comprehensive study of network simulators. The current literature on network simulators has limitations. Limited simulators have been included in the studies with functional and performance criteria appropriate for comparison not been considered, and a reasonable selection model for selecting the suitable simulator has not been presented. To overcome these limitations, we studied twenty-three existing network simulators with classifications, additional comparison parameters, system limitations, and comparisons using several criteria. [ABSTRACT FROM AUTHOR]

Highlights: [•] We proposed prioritized proactive spectrum handoff decision schemes. [•] Proposed schemes are modeled using a preemptive resume priority (PRP) M/G/1 queue. [•] Handoff delay is reduced by the proposed schemes. [ABSTRACT FROM AUTHOR]

As part of a defense-in-depth security solution for domain-controlled enterprise networks, a proposed self-healing system architecture is designed to increase resiliency against botnets with minimal disruption to network services. [ABSTRACT FROM AUTHOR]

Rapid advances in software systems, wireless networks, and embedded devices have led to the development of a pervasive and mobile cyberspace that provides an infrastructure for anywhere/anytime service provisioning in different domains such as engineering, commerce, education, and entertainment. This style of service provisioning enables users to freely move between geographical areas and to continuously access information and conduct online transactions. However, such a high mobility may cause performance and reliability problems during the execution of transactions. For example, the unavailability of sufficient bandwidth can result in failure of transactions when users move from one area (cell) to another. We present a context-aware transaction model that dynamically adapts to the users' needs and execution environments. Accordingly, we develop a new mobility management scheme that ensures seamless connectivity and reliable execution of context-aware transactions during mobility of users. The proposed scheme is designed and developed using a combination of different queuing models. We conduct various experiments in order to show that the proposed scheme optimizes the mobility management process and increases the throughput of context-aware transactions. [ABSTRACT FROM PUBLISHER]

Abstract: Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor network. In this paper, we introduce an adaptive clustering protocol for wireless sensor networks, which is called Adaptive Decentralized Re-Clustering Protocol (ADRP) for Wireless Sensor Networks. In ADRP, the cluster heads and next heads are elected based on residual energy of each node and the average energy of each cluster. The simulation results show that ADRP achieves longer lifetime and more data messages transmissions than current important clustering protocol in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Abstract: Databases form the common component of many software systems. However, performance models specifically targeted at the database design have not been extensively studied. In this paper, we propose QuePED – a queueing network performance evaluation model for database designs. QuePED provides for the performance modelling of database design constructs, including active database rules; thus, deviating from current methods that consider database designs as processing demands on the hardware architecture. We present a formal specification of QuePED and describe its application to database designs. Experimental evaluation has shown the ability of QuePED to capture the steady state performance of an implementation of the TPC-C benchmark. [ABSTRACT FROM AUTHOR]

Abstract: The IEEE 802.16 standard defines several scheduling classes at MAC layer for preferential treatment of service flows depending on QoS requirements specific to a service flow. In this paper, a new framework has been proposed to solve and address QoS issues for fixed point to multipoint (PMP) 802.16 systems. The proposed framework consists of a uplink scheduler and Call Admission Control (CAC) module. The proposed CAC module interact with the uplink scheduler status and makes its decision based on the scheduler’s queues status. Extensive OPNET simulation demonstrates the effectiveness of the proposed framework. [Copyright &y& Elsevier]

Abstract: During the recent years, there has been a tremendous growth in the development and deployment of multimedia based networked applications such as video streaming, IP telephony, interactive games, among others. These applications, in contrast to elastic applications such as email and data sharing, are delay and delay jitter sensitive but can tolerate certain level of packet loss. A vital element of end-to-end delay and delay jitter is the random queueing delays in network switches and routers. Analysis of robust mechanisms for buffer management at network routers needs to be carried out in order to reduce end-to-end delay for traffic generated by multimedia applications. In this context, a threshold based buffer management scheme for accommodating multiple class multimedia traffic in network routers has been analysed. This technique effectively controls the allocation of buffer to various traffic classes according to their delay constraints. The forms of the joint state probabilities, as well as basic performance measures such as blocking probabilities are analytically established at equilibrium. Typical numerical experiments are included to illustrate the credibility of the proposed mechanism in the context of different quality of service (QoS) grades for various network traffic classes. This model, therefore, can be used as a powerful tool to provide a required grade of service to a particular class of multimedia based web traffic in any heterogeneous network. [Copyright &y& Elsevier]

