Query processing optimization in broadcasting XML data in mobile communications

Todays, XML as a de facto standard is used to broadcast data over mobile wireless networks. In these networks, mobile clients send their XML queries over a wireless broadcast channel and recieve their desired XML data from the channel. However, downloading the whole XML data by a mobile device is a challenge since the mobile devices used by clients are small battery powered devices with limited resources. To meet this challenge, the XML data should be indexed in such a way that the desired XML data can be found easily and only such data can be downloaded instead of the whole XML data by the mobile clients. Several indexing methods are proposed to selectively access the XML data over an XML stream. However, the existing indexing methods cause an increase in the size of XML stream by including some extra information over the XML stream. In this paper, a new XML stream structure is proposed to disseminate the XML data over a broadcast channel by grouping and summarizing the structural information of XML nodes. By summarizing such information, the size of XML stream can be reduced and therefore, the latency of retrieving the desired XML data over a wirless broadcast channel can be reduced. The proposed XML stream structure also contains indexes in order to skip from the irrelevant parts over the XML stream. It therefore can reduce the energy consumption of mobile devices in downloading the results of XML queries. In addition, our proposed XML stream structure can process different types of XML queries and experimental results showed that it improves the performace of XML query processing over the XML data stream compared to the existing research works in terms of access and tuning times.

     

Predicting potential deadlocks in multithreaded programs

In a multithreaded program, competition of threads for shared resources raises the deadlock possibility, which narrows the system liveness. Because such errors appear in specific schedules of concurrent executions of threads, runtime verification of threads behavior is a significant concern. In this study, we extended our previous approach for prediction of runtime behavior of threads may lead to an impasse. Such a prediction is of importance because of the nondeterministic manner of competing threads. The prediction process tries to forecast future behavior of threads based on their observed behavior. To this end, we map observed behavior of threads into time‐series data sets and use statistical and artificial intelligence methods for forecasting subsequent members of the sets as future behavior of the threads. The deadlock prediction is carried out based on probing the allocation graph obtained from actual and predicted allocation of resources to threads. In our approach, we use an artificial neural network (ANN) because ANNs enjoy the applicable performance and flexibility in predicting complex behavior. Using three case studies, we contrasted results of the current and our previous approaches to demonstrate results. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of Clustering Algorithms in Ad Hoc Mobile Networks

Ad hoc mobile networks are free of any infrastructures and their nodes are not aware of the connections of the network locating in, since the structure of these networks is dynamic. To send data to other nodes, each node should detect the sending path and then save it. Due to their dynamic nature, these types of networks face design complexity and limitations such as a lack of specific infrastructure and the change of the infrastructure with passing the time, the limitation of energy, bandwidth, and the considerations of quality and security. Therefore, bandwidth optimization, power and energy control and an improvement in transmission quality are challenges of these types of networks in routing. To meet these challenges, the node clustering methods were welcomed for less energy consumption and longer network lifetime. In this paper, we deal with a systematic literature review of different clustering methods and propose a general categorization for them. Furthermore, we compare the performance the methods as well as the related algorithms and their strengths and weaknesses. Finally, we rank the algorithms regarding the four parameters of transmission range, mobility speed, battery, and connectivity degree using the multi-criterion decision-making and analytical hierarchical process techniques.

     

A method for the optimum selection of datacenters in geographically distributed clouds

The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user’s requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user’s request and SLAs. Various attributes are considered in the SLA; in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user’s request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective NSGA-II algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as NSGAII_Cluster, is implemented with the Random, Greedy and MOPSO algorithms; the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.     

Using a recurrent artificial neural network for dynamic self-adaptation of cluster-based web-server systems

To process huge requests issued from web users, web servers often set up a cluster using switches and gateways where a switch directs users’ requests to some gateway. Each gateway, which is connected to some servers, is considered for processing a specific type of request such as fttp or http service. When servers of a gateway are saturated and the gateway is not able to process more requests, adaptation is performed by borrowing a server from another gateway. However, such a reactive adaptation causes some problems. However, due to problem of the reactive techniques, predictive ones have been paid attention. While a reactive adaptation aims to redress the system after incurring a bottleneck, a predictive adaptation tries to prevent the system from entering the bottleneck. In this article, we improved our previous predictive framework using a Recurrent Artificial Neural Network (RANN) called Nonlinear Autoregressive with eXogenous (external) inputs (NARX). We employed our new framework for adaptation of a web-based cluster where each cluster is meant for a specific service and self-adaptation is used for load balancing clusters. To show the improvement, we used the case study presented in our previous study.     

Specification and verification of reliability in dispatching multicast messages

Multicasting some pieces of information such as messages or packets (called dispatches) from source node(s) to a group of target nodes are governed by a specific sequence in the networked systems. The sequence is called a consensus that indicates an ordering on dispatches to be viewed by the target nodes. Achievement of consensus is a concern in some networked based systems such as distributed ones because the lack of consensus leads to conflict among target nodes reaction. A consensus protocol has some properties to be checked when a source node multicasts a sequence of dispatches to target nodes. The CBCAST protocol is a consensus protocol having properties for ordering and synchronization of dispatches in network communications. This paper thinks of the properties and formulates axioms to check them. The axioms can be practiced for network applications such as group communication and web services. Our approach has two phases consisting of modeling and formulation. The first phase addresses specification of sender and recipient processes by tabular automata. The second phase addresses formulation of axioms using the automaton.     

Constructing formal rules to verify message communication in distributed systems

This study presents a method to construct formal rules used to run-time verify message passing between clients in distributed systems. Rules construction is achieved in four steps: (1) Visual specification of expected behavior of the sender, receiver, and network in sending and receiving a message, (2) Extraction of properties of sender, receiver, and network from the visual specification, (3) specification of constraints that should govern message passing in distributed systems, and (4) construction of verifier rules from the properties and the constraints. The rules are used to verify actual sender, receiver, and network behavior. Expected behavior of the client (process) is one that to be and the actual one is the behavior should be verified. The rules were applied to verify the behavior of client and servers that communicated with each other in order to compute Fibonacci numbers in parallel and some violations were discovered.     

Identification of essential proteins based on a new combination of topological and biological features in weighted protein–protein interaction networks

The identification of essential proteins in protein–protein interaction (PPI) networks is not only important in understanding the process of cellular life but also useful in diagnosis and drug design. The network topology‐based centrality measures are sensitive to noise of network. Moreover, these measures cannot detect low‐connectivity essential proteins. The authors have proposed a new method using a combination of topological centrality measures and biological features based on statistical analyses of essential proteins and protein complexes. With incomplete PPI networks, they face the challenge of false‐positive interactions. To remove these interactions, the PPI networks are weighted by gene ontology. Furthermore, they use a combination of classifiers, including the newly proposed measures and traditional weighted centrality measures, to improve the precision of identification. This combination is evaluated using the logistic regression model in terms of significance levels. The proposed method has been implemented and compared to both previous and more recent efficient computational methods using six statistical standards. The results show that the proposed method is more precise in identifying essential proteins than the previous methods. This level of precision was obtained through the use of four different data sets: YHQ‐W, YMBD‐W, YDIP‐W and YMIPS‐W.Inspec keywords: proteins, drugs, biology computing, ontologies (artificial intelligence), topologyOther keywords: biological features, weighted protein–protein interaction networks, network topology‐based centrality measures, low‐connectivity essential proteins, topological centrality measures, protein complexes, incomplete PPI networks, weighted centrality measures, YHQ‐W dataset, YMBD‐W dataset, YDIP‐W dataset, YMIPS‐W dataset