Blockchains and smart contracts are gaining momentum as enabling technologies for a wide set of applications where data distribution and sharing among decentralized infrastructures is required. In this work, we present a distributed application developed using blockchain technologies that allows individuals and health insurance organizations to come into agreement during the implementation of the healthcare insurance policies in each contract. For this purpose, health standards and semantic web technologies were used for the formal expression of both the insured individual's data and contract terms. Accordingly, a fine-grained data access policy was applied for evaluating contract terms on the basis of relevant data captured in healthcare settings. A prototype was implemented involving the development of several different smart contracts for the Ethereum platform as well as the necessary visual environment for accessing them. The developed system validates various features related to blockchain and smart contract features that are briefly discussed in this work, part of which can be mitigated or resolved through the use of a private permissioned blockchain. The application of well-established techniques for potential malfunctions of external services could also boost the security of the system and prevent it from potential attacks.

The proliferation of Internet of Things (IoT) and edge devices constitute important an efficient orchestration of the edge computing infrastructures, calling the providers to rethink their decision making methods. The resource usage prediction can be a prominent source of information for adaptive resource allocation and task offloading. In this research, we propose a Gated Recurrent Neural Network multi-output regression model that leverage time series resource usage metrics. The edge computing infrastructures are characterized as dynamical and heterogeneous environments. This motivated us to propose the innovative Hybrid Bayesian Evolutionary Strategy (HBES) algorithm for automated adaptation of the resource usage models in order to to enhance the generality of our approach. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of RMSE and MAE.

In aiming to create added intelligence for Things interfacing with IoT principles in the form of Services, Smart Heating Management is consistently a field of promising research. In the currently demonstrated work, an approach will be described for an operational software framework of modular components that can create intelligence in Housing Units as those presented in our scenario. The framework is based on the lightweight Case Based Reasoning approach and principles in order to describe the Problem and Solution of heating management, as well as to extract knowledge from generic historical data. Collaboration between houses is included through the sharing of anonymized high level problem-solution data, as an instantiation of the same social learning principles that govern human behavior and enable Knowledge diffusion. The approach is validated through historical data acquired in two time periods from the Camden (London) community residencies and demonstrates an average of 22% savings in boiler active time. Knowledge sharing is also analyzed along with the benefits and pitfalls it might produce.

We present the Disaster Recovery Layer (DRL) that enables OpenStack-managed datacenter workloads, Virtual Machines (VMs) and Volumes, to be protected and recovered in another datacenter, in case of a disaster. This work has been carried out in the context of the EU FP7 ORBIT project that develops technologies for enabling business continuity as a service. The DRL framework is based on a number of autonomous components and extensions of OpenStack modules, while its functionalities are available through OpenStack's Horizon UI and command line interface. Also, the DRL's architecture is extensible, allowing for the easy and dynamic integration of protection, restoration and orchestration plug-ins that adopt new approaches. A distributed disaster detection mechanism was also developed for identifying datacenter disasters and alerting the DRL. For the evaluation of the DRL, a two (active and backup) datacenters testbed has been setup in respective sites in Umea and Lulea, 265km apart and connected through the Swedish national research and education network. In case of a disaster, traffic is redirected between the datacenters utilizing the BGP anycast scheme. The experiments performed, show that DRL can efficiently protect VMs and Volumes, with minimum service disruption in case of failures and low overhead, even when the available bandwidth is limited.

Deciding and reserving appropriate resources in the Cloud, is a basic initial step for adopters when employing an Infrastructure as a Service to host their application. However, the size and number of Virtual Machines used, along with the expected application workload, will highly influence its operation, in terms of the observed Quality of Service. This paper proposes a machine learning approach, based on Artificial Neural Networks, for mapping Quality of Service required levels and (expected) application workload to concrete resource demands. The presented solution is evaluated through a comercial Customer Relationship Management application, generating a training set of realistic workload and Quality of Service measurements in order to illustrate the effectiveness of the proposed technique in a real-world scenario.

Data management has always been a multi-domain problem even in the simplest cases. It involves, quality of service, security, resource management, cost management, incident identification, disaster avoidance and/or recovery, as well as many other concerns. In our case, this situation gets ever more complicated because of the divergent nature of a cloud federation like BASMATI. In this federation, the BASMATI Unified Data Management Framework (BUDaMaF), tries to create an automated uniform way of managing all the data transactions, as well as the data stores themselves, in a polyglot multi-cloud, consisting of a plethora of different machines and data store systems.

The paper presents an innovative Internet of Things architecture for building personalized services in the smart city context. The main blocks of the presented implementation comprise data flows implemented through Node-Red, Neo4j data store for handling the smart city big data and a recommendation service which is applied in order to offer personalized recommendations to the users. The current work studies integration of the various components, the modelling approach for user generated data combined with open big data and proceeds with the appropriate reference implementation and experimentation to validate the personalized recommendation services for innovative citizen-centric applications and use cases. Moreover, we study and validate performance issues of this Neo4j based recommendation service and evaluate it as a useful appliance for real-time big data application.

The integration of social networking concepts into Internet of Things systems is a burgeoning topic of research that promises to support novel and more powerful applications. In this paper we focus on the design and implementation of a highly scalable Trust and Reputation Model for the Internet of Things based on the social approach that the COSMOS project introduces, as part of its final results. We create our model by combining popular solutions proposed for Peer-to-Peer and mobile ad-hoc networks and adapting them on the Internet of Things concept. Each Thing can compute the Trust index of another Thing based on its own experiences, while it has the capability of determining its Reputation Index either by consulting its other “friends” (Followees) or referring to the Platform, a management system used in COSMOS. The model is tested through simulations of the proposed social system, demonstrating the ability of TRM-SIoT to achieve the Social Exclusion of malicious nodes and collectives from the network, with low computational overhead and high scalability. Furthermore, due to the adaptive nature of the system, Social Reintegration of these nodes is also possible.

