To handle task execution, modern supercomputers employ thousands (or millions) of processors. In such supercomputers, task scheduling has a meaningful impression on system performance. To improve efficiency, task scheduling algorithms aim to decrease the volume of communication and the number of message exchanges. These efforts, however, result in other bottlenecks, such as high-link congestion. In addition, the heterogeneity of processors and networks is another major challenge for schedulers. This paper presents a new algorithm for scheduling called Heterogeneity-Aware Task Scheduling (HATS). The proposed algorithm adopts an updated multi-level hyper-graph partitioning approach. It describes a new method of aggregation in the coarsening step that helps to accurately coarsen the hyper-graph of the task model. The Raccoon Optimization algorithm is then used in the initial partitioning phase, and in the un-coarsening phase, a novel refinement procedure optimises the initial partitions. The experiments on this approach showed that, compared to the other well-known algorithms, the proposed method offers better schedules with lower communication volume and imbalance ratio in a shorter time.

Delivering optimum performance on a parallel computer is highly dependant on the efficiency of the scheduling and mapping procedure. If the composition of the parallel application is known a prior, the mapping can be accomplished statically on the compilation time. The mapping algorithm uses the model of the parallel application and maps its tasks to processors in a way to minimize the total execution time. In this article, current modeling approaches have discussed. Later, a new modeling schema named Model of Exascale Message-Passing Programs on Heterogeneous Architectures (MEMPHA) has proposed. A comparative study has been performed between MEMPHA and existing models. To exhibit the efficiency of the MEMPHA, experiments have performed on a set of data-set hypergraphs. The results obtained from the experiments show that deploying the MEMPHA helps to optimize metrics, including the congestion, total communication volume and maximum volume of data being sent or received. These improvements vary from 76 to 1 percent, depending on the metric and benchmark model. Moreover, MEMPHA supports the modeling of applications with multiple producers for a single data transmission, where the rest of the approaches fail.

In Long Term Evolution-Advanced (LTE-A) networks, different aspects of radio resource scheduling such as fairness and Quality of Service (QoS) assurance must be provided for heterogeneous traffic, having different characteristics. However, the ever-growing number of mobile devices sharing the limited radio resources leads to the high cost and difficulty for information acquisition and computations in resource scheduling process. In the present paper, we propose a proportional knapsack scheduling approach for fairness- and QoS-aware downlink transmission of all different service classes in LTE-A networks. Moreover, we assess the computational cost created by the uncertainty and lack of information on user operations, and propose a Gaussian-based approach for ranking the bearers in presence of limited information and computational capabilities. This approach is particularly suitable for emerging next generation wireless networks to support a wide range of applications with huge number of users. The results indicate that significant advantages are achieved both in terms of QoS and fairness and that it is a scalable solution for overload states of the network.

Pre-fetching is used to predict next query of data items before any problems occur due to network congestion, delays, and latency problems. Lately, pre-fetching strategies become more complicated in which to support new types of application especially for mobile devices. Sometime the pre-fetched data items are not interested to the users. Due to this complication, an intelligent technique is introduced where an integrated measurement using data mining with Bayesian approach is proposed to improve the query performance. In previous study, the pre-fetched data items were filtered using data driven measurement. The data was generated based on the data frequency metrics whereby the structure of the query pattern is quantified using statistical methods. The measurement is not good enough to solve sequence query in mobile environment. In this paper, a new technique is proposed to generate new and potential pre-fetching set for the users. A subjective measurement is used to determine the pre-fetching set based on user interestingness. The integrated measurement generates strong and weak association rules based on the data and user interestingness criterions. The result shows that the performance is significantly improved whereby the technique managed to quantify the uncertainty of users' expectation in the next possible query.

Observations of natural phenomena are considered to be the best information source of spontaneous synchronization. Natural phenomena tend to match wireless sensor network (WSN) responses closely. Such synchronization is vital for the proper coordination of power cycles for energy conservation. A large number of fireflies employ the principle of pulse-coupled oscillators for light flash emission to attract mating partners. With respect to WSNs, the nodes are generally unable to afford packet transmission and reception simultaneously, thus preventing complete network synchronization. This paper presents a literature overview concerning the impact of the deafness problem on clock synchronization in a WSN. Data transmission based on synchronization can also be ensured through the optimization of energy usage periodic data capturing in a WSN. This study serves as a useful information source of clock synchronization to assist WSN researchers and novices in obtaining a better understanding of the impact of the deafness problem on clock synchronization and to enable them to promote effective designs and systems that address this problem.

A PMIPv6 supports mobile node (MN) mobility management in a localized domain over the wireless local area network (WLAN). The standard PMIPv6 mobility related signalling are adopted in reactive mode, which results in long services disruption and unavoidable data traffic loss, thus, harmfully affects the MN's communication performance. In this article, we propose a proactive low loss traffic mechanism, which introduces an efficient buffering technique with optimized functions to prevent data traffic loss and save their transmission cost, without any extra signalling or modification on the standard PMIPv6 mobility signalling structures. Through ns-2 simulation, experiment evaluations and numerical results were accomplished to verify the proposed mechanism superior performance.

