With the recent advances in cloud computing and the improvement in the capabilities of mobile devices in terms of speed, storage, and computing power, Mobile Cloud Computing (MCC) is emerging as one of important branches of cloud computing. MCC is an extension of cloud computing with the support of mobility. In this paper, we first present the specific concerns and key challenges in mobile cloud computing, we then discuss the different approaches to tackle the main issues in MCC that have been introduced so far, and finally we focus on describing the proposed overall architecture of a middleware that will contribute to providing mobile users data storage and processing services based on their mobile devices capabilities, availability, and usage. A prototype of the middleware is developed and three scenarios are described to demonstrate how the middleware performs in adapting the provision of cloud web services by transforming SOAP messages to REST and XML format to JSON, in optimizing the results by extracting relevant information, and in improving the availability by caching. Initial analysis shows that the mobile cloud middleware improves the quality of service for mobiles, and provides lightweight responses for mobile cloud services.
The services landscape is changing with the growing adoption by businesses of the Service Oriented Architecture (SOA), the migration of business solutions to the cloud, and the proliferation of smartphones and Internet-enabled handheld devices to consume services. To meet their business goals, organizations increasingly demand services, which can satisfy their functional and non-functional requirements. Service Level Agreements (SLAs) are seen as the means to guarantee the continuity in service provisioning and required levels of service. In this paper, we propose a framework for service provisioning, which aims at providing support for automated SLA negotiation and management. The Service Broker component carries out SLA negotiation with selected service-providers on behalf of service-consumers. Multi-rounds of negotiations are very often required to reach an agreement. In each round, the negotiating parties bargain on multiple SLA parameters by trying to maximize their global utility functions. The monitoring infrastructure is in charge of observing SLA compliance monitoring using measurements obtained from independent third party monitoring services.
The simultaneous use of the new computing paradigms: Domain Specific Modeling, Context Oriented Computing and Service Oriented Computing, raises many challenges. Particularly, the challenge of engineering such systems, which consists of the definition of modeling approaches, processes, techniques and tools to facilitate their construction. The proposed MDSD approach for context-aware service oriented systems is based on Domain Specific Language Engineering. The Context-Aware, Domain Specific and Service Oriented (CADSSO) development approach is based on five models. The first one is domain specific context model; it symbolizes the services context of use. The second is domain specific services model; it takes care of services modeling. The third is service variability model; it formulates services variants. The fourth is adaptation rules model, which is the joint between service variability model and context model. The fifth is business rules model, used to model domain business. A code generator is in charge of the transformation of the five models to the final code.
Software module clustering is generally a difficult and challenging problem in software engineering. In the same way, service identification plays a critical role in service engineering. Existing Service identification approaches are often prescriptive and based on the architect's experience thus could result in non-optimal designs which results in complicated dependencies between services. In this paper we proposes top down approach to identify automatically services from business process by using several design metrics. Service boundaries are identified from business processes by automated search, guided by a multi-objective fitness function and using a clustering combinatorial particle swarm optimization algorithm. In order to evaluate the effectiveness of the proposed approach, a set of experiments were performed. The experimentation results of this empirical study denotes that our approach achieves better results in term of performance and convergence speed.
The paper deals with the definition of procedure that enables one to determine, for a given plant, if all faults can be detected and located after a finite sequence of observable events. More formally, the diagnosability is the property that every fault can be correctly detected from the observable events of the system after its occurrence no later than a bounded number of events. In this paper, the diagnosability problem of Discrete Event Systems DESs is studied. As modeling tool, finite-state automaton in an event-based framework is used. A necessary and sufficient condition of diagnosability of such systems is proposed. The results proposed in this paper allow checking the diagnosability of discrete event systems in an efficient way, i.e. in polynomial time.
The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11. The authors demonstrate here a real world application of determining one's location with the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. They detail their implementation of an Android application which communicates with the Cisco Context-Aware Mobility system to visually display the location of the mobile device. The application was tested in a production environment and limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified. The authors found that to obtain optimal accuracy, a device must be detected by four or more Access points so a recommended distribution for an indoor positioning system built on the Cisco context-aware mobility framework is for an Access Point to be placed every 12-20 linear meters.
Very great research efforts have been made in the last decades to further develop and promote electric vehicles EVs, their charging infrastructures, and operation techniques. However, little attention has been paid so far to the management of their charging planning, EVs assignment and mainly drivers' assistance to get into adequate charging stations CSs. The charging planning and EVs assignment need to be predicted taking into consideration all operating constraints of charging systems including EV characteristics, status of CSs, road traffic, etc. This paper presents a discrete event driven model for EVs predictive charging. The authors mainly focus on behavior modeling of the charging system using max, + algebra and Petri nets. The model is then used to anticipate maximum charging times and charging rates of EVs while respecting their various constraints.