Abstract: Analysis of networks of queues under repetitive service blocking mechanism has been presented in this paper. Nodes are connected according to an arbitrary configuration and each node in the networks employs an active queue management (AQM) based queueing policy to guarantee certain quality of service for multiple class external traffic. This buffer management scheme has been implemented using queue thresholds. The use of queue thresholds is a well known technique for network traffic congestion control. The analysis is based on a queue-by-queue decomposition technique where each queue is modelled as a GE/GE/1/N queue with single server, R (R ⩾2) distinct traffic classes and {N = N 1, N 2,…, N R } buffer threshold values per class under first-come-first-serve (FCFS) service rule. The external traffic is modelled using the generalised exponential (GE) distribution which can capture the bursty property of network traffic. The analytical solution is obtained using the maximum entropy (ME) principle. The forms of the state and blocking probabilities are analytically established at equilibrium via appropriate mean value constraints. The initial numerical results demonstrate the credibility of the proposed analytical solution. [Copyright &y& Elsevier]

Abstract: Composition of Web services enables collaboration among autonomous business organisations such that they can integrate their services to perform collaborative business activities. It facilitates the development of new services using pre-existing Web services thus reducing development and operational costs. However, the highly distributed, dynamic, and autonomous nature of component Web services gives rise to various issues such as service matchmaking, reliability, availability, security and efficiency. This paper presents a new protocol in order to improve the efficiency of Web services composition. The proposed protocol is based on the peer-to-peer paradigm which exploits the capabilities of underlying networks such that part of the processing is carried out at the network nodes. Efficiency of the proposed protocol is tested through various experiments. Experimental results show that the proposed protocol significantly improves performance by reducing the system response time in the composition of Web services. [Copyright &y& Elsevier]

Abstract: The use of queue thresholds is a well known technique for network traffic congestion control. This motivates the study of a single server queue with R(R ⩾2) distinct priority classes under Head of Line (HoL) service priority discipline, Partial Buffer Sharing (PBS) scheme and a finite capacity vector N, representing a sequence of thresholds (N 1, N 2,…, N R ) for each class jobs. The external traffic is modelled using the compound Poisson process or generalised exponential (GE) distribution which can capture the bursty property of the network traffic. The transmission times have also been modelled using the GE distribution to depict the bulk departures from the system. Using a GE/GE/1/N approximation, a closed form cost-effective analytical solution is obtained using the principle of maximum entropy (ME). The forms of the joint, aggregate and marginal state probabilities, as well as basic performance measures such as utilisation and blocking probabilities are analytically established at equilibrium via appropriate mean value constraints and the generating function approach. Consequently, efficient recursive expressions of low computational cost are determined. Typical numerical experiments are included to illustrate the credibility of the proposed mechanism in the context of different QoS grades for various network traffic classes. This model, therefore, can be used as a powerful tool to provide a required grade of service to a particular class of traffic in any heterogeneous networks. [Copyright &y& Elsevier]

Mobile transaction technology ensures reliability, data consistency, and concurrency in mobile computing applications. Current solutions develop numerous transaction models and protocols in order to enhance the reliability and improve the performance of mobile transactions. However, they give no attention to the underlying network on which mobile transactions are carried out. This paper presents a network-centric strategy in order to improve the performance of mobile transactions. The proposed strategy is based on employing preemptive resume scheduling mechanism in mobile transactions. A cost effective analytical model for the network node with bursuty arrival traffic and prioritised scheduling mechanism has been developed using maximum entropy (ME) methodolgy. It involves rigorous analysis of a network node with finite capacity to accommodate messages, bursty arrival process to represent incoming multi-class messages, and the employment of a priority scheduling mechanism to give preferential treatment to high priority messages. This analytical solution provides closed form expressions for calculating the queuing delay per class involved in the commit processing of mobile transactions. The aim is to reduce queuing delays involved in the processing of mobile transactions at each network node. Such reduction of queuing delay significantly improves the performance of mobile transactions given the heavy network traffic and the lower network bandwidth. Several experiments have been conducted using the analytical modelling approach, which determine the impact of priority scheduling mechanism on the performance of mobile transactions. Experiments show significant performance improvements of the proposed strategy over existing approaches. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including peer-to-peer (P2P) network protocol, another on inter-vehicle communications and routing protocol in wireless and ad hoc sensors.

The article features the best six papers from the first workshop on Performance Modelling of Wired and Wireless Mobile Computing and Networks that was held in conjunction with the 11th ICPADS 2005, in Fukuoka, Japan. These papers were chosen by the scientific program committee and extended and revised before taking a peer-review. The workshop was aimed to provide an international forum for researchers and industry practitioners to introduce their research on performance modeling and evaluation studies regarding the wired and wireless networking and computing and to exchange ideas and to explore new avenues of collaborations. The papers include an investigative study by Pong and others; an experimental study by Seah and others; and the detailed investigative study by Yassein and others.