Early, effective director-producer communication enables detection of resource-demanding film production requirements so that a workable production plan can be formed. Traditional communication tools are limited to articulating standard production elements as lists. Yet, production complexity can be affected by latent variables with indirect impact which remain unarticulated. An ontology-driven, interactive visualization system is proposed for exposing production complexity. The system is built upon a visual language for recording directorial choices, employed for system input. By exploiting Ontologies, the visual input is analyzed in real-time and semantic metadata are extracted representing production complexity with focus on latent variables. The metadata are transformed back into an aggregate visual form which combines with the visual input, creating useful synergies. In this way, the system creates a collaborative virtual workspace facilitating director-producer communications. A real-world deployment and an evaluation demonstrated the system's advantages over traditional tools.

We study the problem of trust inference in signed social networks, in which, in addition to rating items, users can also indicate their disposition towards each other through directional signed links. We explore the problem in a semisupervised setting, where given a small fraction of signed edges we classify the remaining edges by leveraging contextual information (i.e. the users' ratings). In order to model user behavior, we use deep learning algorithms i.e. a variation of Restricted Boltzmann machine and Autoencoders for user encoding and edge classification respectively. We evaluate our approach on a large-scale real-world dataset and show that it outperforms state-of-the art methods.

Cloud computing and virtualized infrastructures are currently the baseline environments for the provision of services in different application domains. While the number of service consumers increasingly grows, service providers aim at exploiting infrastructures that enable non-disruptive service provisioning, thus minimizing or even eliminating downtime. Nonetheless, to achieve the latter current approaches are either application-specific or cost inefficient, requiring the use of dedicated hardware. In this paper we present the reference architecture of a fault-tolerance scheme, which not only enhances cloud environments with the aforementioned capabilities but also achieves high-performance as required by mission critical every day applications. To realize the proposed approach, a new paradigm for memory and I/O externalization and consolidation is introduced, while current implementation references are also provided.

Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after their usage severe considerations have emerged with regard to their varying performance due to multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various Cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple Cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.

We present a methodology, called communication-aware virtual infrastructures (COMAVI), for the concurrent migration of multiple Virtual Machines (VMs) in cloud computing infrastructures, which aims at the optimum use of the available computational and network resources, by capturing the interdependencies between the communicating VMs. This methodology uses multiple criteria for selecting the VMs that will migrate, with different weights assigned to each of them. COMAVI also selects the computing sites/units where the migrating VMs will be hosted, by accounting for the way migration affects the logical (or virtual) topologies formed by the communicating VMs and viewing this selection as a logical topology reconfiguration problem. COMAVI resolves the maximum possible number of VM resource shortages, while tending to minimize the number of migrations performed, the induced network overhead, the logical topology reconfigurations required, and the corresponding service interruptions. We evaluate the proposed method through simulations, where we exhibit their performance benefits.

Recently, the idea that the Internet of Things (IoT) systems can be advantaged in many ways by integrating social networking concepts is gaining momentum. In this paper we present the social approach that the COSMOS project introduces. COSMOS supports knowledge flow between Things in order to provide a system that learns, observes and evaluates the usage and communication patterns and generates new knowledge. It focuses on the value of experience and experience-sharing and investigates models and principles designed for the social networks, which would provide it with the potential to support novel applications in more effective and efficient ways.

This paper presents a technique for admission control of a set of horizontally scalable services, and their optimal placement, into a federated Cloud environment. In the proposed model, the focus is on hosting elastic services whose resource requirements may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. The request may also be partially accommodated in federated external providers, if needed or more convenient. In finding the optimum allocation, the presented mechanism uses a probabilistic optimization model, which takes into account eco-efficiency and cost, as well as affinity and anti-affinity rules possibly in place for the components that comprise the services. In addition to modelling and solving the exact optimization problem, we also introduce a heuristic solver that exhibits a reduced complexity and solving time. We show evaluation results for the proposed technique under various scenarios.

The monitoring and the analysis of public clouds gains momentum, due to their widespread exploitation by individual users, researchers and companies for their daily tasks. We propose an algorithm for optimizing the cost and the utilization of a set of running Amazon EC2 instances by resizing them appropriately. The algorithm, namely Cost and Utilization Optimization (CUO) algorithm, receives information regarding the current set of instances used (their number, type, utilization) and proposes a new set of instances for serving the same load, so as to minimize cost and maximize utilization, or increase performance efficiency. CUO is integrated in Smart cloud Monitoring (SuMo), an open-source tool we develop for collecting monitoring data from Amazon Web Services (AWS) and analyzing them. A number of experiments are performed, using input data that correspond to realist AWS configuration scenarios, which exhibit the benefits of the CUO algorithm.

Currently there is significant and increasing demand for quick data storage and access any time. Storage is a fundamental need and Cloud environments accomplish this. The evolvement of the IT technologies offers the user to a large variety of useful services. In order for the user to have a guarantee for these services SLAs are signed. In this paper, we present an SLA Management mechanism for Cloud environments. SLA enforcement is based on rules that are updated in runtime in order to proactively detect possible SLA violations and handle them in an appropriate manner.

The paper shows how different concepts and technologies, such as high quality multimedia content production and delivery, social media interaction, and methodologies for monitoring and analysing social network activity, can be practically combined with traditional virtual reality installations into an innovative experimental system that enhances the experience of the visitors in cultural educational centers. Apart from offering a modern, immersive, richer experience to the visitors, the proposed system involves significant benefits for the organizers as well, supplying them with valuable feedback extracted from the monitoring and analysis mechanisms. The experimental system has been successfully deployed and used in the Foundation of the Hellenic World cultural center.

