An analytical model of pipelined circuit switching in hypercubes in the presence of hot spot traffic

Several recent studies have revealed that PCS can provide superior performance characteristics over wormhole switching under uniform traffic. Analytical model of PCS for common networks (e.g., hypercube) under uniform traffic pattern have recently been reported in the literature. In this paper we propose an analytical model of PCS in the hypercube network augmented with virtual channel in the presence of hot spot traffic. The model has a good agreement with simulation experiments

On the performance of broadcast algorithms in interconnection networks

Broadcast Communication is among the most primitive collective capabilities of any message passing network. Broadcast algorithms for the mesh have been widely reported in the literature. However, most existing algorithms have been studied within limited conditions, such as light traffic load and fixed network sizes. In other words, most of these algorithms have not been studied at different Quality of Service (QoS) levels. In contrast, this study examines the broadcast operation, taking into account the scalability, parallelism, a wide range of traffic loads through the propagation of broadcast messages. To the best of our knowledge, this study is the first to consider the issue of broadcast latency at both the network and node levels across different traffic loads. Results are shown from a comparative analysis confirming that the coded-path based broadcast algorithms exhibit superior performance characteristics over some existing algorithms

Private Cloud Deployment on Shared Computer Labs

A computer laboratory in a school or college is often shared for multiple class and lab sessions. However, often the computers in the lab are just left idling for an extended period of time. Those are potential resources to be harvested for cloud services. This manuscript details the deployment of a private cloud on the shared computer labs. Fundamental services like operation manager, configuration manager, cloud manager, and schedule manager were put up to power on/off computers remotely, specify each computer’s OS configuration, manage cloud services (i.e., provision and retire virtual machines), and schedule OS switching tasks, respectively. OpenStack was employed to manage computer resources for cloud services. The deployment of private cloud can improve the computers’ utilization on the shared computer labs

A survey on Bluetooth multi-hop networks

Bluetooth was firstly announced in 1998. Originally designed as cable replacement connecting devices in a point-to-point fashion its high penetration arouses interest in its ad-hoc networking potential. This ad-hoc networking potential of Bluetooth is advertised for years - but until recently no actual products were available and less than a handful of real Bluetooth multi-hop network deployments were reported. The turnaround was triggered by the release of the Bluetooth Low Energy Mesh Profile which is unquestionable a great achievement but not well suited for all use cases of multi-hop networks. This paper surveys the tremendous work done on Bluetooth multi-hop networks during the last 20 years. All aspects are discussed with demands for a real world Bluetooth multi-hop operation in mind. Relationships and side effects of different topics for a real world implementation are explained. This unique focus distinguishes this survey from existing ones. Furthermore, to the best of the authors’ knowledge this is the first survey consolidating the work on Bluetooth multi-hop networks for classic Bluetooth technology as well as for Bluetooth Low Energy. Another individual characteristic of this survey is a synopsis of real world Bluetooth multi-hop network deployment efforts. In fact, there are only four reports of a successful establishment of a Bluetooth multi-hop network with more than 30 nodes and only one of them was integrated in a real world application - namely a photovoltaic power plant. © 2019 The Author

Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids

To use services transparently in a distributed environment, the Utility Grids develop a cyber-infrastructure. The parameters of the Quality of Service such as the allocation-cost and makespan have to be dealt with in order to schedule workflow application tasks in the Utility Grids. Optimization of both target parameters above is a challenge in a distributed environment and may conflict one another. We, therefore, present a novel heuristic algorithm for scheduling a workflow application on Utility Grids. Our proposed algorithm optimizes the allocation-cost and makespan in a scalable and very low runtime. The results of the wide-spread simulation indicate that the proposed algorithm is scalable against an increase in the application size and task parallelism of the application. The proposed algorithm effectively outperforms the current algorithms in terms of the allocation-cost, makespan and runtime scalability

Scalable computing for earth observation - Application on Sea Ice analysis

In recent years, Deep learning (DL) networks have shown considerable improvements and have become a preferred methodology in many different applications. These networks have outperformed other classical techniques, particularly in large data settings. In earth observation from the satellite field, for example, DL algorithms have demonstrated the ability to learn complicated nonlinear relationships in input data accurately. Thus, it contributed to advancement in this field. However, the training process of these networks has heavy computational overheads. The reason is two-fold: The sizable complexity of these networks and the high number of training samples needed to learn all parameters comprising these architectures. Although the quantity of training data enhances the accuracy of the trained models in general, the computational cost may restrict the amount of analysis that can be done. This issue is particularly critical in satellite remote sensing, where a myriad of satellites generate an enormous amount of data daily, and acquiring in-situ ground truth for building a large training dataset is a fundamental prerequisite. This dissertation considers various aspects of deep learning based sea ice monitoring from SAR data. In this application, labeling data is very costly and time-consuming. Also, in some cases, it is not even achievable due to challenges in establishing the required domain knowledge, specifically when it comes to monitoring Arctic Sea ice with Synthetic Aperture Radar (SAR), which is the application domain of this thesis. Because the Arctic is remote, has long dark seasons, and has a very dynamic weather system, the collection of reliable in-situ data is very demanding. In addition to the challenges of interpreting SAR data of sea ice, this issue makes SAR-based sea ice analysis with DL networks a complicated process. We propose novel DL methods to cope with the problems of scarce training data and address the computational cost of the training process. We analyze DL network capabilities based on self-designed architectures and learn strategies, such as transfer learning for sea ice classification. We also address the scarcity of training data by proposing a novel deep semi-supervised learning method based on SAR data for incorporating unlabeled data information into the training process. Finally, a new distributed DL method that can be used in a semi-supervised manner is proposed to address the computational complexity of deep neural network training

Cross-Layer Service Discovery Mechanism for OLSRv2 Mobile Ad Hoc Networks

Service discovery plays an important role in mobile ad hoc networks (MANETs). The lack of central infrastructure, limited resources and high mobility make service discovery a challenging issue for this kind of network. This article proposes a new service discovery mechanism for discovering and advertising services integrated into the Optimized Link State Routing Protocol Version 2 (OLSRv2). In previous studies, we demonstrated the validity of a similar service discovery mechanism integrated into the previous version of OLSR (OLSRv1). In order to advertise services, we have added a new type-length-value structure (TLV) to the OLSRv2 protocol, called service discovery message (SDM), according to the Generalized MANET Packet/Message Format defined in Request For Comments (RFC) 5444. Each node in the ad hoc network only advertises its own services. The advertisement frequency is a user-configurable parameter, so that it can be modified depending on the user requirements. Each node maintains two service tables, one to store information about its own services and another one to store information about the services it discovers in the network. We present simulation results, that compare our service discovery integrated into OLSRv2 with the one defined for OLSRv1 and with the integration of service discovery in Ad hoc On-demand Distance Vector (AODV) protocol, in terms of service discovery ratio, service latency and network overhead.This work is partially supported by the Spanish Ministry of Science and Innovation through the Continuity of Service, Security and QoS for Transportation Systems (CONSEQUENCE) (TEC2010-20572-C02-01/02) and INcident monitoRing In Smart COmmunities (INRISCO) (TEC2014-54335-C4-2-R) projects. We thank the editor and anonymous reviewers for their constructive comments, which helped us to improve our manuscript

Forest management planning as a tool to enhance biodiversity protection

Non peer reviewe