The author discusses the issues related to the performance engineering of communication systems and their applications. He mentions that performance engineering has evolved in many areas of research due to rapid growth of the communication technologies and the increasing development of their diverse applications including system design, performance measurements and prototyping.

Presents an introduction to the October 2004 issue of journal "lEE Proceedings -- Software."

Abstract: In mobile ad hoc networks (MANETs), flooding is a required message dissemination technique for network-wide broadcast. The conventional blind flooding algorithm causes broadcast storm problem, a high number of unnecessary packet rebroadcasts thus resulting in high contention and packet collisions. This paper proposes a new probabilistic approach that dynamically fine-tunes the rebroadcasting probability of a node for routing request packets (RREQs) according to the number of neighbour nodes. We evaluate the performance of the proposed approach for the ad hoc on demand distance vector (AODV) routing protocol and compared against the blind flooding, fixed probabilistic and adjusted probabilistic flooding [L.M.M.M. Bani-Yassein, M. Ould-Khaoua et al., Performance analysis of adjusted probabilistic broadcasting in mobile ad hoc networks, International Journal of Wireless Information Networks 13(2) (2006) 127–140; M.B. Yassein, M.O. Khaoua et al., Improving route discovery in on-demand routing protocols using local topology information in MANETs, Proceedings of the ACM international workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, Terromolinos, Spain, ACM Press, 2006, pp. 95–99.] approaches. The simulation results show that our proposed approach demonstrates better performance than blind flooding, fixed probabilistic and adjusted flooding approaches. [Copyright &y& Elsevier]

Traffic congestion degrades not only the user-perceived Quality-of-Service (QoS), such as leading to high packet loss rates, low throughput, and increased delays, but also causes excessive energy consumption in energy-sensitive systems (e.g., wireless sensor networks). A simple way to detect congestion is to monitor and measure queue length in network nodes or routers. This paper develops an analytical performance model for a finite capacity queueing system with an enhanced Random Early Detection (RED) congestion control scheme based on the instantaneous queue length in the presence of differentiated classes of bursty traffic. The aggregate traffic is captured by the superposition of 2-state Markov Modulated Poisson Processes (MMPP). The individual threshold is assigned to each traffic class in order to differentially control traffic injection rate. The accuracy of this model is verified by comparing the analytical results against those obtained from simulation experiments. The model is adopted to investigate the effects of traffic burstiness and system capacity on the performance of the congestion control scheme. [ABSTRACT FROM AUTHOR]

Credit-based flow control scheme that can be used to support both end-to-end and link-level flow control is becoming increasingly popular in high speed system area networks (SAN), e.g. InfiniBand networks where multiple processor nodes and I/O devices are interconnected using switched point-to-point links. By virtue of such a scheme, the downstream node sends credits to the upstream node indicating the availability of buffer spaces. Upon receiving credits, the upstream node injects packets into the networks. Performance analysis of credit-based flow control scheme plays an important role for the design and optimization of InfiniBand interconnection networks which have been widely used in many high-performance cluster, Grid and P2P computing systems. This study develops a new queueing network model for performance evaluation of credit-based flow control in InfiniBand networks. The performance metrics to be derived include the mean queue length, throughput and response time of the system. Simulation experiments have been used to validate the accuracy of the queueing network model. Results obtained from the analytical model have showed that this model can effectively evaluate the performance of credit-based flow control in InfiniBand networks. [ABSTRACT FROM AUTHOR]

With the exponential growth in the number of internet-enabled devices, large scale security threats such as distributed denial of service (DDoS) attacks significantly affect software defined networks (SDNs). This necessitates efficient detection and mitigation solutions. Monitoring of SDN activities (typically identified using metrics such as throughput, jitter and response time) to ascertain deviations from profiles of normality (previously learned from benign traffic) is a key approach in detecting attacks on SDNs. In this paper, local sensitivity analysis (LSA) is implemented to identify the key network metrics that mainly influence the prediction of whether an SDN is under attack or secure. Using throughput, jitter and response time as the network impact metrics and a mathematical cost function based on min-max feature scaling to associate SDN scenarios with their respective SDN impact metrics, an artificial neural network (ANN)-based prediction model is built. The sensitivity of throughput, jitter and response time is then evaluated using the deviations of newly predicted target values of the ANN model from the actual target values when an additive white Gaussian noise (AWGN) is added to the respective impact metrics. The results of this study show that throughput, jitter and response time are all statistically sensitive to a DDoS flooding attack of the SDN. Also, Jitter was found to be the most sensitive network metric to a DDoS flooding attack of the SDN. [ABSTRACT FROM AUTHOR]