Future Internet applications raise the need for infrastructures that can facilitate real-time and interactivity without major modifications in the application domain. Such infrastructures should be able to efficiently adapt resource provisioning to the dynamic demands of the applications, the majority of which tends to be real-time and interactive, posing specific Quality of Service (QoS) requirements. In this context, we present initial experimentation outcomes of a set of mechanisms (namely QoS-oriented Service Management mechanisms) that aim at enabling the provision of QoS guarantees in cloud infrastructures. They incorporate an orchestration framework that allows for workflow management in the case of non-monolithic applications, an adaptable monitoring mechanism that collects and aggregates monitoring information from application and infrastructure levels, and an evaluator service enabling runtime adaptability through provisioning policies. We also demonstrate the operation of the implemented mechanisms and evaluate their effectiveness in a large-scale multi-site cloud facility.

In a world of multi-stakeholder information and assets provision on top of millions of real-time interacting and communicating things, systems based on Internet of Things (IoT) technologies aim at exploiting these assets in a resilient and sustainable way allowing them to reach their full potential. In this paper we present two innovative smart city IoT applications: the first one refers to heat and energy management, and aims at utilizing different resources (such as heat and electricity meters) in order to optimize use of energy in commercial and residential areas. The second application refers to cruise control for public transportation, and aims at utilizing different resources (such as environmental and traffic sensors) in order to provide driving recommendations that aim at eco efficiency. We also highlight the IoT challenges as well as potential enabling technologies that will allow for the realization of the proposed applications.

In modern utility computing infrastructures, like grids and clouds, one of the significant actions of a service provider is to predict the resources needed by the services included in its platform in an automated fashion for service provisioning optimization. Furthermore, a variety of software toolkits exist that implement an extended set of algorithms applicable to workload forecasting. However, their automated use as services in the distributed computing paradigm includes a number of design and implementation challenges. In this paper, a decoupled framework is presented, for taking advantage of software like GNU Octave in the process of creating and using prediction models during the service life cycle of a SOI. A performance analysis of the framework is also conducted. In this context, a methodology for creating parametric or gearbox services with multiple modes of operations based on the execution conditions is portrayed and is applied to transform the aforementioned service framework to optimize service performance. A new estimation algorithm is introduced, that creates performance rules of applications as black boxes, through the creation and usage of genetically optimized artificial neural networks. Through this combination, the critical parameters of the networks are decided through an evolutionary iterative process.

The emergence of Cloud technologies ultimately affected the service computing ecosystem introducing new roles and relationships as well as new architectural and business models. Along with the increase of the capabilities and potentials of the service providers came the increase of the information being available and the need to manage it in an efficient way. To this end, in this paper we present a management service architecture as well as the information model that the solution is based on. The model has been designed to serve a storage service provided by a Cloud infrastructure but the approach is implemented in a flexible and modular fashion in order to support different Cloud situations.

This paper presents an admission control test for deciding whether or not it is worth to admit a set of services into a Cloud, and in case of acceptance, obtain the optimum allocation for each of the components that comprise the services. In the proposed model, the focus is on hosting elastic services the resource requirements of which may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. In finding the optimum allocation, the presented admission control test uses an optimization model, which incorporates business rules in terms of trust, eco-efficiency and cost, and also takes into account affinity rules the components that comprise the service may have. The problem is modeled on the General Algebraic Modeling System (GAMS) and solved under realistic provider's settings that demonstrate the efficiency of the proposed method.

In this paper, we address the problem of managing SLAs in cloud computing environments. The idea is to take advantage of the content terms that concern the objects and support more efficient capabilities, such as quicker search and retrieval of the objects. As a result, the operational cost is reduced and consequently this fact lessens the customer's charge.

Nowadays the general interest on Cloud Computing as a technical and business solution is growing. The pool of available cloud applications, services and platforms is increasing leveraging new features for the cloud assets provisioned to the end user. This massive amount and divergence of cloud features across the cloud stack layers introduces considerable management overhead to the various stakeholders for the effective management of both the offered products and the system itself. In this paper we focus on the challenges arise for providing dynamic elasticity management as one the fundamental features of Clouds. To this direction, we introduce the concept of Elasticity as a Service and present a generic, service-oriented framework capable to fulfill, in a personalized manner, the elasticity management needs of any cloud environment and cloud-enabled application.

Cloud computing has changed considerably the architecture of modern IT systems introducing new design principles based on the notion of the service as main building unit. Decoupling the service offering from the underlying infrastructure, cloud computing allows for more efficient management of the required resources which in business level is translated into higher flexibility and increased profit. To this end, Clouds are not only an innovative technological environment but also a beneficial market for all the involved stakeholders. This market expands continuously and new service aggregations, customizations and business models are offered in order to cover the needs of all end-users. However, even in this new business ecosystem, the question still remains: “Which offering should I buy”? In this paper we present an approach for incorporating business intelligence in cloud marketplaces through a mechanism for answering this question based on the individual requirements and use profile of each particular cloud customer.

In the recent years, cloud computing has emerged as the new IT paradigm that promises elastic resources on a pay-per-use basis. The challenges of cloud computing are focused around massive data storage and efficient large scale distributed computation. Hadoop, a community driven Apache project has provided an efficient and cost effective platform for large scale computation using the map-reduce methodology, pioneered by Google. In this paper, the design of a Hadoop-based data management system as the front-end service for Cloud data management is investigated. This framework is enriched with Restful APIs in front of Hadoop and a series of components that aim to extend Hadoop's functionality beyond its well known back-end, heavy data processing scope. These components are used to enrich security, logging and data analysis features and also data access compatibility between different but interconnected Cloud providers (federated Clouds). Hadoop capabilities are also extended in a quest for intelligent decision making regarding the choice of the fittest services for federation in a federated cloud scenario, in addition to legally compliant behaviour regarding the geographical location of data storage.

