In this paper, a framework to integrate negotiation capabilities - particularly components implementing a negotiation strategy - into mobile agents is introduced. The framework is based on a plug-in mechanism enabling...
详细信息
ISBN:
(纸本)0769503403
In this paper, a framework to integrate negotiation capabilities - particularly components implementing a negotiation strategy - into mobile agents is introduced. The framework is based on a plug-in mechanism enabling a dynamic composition of negotiating agents out of pluggable modules. Strategy modules are further decomposed into parallel execution units based on the notion of an actor system.
Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based o...
详细信息
Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, service quality is often compromised. Aimed to address this issue, this paper proposes a novel approach to forecast the connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile operating systems, such as Android. We evaluate our approach with real sensor data and show how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making on the mobile edge of the network.
Markets for mobile applications offer myriads of apps ranging from simple to quite demanding ones. The latter are on the rise since every new generation of smartphones is equipped with more resources (CPU, memory, ban...
详细信息
Markets for mobile applications offer myriads of apps ranging from simple to quite demanding ones. The latter are on the rise since every new generation of smartphones is equipped with more resources (CPU, memory, bandwidth, energy) to even allow resource-demanding services like speech- or face recognition to be executed locally on a device. But compared to their stationary counterparts, mobile devices remain comparatively limited in terms of resources. Because of this, current approaches aim at extending mobile device capabilities with computation and storage resources offered by cloud services or other nearby devices. This paradigm, known as Mobile Cloud Computing (MCC), is challenged by the dynamically changing context of mobile devices, which developers are required to take into account to decide, e.g., which application parts are when to offload. To rise to such and similar challenges we introduce the concept of Generic Context Adaptation (GCA), a data mining process that facilitates the adaptation of (mobile) applications to their current and future context. Moreover, we evaluate our approach with real usage data provided by the Nokia Mobile Data Challenge (MDC) as well as with CloudAware , a context-adaptive mobile middleware for MCC that supports automated context-aware self-adaptation techniques.
Along with the rise of mobile handheld devices the resource demands of respective applications grow as well. However, mobile devices are still and will always be limited related to performance (e.g., computation, stor...
详细信息
Along with the rise of mobile handheld devices the resource demands of respective applications grow as well. However, mobile devices are still and will always be limited related to performance (e.g., computation, storage and battery life), context adaptation (e.g., intermittent connectivity, scalability and heterogeneity) and security aspects. A prominent solution to overcome these limita- tions is the so-called computation offloading, which is the focus of mobile cloud computing (MCC). However, current approaches fail to address the complexity that results from quickly and constantly changing context conditions in mobile user scenarios and hence developing effective and efficient MCC applications is still challenging. Therefore, this paper first presents a list of re- quirements for MCC applications together with a survey and classification of current solutions. Furthermore, it provides a design guideline for the selection of suitable concepts for different classes of common cloud-augmented mobile applications. Finally, it presents open issues that developers and researchers should be aware of when designing their MCC-approach.
In disaster situations communication and power infrastructure could be damaged, disrupting their associated services. This could delay relief efforts and cost more lives. In situations where connections are damaged an...
详细信息
Learning programming is hard - teaching it well is even more challenging. At university, the focus is often on functional correctness and neglects the topic of clean and maintainable code, despite the dire need for de...
详细信息
Determine the appropriate loop transformations is an essential process in the automatic parallelization field. The sequence of loop transformation to be applied also must be considered. Selection of loop transformatio...
详细信息
ISBN:
(纸本)1601320841
Determine the appropriate loop transformations is an essential process in the automatic parallelization field. The sequence of loop transformation to be applied also must be considered. Selection of loop transformations faces many challenges, it is needed an experienced. In this paper, An Intelligent Loop Transformation Selector (ILTS);as a part of parallelizing tool project, was developed to overcome on these challenges and imitate an experienced. A Kohonen's Self-Organizing Map (SOM) neural network is used to select the appropriate loop transformation or sequence of them. Neural Networks offer intelligent transformations selection to reduce or eliminate the dependencies and maximize the parallelization in the sequential code. The experimental results show that ILTS chooses loop transformations successfully in most cases. This tool can be integrated with any parallelizing compiler to enhance loop transformation selection process.
In this paper we present the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a derivative of standard Multilayer Percep...
详细信息
In this paper we present the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a derivative of standard Multilayer Perceptron (MLP) neural networks optimized for two-dimensional pattern recognition problems such as Optical Character Recognition (OCR) or face detection. We describe the basic parts of a CNN and demonstrate the performance and scalability improvement that can be achieved by shifting the computation-intensive tasks of a CNN to the GPU. Depending on the network topology training and classification on the GPU performs 2 to 24 times faster than on the CPU. Furthermore, the GPU version scales much better than the CPU implementation with respect to the network size.
Classification turns chaotic knowledge into regularity by systematizing a domain and providing a common vocabulary. Currently there is a lack of systematic and comprehensive studies in organization and classification ...
详细信息
Classification turns chaotic knowledge into regularity by systematizing a domain and providing a common vocabulary. Currently there is a lack of systematic and comprehensive studies in organization and classification of Grid faults. We address this gap with a multi-perspective Grid fault taxonomy describing an incident using eight different characteristics. It is hard to define a taxonomy of broad validity and acceptance that satisfies the vast number of requirements of the many Grid user communities. Nevertheless we proof that our taxonomy can serve as a solid basis for defining project-specific custom classification schemes by giving a concrete example created for a state-of-the-art Grid middleware environment.
Many time-critical and data-intensive distributed applications for the computing continuum depend on low-latency, scalable, and highly available distributed key value storages. In this paper, we introduce SDKV, a scal...
详细信息
ISBN:
(纸本)9798400702341
Many time-critical and data-intensive distributed applications for the computing continuum depend on low-latency, scalable, and highly available distributed key value storages. In this paper, we introduce SDKV, a scalable -Smart and distributed Key-Value- store for the Edge-Cloud continuum to automatically place data in close proximity to clients resulting in low response times. The clients of SDKV can influence data availability and access latency by specifying the number of replicas and the desired level of data consistency (strong or eventual) on a per key-value pair basis, which favors the support of a wide range of applications. Results reveal that for different workloads and client access behaviors, SDKV outperforms existing distributed data storages and their data placement algorithms by 12--69% for both consistency models. Moreover, the proposed placement algorithm of SDKV provides fast decision times and scales linearly with the number of keys.
暂无评论