With widespread deployment of multicast over Wireless Local Area Networks (WLANs), several issues including fixed data rate transmission, multicast key distribution security, and overlapped multicast address have to be addressed for accommodating an efficient multicast scheme for WLANs. The latter problem can be addressed by utilizing Internet Protocol version 6 (IPv6) which provides significantly more address space compare to existing IPv4. However, in multicast IPv6 over WLANs, when a mobile moves to the border of the multicast group, the data are transmitted at the lowest base rate to support more coverage area, leading to poor Quality of Service (QoS). In this paper, a novel multicast data delivery method over WLANs based on IPv6 protocol is proposed to overcome the problem of fixed base rate and security key distribution in WLANs. Specifically, the proposed method dictates a WLAN Access Point (AP) to encapsulate the multicast packets into unicast Medium Access Control (MAC) packets, and subsequently forward them to the mobile host. In addition, the AP is also responsible for updating and distributing security keys whenever a join or leave operation occurs. The results from our test-bed indicate that the proposed method significantly improve the QoS metrics (i.e., throughput and delay) compared to the existing multicast scenario, as well as able to reduce the amount of generated keys in the networks.

Database integration aims at providing a uniform and consistent view called global schema, over a set of autonomous and heterogeneous data sources, so that data residing in different sources can be accessed as if it was in a single schema. The integration of data sources can be performed in two steps, a matching and a data transformation step. Schema matching, the focus of this paper, is a fundamental operation in the manipulation of schema in formatting match, which takes two schemas that correspond semantically to each other. Manually specifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process, which is a growing problem given the rapidly increasing number of data sources to integrate. As systems become able to handle more complex databases and applications such as biomedical databases schemas, their schemas become large, further increasing the number of matches to be performed. Several solutions in solving the issues of schema matching have been proposed. However, these solutions are still limited as (i) they do not explore most of the available information related to schemas, (ii) the approaches rely strictly on the assumption that the schemas to be matched are from the same application domain, and (iii) the approaches either match schemas by comparing the strings of the elements' names or by checking if those names are synonyms. This paper addresses the above limitations by proposing a model for matching heterogeneous relational biomedical databases' schemas that further improves the results of the integration.

Database integration aims at providing a uniform and consistent view called global schema, over a set of autonomous and heterogeneous data sources, so that data residing in different sources can be accessed as if it was in a single schema. The integration of databases' schemas can be performed in four main phases, namely: preintegration, comparing of the schemas, conforming of the schemas, and merging and restructuring. The second and third phases can be combined and normally is known as schema matching. Schema matching, the focus of this paper, is a fundamental operation in the manipulation of schema in formatting match, which takes two schemas that correspond semantically to each other. Manually specifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process, which is a growing problem given the rapidly increasing number of data sources to integrate. As systems become able to handle more complex databases and applications, their schemas become large, further increasing the number of matches to be performed. Thus, automating this process, which attempts to achieve faster and less labor-intensive, has been one of the main tasks in data integration. However, it is not possible to determine fully automatically the different correspondences between schemas, primarily because of the differing and often not explicated or documented semantics of the schemas. Several solutions in solving the issues of schema matching have been proposed. Nevertheless, these solutions are still limited, as they do not explore most of the available information related to schemas and thus affect the result of integration. This paper presents an approach for integrating heterogeneous relational databases' schemas through schema matching that utilizes most of the information related to schemas, which indirectly explores the implicit semantics of the schemas, that further improves the results of the integration. This paper also shows that the produced integrated schemas (global schemas) maintained the properties of the initial input schemas and also the characteristics of the relational model.

Healthcare information system involves a wide range of collaboration among distributed organizations such as home care organizations, wound care clinics and outpatient surgery facilities which often needs to access to patient medical data which is distributed across several components, structured in multiple data models or formats and with different semantics and stored in heterogeneous feeder systems. The nature of heterogeneous and distributed in healthcare environment causes severe semantic conflicts whenever electronic patient data exchange is needed. It is essential that these components be able to inter-operate with each other and facilitated in heterogeneous medical information exchange over the internet while reducing patient care cost and meanwhile improving the efficiency of patient diagnose. This paper proposed a schema matching approach not only reconcile semantic conflicts but also bring integrated access to patient medical data by mapping dispersed patient information which is semantically match and presenting it to authorized healthcare user.

Maintaining cache consistency in mobile computing is challenging due to the limitations in the mobile environment such as narrow bandwidth, limited capabilities of mobile devices, disconnectivity, and user mobility. In this paper we survey the different methods used for maintaining mobile cache consistency in client-server architecture. Moreover, we categorize these methods based on the inheritance problems of mobile environment that they are dealing with, and present the strengths and the weaknesses of these methods. We conclude from this survey that, till now there is no optimum scheme that maintains the consistency of the mobile cache without degrading in caching system functionality.