Nowadays cloud computing can be considered as a key element of modern ICT systems, changing the technological and architectural aspects that these systems are designed and managed. The number and variety of applications, exploiting the advantages of this new computing paradigm, is increasing, emerging a new market of services and resources. Modern applications, from enterprise software to mobile and social networking apps are adapted and become available through the Cloud, allowing wider adoption and advanced functionality. Besides the numerous technical and technological advancements, cloud computing also leverages new business models and value networks. To this direction more entities are involved in the service delivery process and marketplaces are created to ease the development of applications through reusability and aggregation of services and resources. In this paper we present an innovative mechanism for the resolution of the customers' requirements which enhances the process of selecting cloud services from the business point of view.

In this paper we introduce the WR (Workflow Recognition) dataset. Recorded in the production line of a major automobile manufacturer, this dataset consists of sequences that depict workers executing industrial workflows. The heavy occlusions, outliers, the visually complicated background and the human-machinery interaction are among the factors that make this dataset a very challenging testbed for computer vision and image processing algorithms. We provide the original video sequences together with event labeling, as well as feature vectors extracted through our proposed scene representation methodology, and we refer to our results so far in workflow recognition using this dataset.

The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each “layer”. However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive e-Learning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.

The advancements in distributed computing have driven the emergence of service-based infrastructures that allow for on-demand provision of ICT assets. Taking into consideration the complexity of distributed environments, significant challenges exist in providing and managing the offered on-demand resources with the required level of Quality of Service (QoS), especially for real-time interactive multimedia applications. Common to these applications is the service-oriented approach being followed, thus applications consist of application service components that interact in order to provide the corresponding application functionality. In this paper we present an architectural design of a complete Workflow Management System (WfMS) for enacting application service components that have been deployed in a Virtualized Environment. The WfMS aims at synchronizing the application components, monitoring their execution and handling faults while adhering to the QoS requirements of the application.

In this work, we propose a framework for classifying structured human behavior in complex real environments, where problems such as frequent illumination changes and heavy occlusions are expected. Since target recognition and tracking can be very challenging, we bypass these problems by employing an approach similar to Motion History Images for feature extraction. Furthermore, to tackle outliers residing within the training data, which might affect severely the training algorithm of models with Gaussian observation likelihoods, we scrutinize the effectiveness of the multivariate Student-t distribution as the observation likelihood of the employed Hidden Markov Models. Additionally, the problem of visibility and occlusions is addressed by providing various extensions of the framework for multiple cameras, both at the feature and at the state level. Finally, we evaluate the performance of the examined approaches under real-life visual behavior understanding scenarios and we compare and discuss the obtained results.

This paper deals with the problem of scheduling workflow applications with quality of service (QoS) constraints, comprising real-time and interactivity constraints, over a service-oriented grid network. A novel approach is proposed, in which high-level advance reservations, supporting flexible start and end time, are combined with low-level soft real-time scheduling, allowing for the concurrent deployment of multiple services on the same host while fulfilling their QoS requirements. By undertaking a stochastic approach, in which a-priori knowledge is leveraged about the probability of activation of the application workflows within the reserved time-frame, the proposed methodology allows for the achievement of various trade-offs between the need for respecting QoS constraints (user perspective) and the need for having good resource saturation levels (service provider perspective).

Abstract The advent of heterogeneous and distributed environments, such as Grid environments, made feasible the solution to computational‐intensive problems in a reliable and cost‐effective manner. In parallel, workflows with increased complexity that require specialized systems to deal with them are emerging, so as to carry out more composite and mission‐critical applications. In that rationale, quality‐of‐service (QoS) issues need to be tackled in order to ensure that each application satisfies the corresponding user requirements. Therefore, considering the quality provision aspect as fundamental for enabling Grid applications to become QoS compliant, we present an approach for service selection using QoS criteria. The latter is achieved with a suite of components that allow the different mappings of application workflow processes to Grid services that not only meet the user goals and requirements but also maximize his/her benefit in terms of the offered QoS level. We also demonstrate the operation of the aforementioned suite of components and evaluate its performance and effectiveness using a Grid scenario, based on a 3D image rendering application. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Grids and mobile Grids can form the basis and the enabling technology for pervasive and utility computing due to their ability to being open, highly heterogeneous and scalable. In this paper we present a scheme for advancing quality of service (QoS) attributes, such as fault tolerance and prioritized scheduling, in OGSA‐based mobile Grids. The fault tolerance is achieved by producing and managing sufficient replicas of tasks submitted for execution on the mobile Grid resources. We design a simple and efficient prioritization scheme, which allows the scheduling of the tasks submitted by the Grid users as distinguished priorities that can be managed and exploited as a QoS parameter by the Grid infrastructure operator. The results that are presented show the efficiency of the proposed scheme in being simple and additionally enriching with reliability and QoS features the applications that are built on the concept of mobile Grids. Copyright © 2008 John Wiley & Sons, Ltd.

Hidden Markov (chain) models using finite Gaussian mixture models as their hidden state distributions have been successfully applied in sequential data modeling and classification applications. Nevertheless, Gaussian mixture models are well known to be highly intolerant to the presence of untypical data within the fitting data sets used for their estimation. Finite Student's t-mixture models have recently emerged as a heavier-tailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit these merits of Student's t-mixture models in the context of a sequential data modeling setting, we introduce, in this paper, a novel hidden Markov model where the hidden state distributions are considered to be finite mixtures of multivariate Student's t-densities. We derive an algorithm for the model parameters estimation under a maximum likelihood framework, assuming full, diagonal, and factor-analyzed covariance matrices. The advantages of the proposed model over conventional approaches are experimentally demonstrated through a series of sequential data modeling applications.