Cloud Computing becomes interesting for enterprises across all branches. Renting computing capabilities from external providers avoids initial investments, as only those resources have to be paid that were used eventually. Especially in the context of “Big Data” this pay-as-you-go accounting model is particularly important. The dynamically scalable resources from the Cloud enable enterprises to store or analyze these huge amounts of unstructured data without using their own hardware infrastructure. However, Cloud Computing is currently facing severe data security and protection issues. These challenges require new ways to store and analyze data, especially when huge data volumes with sensitive data are stored at external locations. The presented approach separates data on database table level into independent chunks and distributes them across several clouds. Hence, this work is a contribution to a more secure and resilient cloud architecture as multiple public and private cloud providers can be used independently to store data without losing data security and privacy constraints.
Real time impact in many applications is the subject of a recent field of studies in information systems. Web services are a solution for the integration of distributed information systems that are autonomous, heterogeneous and auto adaptable to the context. This impact can resolve many problems in different systems based on Service Oriented Architecture SOA and web services. In this paper, the authors are interested in defining an approach to provide the different needs of self-adaptability of SOA to the context based on workflow, define the real time goal in their approach and show the feasibility and performance evaluation of their approach in an ambulance trajectory case study.
In this paper the authors investigate what factors can promote population diversity. They compare different partner selection models and strategy mobility on the Battle of Sexes game. This is a game with a coordination dilemma where players must decide which event to attend given that each one has its preferred event but they prefer going together. They investigate two types of partner selection: one based in private information and another based on public information, which is based on an opinion model. The authors analyze two variants of the opinion model. Experimental analysis shows that partner selection plays a minor role of favoring population diversity. One of the most important factors is strategy mobility either implicitly through mutation or explicitly when an offspring is placed in a different location.
The improved particle filter based simultaneous localization and mapping (SLAM) has been developed for many robotic applications. The main purpose of this article is to demonstrate that recent heterogeneous architectures can be used to implement the FastSLAM2.0 and can greatly help to design embedded systems based robot applications and autonomous navigation. The algorithm is studied, optimized and evaluated with a real dataset using different sensors data and a hardware in the loop (HIL) method. Authors have implemented the algorithm on a system based embedded applications. Results demonstrate that an optimized FastSLAM2.0 algorithm provides a consistent localization according to a reference. Such systems are suitable for real time SLAM applications.
The aim of adaptive user interface is to provide different layouts and relevant information according to the current context-of-use users, platforms and environments. Today, these systems are indispensable to those who want to retrieve appropriate information with less effort at anytime and anywhere. In this paper, the authors present an approach to automatically evaluate UI adaptation at runtime. The idea consists on foreseeing the evaluation from the early stages of application development by integrating a tracing system which represents the first phase of a user-centred approach for the design and the evaluation of adaptive system AS called MetTra evaluation METhod based on a TRAcing system. In fact, the authors will explain in depth the stages of tracing mechanism integration in AS design, with illustrations concerning transport applications. Finally, they will propose some future works.
A numerical solution of Hodgkin Huxley equations is presented to simulate the spiking behavior of a biological neuron. The solution is illustrated by building a graphical chart interface to finely tune the behavior of the neuron under different stimulations. In addition, a Multi-Agent System MAS has been developed to simulate the Visual Attention Network Model of the brain. Tasks are assigned to the agents according to the Attention Network Theory, developed by neuroscientists. A sequential communication model based on simple objects has been constructed, aiming to show the relations and the workflow between the different visual attention networks. Each agent is being used as an analogy to a role or function of the visual attention systems in the brain. Some experimental results based on this model have been presented in an earlier paper. The two approaches are at the moment not integrated. The long term goal is to develop an integrated parallel layered object model of the visual attention process, as a tool for simulating neuron interactions described by Hodgkin Huxley's equations or the Leaky-Integrate-and-Fire model.
This paper concerns the problem of diagnosing the occurrence of permanent fault events in partially observed discrete event systems modelled as finite-state automata in a event-based framework. The first step is the verification of the existence of diagnosers (the diagnosability problem) for such systems. We define a necessary and sufficient condition of diagnosability of such systems. The results proposed in this paper allow testing the diagnosability of discrete event systems in an efficient way, i.e. in polynomial time. The diagnosability test is stated in terms of existence of similar cycles of observable events both in the faultless model and in the faulty model.
Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from workflow tasks specification, decentralizing the control of workflow activities, and allowing their tasks to run autonomous in distributed infrastructures, for instance on Clouds. Furthermore many workflow tools only support the execution of Direct Acyclic Graphs (DAG) without the concept of iterations, where activities are executed millions of iterations during long periods of time and supporting dynamic workflow reconfigurations after certain iteration. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on the Process Networks model, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures, e. g. on Clouds. Each AWA executes a Task developed as a Java class that implements a generic interface allowing end-users to code their applications without concerns for low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables support to dynamic workflow reconfiguration and monitoring of the execution of workflows. We describe how AWARD supports dynamic reconfiguration and discuss typical workflow reconfiguration scenarios. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to a small dedicated cluster and the Amazon (Elastic Computing EC2) Cloud.
Prolongation of the lifetime has become a key challenge in design and implementation of Wireless Multimedia Sensor Networks (WMSNs). The energy consumed in multimedia sensor nodes is much more than in the scalar sensors; a multimedia sensor captures images or acoustic signals containing a huge amount of data while in the scalar sensors a scalar value is measured (e.g., temperature). On the other hand, given the large amount of data generated by the visual nodes, both processing and transmitting image data are quite costly in terms of energy in comparison with other types of sensor networks. Therefore, energy efficiency is a main concern in WMSNs. In this paper an energy efficient collaborative mechanism for monitoring is proposed. The proposed scheme employs a mixed random deployment of acoustic and visual sensor nodes. Acoustic sensors detect and localize the occurred event/object(s) in a duty-cycled manner by sampling the received signals and then trigger the visual sensor nodes covering the objects to monitor them. Hence, visual sensors are warily scheduled to be awakened just for monitoring the object(s) detected in their domain, otherwise they save their energy.
As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and how to reduce the cost of configuration are essential issues in cloud computing. In this paper, the authors propose a framework to achieve these objectives by optimizing VM placement and deciding when and how to perform the VM reconfigurations. The authors leverage the vector arithmetic to model the objective of balancing the multiple resource utilization and propose an optimization method for the static VM placement. Then the authors propose a two-level runtime reconfiguration policy, including the local adjustment and the parallel migration, to minimize the reconfiguration cost. Finally, the authors implement a prototype to validate and evaluate the proposed mechanism with a set of preliminary experiments, which shows that our work can maximize the resource utilization while effectively reducing the cost of the runtime reconfiguration.
Context-awareness is a quintessential feature of ubiquitous computing. Contextual information not only facilitates improved applications, but can also become significant security parameters — which in turn can potentially ensure service delivery not to anyone anytime anywhere, but to the right person at the right time and place. Specially, in determining access control to resources, contextual information can play an important role. Access control models, as studied in traditional computing security, however, have no notion of context-awareness; and the recent works in the nascent field of context-aware access control predominantly focus on spatio-temporal contexts, disregarding a host of other pertinent contexts. In this paper, with a view to exploring the relationship of access control and context-awareness in ubiquitous computing, the authors propose a comprehensive context-aware access control model for ubiquitous healthcare services. They explain the design, implementation and evaluation of the proposed model in detail. They chose healthcare as a representative application domain because healthcare systems pose an array of non-trivial context-sensitive access control requirements, many of which are directly or indirectly applicable to other context-aware ubiquitous computing applications.
With the on-demand ability of cloud computing, the performance requirement of a cloud application can be satisfied by adding a certain amount of computing resources to or removing some from the application in response to the workload fluctuation. However, the problem of the availability of application influenced by VM-based physical relative locations during resource scaling process is a challenge and has not been widely discussed yet. In this paper, the authors present a novel availability-based computing model to describe availability attribute of one application in the hierarchical topology of clouds. Moreover, the authors propose an availability-aware scaling mechanism by performing both vertical and horizontal resizing to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and the proposed dynamic scaling approach achieves the objectives of meeting availability demands and minimizing the total cost.
Continuous development and evolution of mobile communications toward end user expectations has led to heterogeneous mobile networks in the most general sense of the word. The mixture of core network infrastructures and radio access technologies, due to the arrival of new technologies while older ones are still used and not fully exploited, brings mobile operators to a complex business environment. Such a situation threatens future profit margins since the cost of running mobile networks rises with greater pace than total profit. A significant share in the total costs of running a mobile network belongs to energy consumption. Decreasing energy costs by designing hardware with low-energy consumption characteristics and site collocation is already under way, but additional sparing could be achieved through soft solutions or adaptive networks. To reduce operational costs, the crucial role will be played by the self-organizing network paradigm, featured by network management systems and operations/business support systems. In this paper, the authors provide an overview of adaptive and resilient concepts appropriate for improving the energy efficiency of mobile access networks. More precisely, they present the most promising self-organizing network solutions in the radio access and backhaul part of mobile networks, the implementation of which can bring a synergetic effect in terms of significant energy savings.