This paper investigates into the performance and load management of web servers that are deployed in commercial websites. Such websites offer various services such as flight/hotel booking, online banking, stock trading, and product purchases among others. Customers are increasingly relying on these round-the-clock services which are easier and (generally) cheaper to order. However, such an increasing number of customers' requests makes a greater demand on the web servers. This leads to web servers' overload and the consequential provisioning of inadequate level of service. This paper addresses these issues and proposes an admission control scheme which is based on the class-based priority scheme that classifies customer's requests into different classes. The proposed scheme is formally specified using $$\Pi $$ -calculus and is implemented as a Java-based prototype system. The prototype system is used to simulate the behaviour of commercial website servers and to evaluate their performance in terms of response time, throughput, arrival rate, and the percentage of dropped requests. Experimental results demonstrate that the proposed scheme significantly improves the performance of high priority requests but without causing adverse effects on low priority requests. [ABSTRACT FROM AUTHOR]

Abstract: Model based control of automotive engines for fuel economy and pollution minimization depends on accuracy of models used. A number of mathematical models of automotive engine processes are available for this purpose but critical model parameters are difficult to obtain and generalize. This paper presents a novel method of online estimation of discharge coefficient of throttle body at the intake manifold of gasoline engines. The discharge coefficient is taken to be a varying parameter. Air mass flow across the throttle body is a critical variable in maintaining a closer to stoichiometric air fuel ratio; which is necessary to minimize the pollution contents in exhaust gases. The estimation method is based on sliding mode technique. A classical first Sliding mode observer is designed to estimate intake manifold pressure and the model uncertainty arising from the uncertain and time varying discharge coefficient is compensated by the discontinuity/switching signal of sliding mode observer. This discontinuity is used to compute coefficient of discharge as a time varying signal. The discharge coefficient is used to tune/correct the intake manifold model to engine measurements. The resulting model shows a very good agreement with engine measurements in steady as well s transient state. The stability of the observer is shown by Lyapunov direct method and the validity of the online estimation is successfully demonstrated by experimental results. OBD-II (On Board Diagnostic revision II) based sensor data acquisition from the ECU (Electronic Control Unit) of a production model vehicle is used. The devised algorithm is simple enough to be designed and implemented in a production environment. The online estimation of parameter can also be used for engine fault diagnosis work. [Copyright &y& Elsevier]

Abstract: A novel robust technique for speed control application of variable reluctance motor is proposed. The suggested scheme is model based and uses a mathematical model of an SR motor, and Second Order Sliding Mode Control (SOSMC) with super-twisting algorithm. Sliding mode controllers for SR motor were reported before but super twisting SOSMC have an added advantage of reduced chattering which is one of the main focuses of this work. The proposed controller gives fast dynamic response with no overshoot and nearly zero steady state error. The effectiveness of the proposed controller and its robustness to parameter variations is also confirmed by simulation results. [Copyright &y& Elsevier]

Abstract: Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution. [Copyright &y& Elsevier]

Many performance models for deterministic routing in multicomputer interconnection networks have been derived and analyzed under the assumption of the traditional Poisson stochastic arrival process, which is inherently unable to capture traffic self-similarity revealed by many real-world parallel applications. In an effort towards understanding the network performance under various traffic loads and different design alternatives, this paper presents an analytical model for dimension-ordered routing in k-ary n-cubes when subjected to self-similar traffic. As the service time, blocking probability and waiting time experienced by a message vary from a dimension to another, the design of such a model for dimension-ordered routing poses greater challenges. The developed analytical model is then used to investigate the efficiency of two different ways to organize virtual channels for deterministic routing and to evaluate the impact of self-similar traffic with various Hurst parameters on network performance. [ABSTRACT FROM AUTHOR]

• Development of a new model for representing cloud services QoS attributes • New simulation tool for simulating the proposed model and MCDM models • Improving credibility & correctness of cloud service selection • Simplifies selection process by considering user's knowledge of cloud computing Cloud service selection process is significantly challenging and complicated as there are various QoS factors to consider when selecting cloud services. This paper proposes a new QoS-aware selection model systematically and succinctly representing QoS attributes which cloud consumers can easily use and understand when selecting cloud services. In order to ensure the credibility of the cloud service selection, the proposed model collects QoS data from different sources including cloud providers, users' reviews and cloud monitoring tools. It implements Multi-Criteria Decision Making models in order to rank services based on various QoS attributes. The proposed model is implemented as a simulation tool which is deployed on Amazon cloud platform. Using the simulation tool, the proposed model is rigorously evaluated through a number of experiments by taking into account data from widely used commercial cloud service providers. The experimental results show that the proposed model ranks and selects cloud services according to the QoS requirements of cloud service consumers. Unlike existing approaches the proposed model takes into account multi-level QoS attributes and data from real cloud providers when ranking cloud services. [ABSTRACT FROM AUTHOR]