Factor analysis is a latent subspace model commonly used for local dimensionality reduction tasks. Fuzzyc-means (FCM) type fuzzy clustering approaches are closely related to Gaussian mixture models (GMMs), and expectation-maximization (EM) like algorithms have been employed in fuzzy clustering with regularized objective functions. Student'st-mixture models (SMMs) have been proposed recently as an alternative to GMMs, resolving their outlier vulnerability problems. In this paper, we propose a novel FCM-type fuzzy clustering scheme providing two significant benefits when compared with the existing approaches. First, it provides a well-established observation space dimensionality reduction framework for fuzzy clustering algorithms based on factor analysis, allowing concurrent performance of fuzzy clustering and, within each cluster, local dimensionality reduction. Second, it exploits the outlier tolerance advantages of SMMs to provide a novel, soundly founded, nonheuristic, robust fuzzy clustering framework by introducing the effective means to incorporate the explicit assumption about student'st-distributed data into the fuzzy clustering procedure. This way, the proposed model yields a significant performance increase for the fuzzy clustering algorithm, as we experimentally demonstrate.

A constantly increasing number of applications from various scientific sectors are finding their way towards adopting Grid technologies in order to take advantage of their capabilities: the advent of Grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. The aim of this paper is to demonstrate how multilevel tumour growth and response to therapeutic treatment models can be used in order to simulate clinical trials, with the long-term intention of better designing clinical studies and understanding their outcome based on basic biological science. For this purpose, a computer simulation model of glioblastoma multiforme response to radiotherapy has been applied to perform the aforementioned simulation in a real Grid environment by also taking into account historical data. The proposed approach yields very good results for the conducted virtual trial since these are in agreement with the outcome of the real clinical study, while the use of Grid technologies demonstrate and highlight their added-value.

This paper advocates the need for a mechanism that will allow the evaluation of the provided quality of service (QoS) by a service provider to a service customer in B2B service provisioning. Furthermore, this study goes on with presenting the feasibility of this mechanism by designing and testing a reference implementation that can be used in a service oriented architecture (SOA) environment which is able to support business application services. Experience gained in the frame of the NextGRID IST project that focused on the business perspectives of Grid computing and adopted SOA as its baseline architecture, has shown that such a mechanism is essential for enabling the economic viability of such large scale computing platforms.

Hidden Markov models using finite Gaussian mixture models as their hidden state distributions have been applied in modeling of time series that result from various noisy signals. Nevertheless, Gaussian mixture models are well-known to be highly intolerant to the presence of outliers within the fitting sets used for their estimation. Finite Student's-i mixture models have recently emerged as a heavier-tailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit those merits of Student's-i mixture models, we introduce in this paper a novel hidden Markov chain model where the hidden state distributions are considered to be finite mixtures of multivariate Student's-i densities and we derive an algorithm for the model parameters estimation under a maximum likelihood framework. We apply this novel approach in automatic gesture recognition and we show that our model provides a substantial improvement in data representation performance and computational efficiency over the standard Gaussian model.

A constantly increasing number of applications from various scientific sectors are finding their way towards adopting grid technologies in order to take advantage of their capabilities: the advent of grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. In this paper we present a grid-based approach for aggregation of data that are obtained from various sources (e.g. cameras, sensors) and their analysis with the use of genetic algorithms. By also taking into consideration general historical data and patient-specific medical information, we present the realization of the proposed approach with an application scenario for personalized healthcare and medicine.

AREA 2008 is the first ACM international workshop on analysis and retrieval of events, actions and workflows in video streams. Such research is nowadays critical for many real-life applications, such as area supervision, semantic characterization and annotation of video streams, quality assurance, and security. This workshop consists of 16 high quality papers organized in four thematic sessions. More specifically, the first session is dedicated to new objects tracking algorithms under complex environments and to object labeling techniques. The second session deals with methods, tools and architectures for detecting high level semantics (events, actions, and workflows) in video sequences. The third session presents new algorithms for analyzing video sequences oriented to detecting humans' actions or implicitly annotating multimedia content. Finally, the fourth includes a special session of the recent advantages of the ongoing research projects in the field of multimedia analysis, cognitive video supervision, personalized video annotation, fast retrieval of multimedia content in compressed domain and scheduling tools for interactive multimedia services. We hope that these proceedings will serve as a valuable reference for analysis of events in video streams.

Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzyc-means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications.

Abstract Enabling e‐Business applications on Grid computing has been an important topic of research as the beneficial perspective of Grids lies on solving computational intensive problems by large‐scale distributed resources. In this paper we present a toolkit for centralized workload prediction and access, in order to meet the requirements of execution of commercial business processes on Grid infrastructures as well as to take advantage of their cost‐effective capability for industrial users. The implementation of centralized workload prediction is achieved with an artificial neural network—available in the toolkit—each time a user completes a job execution. This approach has been adopted and validated within the framework of a GRIA IST project for aspecific industrial application, namely 3D image rendering, which meets the e‐Business application's standards. The accuracy of the prediction showed promising results and in combination with the centralized access capability makes e‐Business applications feasible on Grids. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the Earliest Deadline First and the First Come First Serve schemes. Our algorithm uses a max-min fair sharing approach for providing fair access to users. When there is no shortage of resources, the algorithm assigns to each task enough computational power for it to finish within its deadline. When there is congestion, the main idea is to fairly reduce the CPU rates assigned to the tasks, so that the share of resources that each user gets is proportional to the user’s weight. The weight of a user may be defined as the user’s contribution to the infrastructure or the price he is willing to pay for services or any other socioeconomic consideration. In our algorithms, all tasks whose requirements are lower than their fair share CPU rate are served at their demanded CPU rates. However, the CPU rates of tasks whose requirements are larger than their fair share CPU rate are reduced to fit the total available computational capacity in a fair manner.Three different versions of fair scheduling are adopted in this paper; the Simple Fair Task Order (SFTO), which schedules the tasks according to their respective fair completion times, the Adjusted Fair Task Order (AFTO), that refines the SFTO policy by ordering the tasks using the adjusted fair completion times, and the Max-min Fair Share (MMFS) scheduling policy, which simultaneously addresses the problem of finding a fair task order and assigning a processor to each task based on a Max-Min fair sharing policy. Experimental results and comparisons with traditional scheduling schemes, such as the Earliest Deadline First (EDF) and the First Come First Served (FCFS) are presented using three different error criteria. Validation of the simulations using real experiments of tasks generated from 3D image rendering processes is also provided. The three proposed scheduling schemes can be inte

Grids are typically used for solving large-scale resource and computing intensive problems in science, engineering, and commerce as they seem to be cost-effective for industrial users. In order to be able to meet this requirement the software modules developed should be designed to meet the requisites for commercial business processes on the grid. In this paper we present a module for predicting computational workload of jobs assigned for execution on commercially exploited grid infrastructures. The module aims to identify the complexity of a given job and predict the workload that it is going to stress on the grid infrastructure. The prediction is achieved with the use of a trained artificial neural network, which has been implemented, with the use of the open source software package Joone. The approach has been implemented and validated within the framework of GRIA IST project for a specific industrial based application namely, 3D image rendering. The evaluation of the approach showed very promising results not only for the adoption of an open source package in a commercial application but also concerning the accuracy of the prediction and the benefit that it can provide in grids for business.

An integrated, reconfigurable, adaptable and open system for mining, indexing and retrieving multimedia information based on a mobile agent technology scheme is presented. The system consists of three integral subsystems, namely the acquisition, the transformation and the distribution modules. Innovative algorithms are used to extract information from Web sources, transform it and deliver it to the end users. The system supports efficient content adaptation mechanisms, textual and visual summarization schemes (both sequential and hierarchical), automatic language translation, ontological representation, visual processing and Web-based data mining. Experimental analysis on real-life Web sites has been performed to test the efficiency of the proposed scheme and compare it with other approaches presented in the literature

Present multimedia services are provided through a heterogeneous set of networks. Because of the heterogeneity of the networks, long-term resource availability guarantees are difficult to obtain. Consequently, even if the resource requirements throughout the service could be accurately mapped, it would not be feasible to provide overall performance guarantees. Application adaptation arises as an appropriate solution for quality-of-service assurance in such dynamic service infrastructures. Application adaptation basically involves adapting application characteristics according to the network resource availability. We formulate the problem of adaptation of multimedia applications to network infrastructure as well as to user- and application-imposed constraints and preferences. We benefit from the good approximation, identification, and control capabilities of recurrent high-order neural networks and we introduce an algorithm that guarantees the resource constraints of the network infrastructure will not be violated while maintaining the user and application requirements.

A fully automated wrapper for information extraction from Web pages is presented. The motivation behind such systems lies in the emerging need for going beyond the concept of "human browsing". The World Wide Web is today the main "all kind of information" repository and has been so far very successful in disseminating information to humans. By automating the process of information retrieval, further utilization by targeted applications is enabled. The key idea in our novel system is to exploit the format of the Web pages to discover the underlying structure in order to finally infer and extract pieces of information from the Web page. Our system first identifies the section of the Web page that contains the information to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. STAVIES can operate without human intervention and does not require any training. The main innovation and contribution of the proposed system consists of introducing a signal-wise treatment of the tag structural hierarchy and using hierarchical clustering techniques to segment the Web pages. The importance of such a treatment is significant since it permits abstracting away from the raw tag-manipulating approach. Experimental results and comparisons with other state of the art systems are presented and discussed in the paper, indicating the high performance of the proposed algorithm.

We present a technology which enables wide-scale deployment of a unique and powerful data access and search tool. This tool makes it possible for content owners on the Internet to offer end-users the ability to search, assess, or otherwise examine their nondisclosable private data. The end-users may use arbitrary, distrusted assessment algorithms and receive the results which are produced. These results take the form of a meaningful digest, e.g., a useful statistic, a relevance assessment, etc. There exists some controllable danger of information theft which, however, is quantitatively dependent on system parameters and can be tuned accordingly. We consider a broad class of practical applications where brute force attacks exist which are easier and cheaper to implement than an attack through appropriately configured PIVOTS, making our system, effectively, very trustworthy. The actual creation of the search algorithm may be performed by the end-users themselves in specialized applications, or by third-party search-engine operators who offer their services to end-users. Sophisticated data mining technologies which are very often not directly available to content owners can thus be exploited to mine even private data collections. Users discovering interesting items in private data sets may purchase this specific data, but other desirable exploitation possibilities are also analyzed. A novel business model is presented and its potential for great impact on e-commerce is explored. Three diverse and important real-world applications are presented in conclusion.

Implementation of a commercial application to a grid infrastructure introduces new challenges in managing the quality-of-service (QoS) requirements, most stem from the fact that negotiation on QoS between the user and the service provider should strictly be satisfied. An interesting commercial application with a wide impact on a variety of fields, which can benefit from the computational grid technologies, is three-dimensional (3-D) rendering. In order to implement, however, 3-D rendering to a grid infrastructure, we should develop appropriate scheduling and resource allocation mechanisms so that the negotiated (QoS) requirements are met. Efficient scheduling schemes require modeling and prediction of rendering workload. In this paper workload prediction is addressed based on a combined fuzzy classification and neural network model. Initially, appropriate descriptors are extracted to represent the synthetic world. The descriptors are obtained by parsing RIB formatted files, which provides a general structure for describing computer-generated images. Fuzzy classification is used for organizing rendering descriptor so that a reliable representation is accomplished which increases the prediction accuracy. Neural network performs workload prediction by modeling the nonlinear input-output relationship between rendering descriptors and the respective computational complexity. To increase prediction accuracy, a constructive algorithm is adopted in this paper to train the neural network so that network weights and size are simultaneously estimated. Then, a grid scheduler scheme is proposed to estimate the queuing order that the tasks should be executed and the most appopriate processor assignment so that the demanded QoS are satisfied as much as possible. A fair scheduling policy is considered as the most appropriate. Experimental results on a real grid infrastructure are presented to illustrate the efficiency of the proposed workload prediction--scheduling algorithm compared to other approaches presented in the literature.

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

Industrial embedded supercomputing applications benefit from a systematic approach to fault tolerance. The EFTOS (embedded fault-tolerant supercomputing) framework provides a flexible and adaptable set of fault-tolerance tools from which the application developer can choose to make an embedded application on a parallel or distributed system more dependable. A high-level description (recovery language) helps the developer specify the fault-tolerance strategies of the application as a second application layer; this separates functional from fault-tolerance aspects of an application, thus shortening the development cycle and improving maintainability. The framework incorporates a backbone (to hook a set of fault-tolerance tools onto, and to coordinate the fault-tolerance actions) and a presentation layer (to monitor and test the fault tolerance behavior). A practical implementation is described with its performance evaluation, using an industrial case study from the energy-transport area, as well as an analytic deduction of the appropriateness of fault-tolerance techniques for various application profiles.

We propose the robust algorithm-configured emulation (RACE) scheme for efficient parallel computation and communication in the presence of faults. A wide variety of algorithms originally designed for fault-free meshes, tori, and k-ary n-cubes can be transformed to corresponding robust algorithm through RACE. In particular optimal robust algorithms can be derived for total exchange (TE) and ascend/descend operations with a factor of 1+o (1) slowdown. Also, RACE can tolerate a large number of faulty elements, without relying on hardware redundancy or any assumption about the availability of a complete subarray.

The development of the Greek text-to-speech (TTS) system by NTUA is based on the method of concatenative synthesis and follows the Bell Labs approach to this technique. Concatenative synthesis is one of the simplest methods for speech synthesis and at the same time bypasses most of the problems encountered by articulatory and formant synthesis techniques. The method relies on designing and creating the acoustic inventory of the language by taking real recorded speech, cutting it into segments and concatenating these segments back together during synthesis. The design and implementation of the acoustic database is a key factor for the performance of the synthesizer, since all the possible phone-to-phone transitions must be considered in order to minimize abrupt discontinuities and thus maximize the naturalness of the synthesized utterances.

The goal of this paper is to present the work carried out up to now for the development of the Greek Text-To-Speech (GRTTS) system by NTUA. The system under consideration is based on the method of concatenative synthesis and follows the Bell Labs approach to this technique. In order that the input text to the GRTTS is translated into continuous synthetic speech the following modules have already been studied and implemented: (i) module for the linguistic analysis of the input text; (ii) the acoustic inventory module. On the same time it is under development the duration module of the GRTTS, for the computation of the appropriate temporal structure of synthesized speech. The objectives of the above studies, in combination with the concatenative synthesis technique, which is one of the simplest methods for speech synthesis, are to bypass most of the problems encountered by other synthesis methods such as articulatory and formant synthesis systems. The major objective is to minimize abrupt discontinuities and thus maximize the naturalness of the synthesized utterances.

In this paper we introduce a new model for the definition and the implementation of quality of service (QoS) in distributed multimedia systems. The model provides both the users and the provider/manager of the system with a framework for the high level description of the system's configuration and operation, as well as with an application/user specific definition of QoS. The model maps this high level QoS description to quality requirements for the individual resources that compose the overall service. The implementation of the QoS of the overall service is done by providing probability of availability guarantees for certain levels of QoS. Given some constraints on the total assets available for the implementation of a specific service, the model computes the optimal utilisation of the system resources to implement a specific user/application QoS profile. The model is reduced to a general asset allocation problem (GA/sup 2/P) and we solve the GA/sup 2/P in a provably optimal way for a wide range of realistic services.

We present new results in the area of reconfiguration of stateful interactive processes in the presence of faults. More precisely, we consider a set of servers/processes that have the same functionality, i.e., are able to perform the same tasks and provide the same set of services to their clients. In the case when several of them turn out to be faulty, we want to reconfigure the system so that the clients of the faulty servers/processes are served by some other, fault-free, servers of the system in a way that is transparent to all the system clients. We propose a novel method for reconfiguring in the presence of faults: compensation paths. Compensation paths are an efficient way of shifting spare resources from where they are available to where they are needed. We also present optimal and suboptimal simple reconfiguration algorithms of low polynomial time complexity O(nmlog(n/sup 2//m)) for the optimal and O(m) for the suboptimal algorithms, where n is the number of processes and m is the number of primary-backup relationships. The optimal algorithms compute the way to reconfigure the system whenever the reconfiguration is possible. The suboptimal algorithms may sometimes fail to reconfigure the system, although reconfiguration would be possible by using the optimal centralized algorithms. However, suboptimal algorithms have other competitive advantages over the centralized optimal algorithms with regard to time complexity and communication overhead.

The complexity of modern networks calls for decentralized control schemes where each user makes its control decisions independently based on some individual performance objectives. The operating points of such noncooperative networks are the Nash equilibria of the underlying control game. Nash equilibria are generically inefficient and lead to suboptimal network performance. Using routing as a control paradigm, a methodology is devised for overcoming this inefficiency based on pricing mechanisms. Assuming that the price for usage of each link's proportional to the congestion level at the link, is shown that the provider can enforce any operating point it deems efficient by offering the capacity of the various links at discount prices. The incentive compatible discount vector is shown to be unique to the extent of a multiplicative constant, and its structure is specified explicitly. An adaptive algorithm for distributed computation of the incentive compatible discount vector is introduced. The applicability of the results in various practical scenarios is investigated by means of a prototype that implements the routing game as a Web-based game.

A metacomputing environment is presented, suitable for the efficient execution of computationally intensive parallel jobs that may require/generate large volume of data. A cluster of networked computing nodes as a physical layer to the distributed architecture is considered. A job model for the submitted tasks is presented that derives from real-life application areas. The proposed distribution of work over the nodes of the execution network is proven optimal, in terms of minimizing the execution time, with respect to the availability of resources. We conclude by presenting our experience of resting the environment for computing-intensive 3D rendering jobs derived from the ESPRIT project EROPPA.

In this paper a pricing technique for resource management in an intranet environment is given. The price of the services are dynamically determined by the servers of the network, taking into account the general policy of the manager of the network. Both decentralized and centralized control is used to increase the efficiency of the network. An architecture imposing small implementation cost is presented. Implementation issues are discussed and experimental results are given.

The paper considers the problem of rendering post production applications in a distributed environment. The aim is to limit the concessions that conventional, in-house methods force graphics operators to make to the quality of the end products to get them on time and within budget. A reference architecture for distributed rendering is proposed that takes advantage of the parallel properties of the application to reduce the job turnaround time. Performance issues that stimulate the industrial sector to use the environment are discussed through a simplified evaluation framework. The authors also examine how to serve multiple customers in a metacomputing environment employing limited resources. User strategies for submitting jobs over a priority-based service system that minimize the associated rendering cost are defined. Furthermore, a pricing scheme is implemented that succeeds in driving the user actions to an operating point in which certain provider objectives are met and, at the same time, discourages the users from utilizing a congested system, in which meeting of deadlines cannot be assured. Results and directions are not only restricted to the post production area, but apply over a wide range of remote operations.

We consider the problem of scheduling tasks on multiprocessor architectures in the presence of communication delays. Given a set of dependent tasks, the scheduling problem is to allocate the tasks to processors such that the pre-specified precedence constraints among the tasks are obeyed and certain cost-measures (such as the computation time) are minimized. Several cases of the scheduling problem have been proven to be NP-complete. Nevertheless, there are polynomial time algorithms for interesting special cases of the general scheduling problem. Most of these results, however, do not take into consideration the delays due to message passing among processors. In this paper we study the increase in time complexity of scheduling problems due to the introduction of communication delays. In particular, we address the open problem of scheduling Out-forests (In-forests) in a multiprocessor system of m identical processors when communication delays are considered. The corresponding problem of scheduling Out-forests (In-forests) without communication delays admits an elegant polynomial time solution as presented first by Hu in 1961; however, the problem in the presence of communication delays has remained unsolved. We present here first known polynomial time algorithms for the computation of the optimal schedule when the number of available processors is given and bounded and both computation and communication delays are assumed to take one unit of time. Furthermore, we present a linear-time algorithm for computing a near-optimal schedule for unit-delay out-forests. The schedule's length exceeds the optimum by no more than (m-2) time units, where m is the number of processors. Hence for two processors the computed schedule is strictly optimum.

We propose distributed algorithms for assigning tasks to processors in a distributed system, as well as algorithms for reassigning tasks from a failed processor to the remaining fault free processors in the system. The assignment and reassignment of the tasks should 1) make an efficient use of the capacity of the processors in the system, 2) distribute the load in a balanced way among the processors in the system, 3) assign tasks to processors for which they have a large preference (affinity), and 4) minimize the disturbance that is introduced by the migration of some tasks from one processor to another. We show that the distributed algorithms perform very closely to the centralized ones, even though they hold and maintain information only about processors in their local neighborhood.>

A polynomial time algorithm for solving the combinatorial problem that underlies the reconfiguration issues in the m1/2-track-m-spare model, for any arbitrary m, is discussed. The following combinatorial problem is solved: Given a set of points in a two-dimensional grid, find a set of noninteracting straight lines such that every line starts at a point and connects to one of the boundaries of the grid, there are no more than m lines overlapping in any row or column of the grid, and there are no near-miss situations. The time complexity of the algorithm is shown to be O(m mod F mod /sup 2/), where mod F is the number of faulty processors.>

Present new results on systematic procedures for reconfiguring processor arrays in the presence of faulty processors. In particular, the authors consider models that use multiple tracks along every channel and a single spare row (or column) of processing elements (PEs) along each boundary of the array. In the presence of faulty PEs the general methodology for reconfiguration involves replacing every faulty PE logically (rather than physically) by a spare PE through a sequence of logical substitutions; these sequences of substitutions are referred to as compensation paths. The authors show that if there exists a set of compensation paths subjected only to the constraints of continuity and nonintersection, then routing channels with three tracks are enough for the reconfiguration of the array. They refer to the underlying model as a S-track-l-spare model; this is done to distinguish it from other models that not only use multiple tracks but also multiple spare rows (or columns) along each boundary. An efficient algorithm for reconfiguration in our 3-track-1-spare model is presented and its performance evaluated. Experimental results show that the 3-track-1-spare model has much higher reconfiguration probability than other models that use considerably more spare processors.>