Antonios Litke

Efficient task replication and management for adaptive fault tolerance in mobile Grid environments

Fault tolerant Grid computing is of vital importance as the Grid and Mobile computing worlds converge to the Mobile Grid computing paradigm. We present an efficient scheme based on task replication, which utilizes the Weibull reliability function for the Grid resources so as to estimate the number of replicas that are going to be scheduled in order to guarantee a specific fault tolerance level for the Grid environment. The additional workload that is produced by the replication is handled by a resource management scheme which is based on the knapsack formulation and which aims to maximize the utilization and profit of the Grid infrastructure. The proposed model has been evaluated through simulation and has shown its efficiency for being used in a middleware approach in future mobile Grid environments.

An innovative workflow mapping mechanism for Grids in the frame of Quality of Service

The advent of Grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. As workflow systems carry out more complex and mission-critical applications, Quality of Service (QoS) analysis serves to ensure that each application meets user requirements. In that frame, we present a novel algorithm which allows the mapping of workflow processes to Grid provided services assuring at the same time end-to-end provision of QoS based on user-defined parameters and preferences. We also demonstrate the operation of the implemented algorithm and evaluate its effectiveness using a Grid scenario, based on a 3D image rendering application.

Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing

In this paper, we propose an efficient non-linear task workload prediction mechanism incorporated with a fair scheduling algorithm for task allocation and resource management in Grid computing. Workload prediction is accomplished in a Grid middleware approach using a non-linear model expressed as a series of finite known functional components using concepts of functional analysis. The coefficient of functional components are obtained using a training set of appropriate samples, the pairs of which are estimated based on a runtime estimation model relied on a least squares approximation scheme. The advantages of the proposed non-linear task workload prediction scheme is that (i) it is not constrained by analysis of source code (analytical methods), which is practically impossible to be implemented in complicated real-life applications or (ii) it does not exploit the variations of the workload statistics as the statistical approaches does. The predicted task workload is then exploited by a novel scheduling algorithm, enabling a fair Quality of Service oriented resource management so that some tasks are not favored against others. The algorithm is based on estimating the adjusted fair completion times of the tasks for task order selection and on an earliest completion time strategy for the grid resource assignment. Experimental results and comparisons with traditional scheduling approaches as implemented in the framework of European Union funded research projects GRIA and GRIDLAB grid infrastructures have revealed the outperformance of the proposed method.

Managing service level agreement contracts in OGSA-based Grids

Grids and mobile Grids can form the basis and the enabling technology for pervasive and utility computing due to their ability to be open, highly heterogeneous and scalable. However, the process of selecting the appropriate resources and initiating the execution of a job is not enough to provide quality in a dynamic environment such as a mobile Grid, where changes are numerous, highly variable and with unpredictable effects. In this paper we present a scheme for advancing and managing Quality of Service (QoS) attributes contained in Service Level Agreement (SLA) contracts of Grids that follow the Open Grid Services Architecture (OGSA). In order to achieve this, the execution environment of the Grid infrastructure establishes and exploits the synergies between the various modules of the architecture that participate in the management of the execution and the enforcement of the SLA contractual terms. We introduce an Execution Management Service which is in collaboration with both the application services and the network services in order to provide an adjustable quality of the requested services. The components that manage and control the execution in the Grid environment interact with the suit of the SLA-related services exchanging information that is used to provide the quality framework of the execution with respect to the agreed contractual terms. The described scheme has been implemented in the framework of the Akogrimo IST project.

An Advanced Architecture for a Commercial Grid Infrastructure

Grid Infrastructures have been used to solve large scale scientific problems that do not have special requirements on QoS. However, the introduction and success of the Grids in commercial applications as well, entails the provision of QoS mechanisms which will allow for meeting the special requirements of the users-customers. In this paper we present an advanced Grid Architecture which incorporates appropriate mechanisms so as to allow guarantees of the diverse and contradictory users’ QoS requirements. We present a runtime estimation model, which is the heart of any scheduling and resource allocation algorithm, and we propose a scheme able to predict the runtime of submitted jobs for any given application on any computer by introducing a general prediction model. Experimental results are presented which indicate the robustness and reliability of the proposed architecture. The scheme has been implemented in the framework of GRIA IST project (Grid Resources for Industrial Applications).

Computational workload prediction for grid oriented industrial applications: the case of 3D-image rendering

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.

MEMPHIS: a mobile agent-based system for enabling acquisition of multilingual content and providing flexible format internet premium services

Mobile Agent technology is an interesting concept for large network-based system structures. In this paper we describe a Java-based agent environment which integrates agent execution platforms into World Wide Web servers promoting a world wide infrastructure for Mobile Agents. This system has been developed within the framework of MEMPHIS IST project, which involves the definition, development and validation of a new system for commercially significant multilingual premium services, focused on thin clients such as mobile phones, Personal Digital Assistants, etc. The concept relies on the extraction of multilingual information from various web-based content sources in different formats. The system is composed by three different Modules, namely the Acquisition, the Transformation and the Distribution Module. The content acquisition is based on dynamic user profiles. The acquired content is transformed into a meta-representation, which enables content storage independent both of source and destination format and language. The output format is based on the requirements of employed thin client terminals and the output document is distributed via the appropriate networks. The end-user receives the required information as premium services via the chosen output medium. The system supports both pull and push services. An evaluation of the system is also presented.

SHIELD: A novel NFV-based cybersecurity framework

SHIELD is an EU-funded project, targeting at the design and development of a novel cybersecurity framework, which offers security-as-a-Service in an evolved telco environment. The SHIELD framework leverages NFV (Network Functions Virtualization) and SDN (Software-Defined Networking) for virtualization and dynamic placement of virtualised security appliances in the network (virtual Network Security Functions - vNSFs), Big Data analytics for real-time incident detection and mitigation, as well as attestation techniques for securing both the infrastructure and the services. This papers discusses key use cases and requirements for the SHIELD framework and presents a high-level architectural approach.

Big IoT and Social Networking Data for Smart Cities

In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.

MI-MERCURY: A mobile agent architecture for ubiquitous retrieval and delivery of multimedia information

Mining multimedia information in the Web is in general an arduous task, due to the fact that, (a) humans perceive media content using high level concepts, (b) the subjective and vagueness of content interpretation, and (c) the fact that relevant data are often hidden in a huge amount of irrelevant information. In addition, delivering and distributing the retrieved information to a wide range of terminal devices of different properties over a wide range of networks to users of different preferences requires new tools and mechanisms for content transformation and adaptation. Other problems concern the language that the data are stored, which may not be the user’s preferred language. To address these issues we propose an integrated, reconfigurable, adaptable and open architecture for mining, indexing and retrieving multimedia information based on a mobile agent technology scheme. The proposed architecture consists of three integral subsystems: the acquisition module, responsible for searching and retrieving media data (both textual and visual), the transformation module, able to adapt and transform the mined information to other forms of representation, and the distribution module for delivering and adapting the retrieved data in terms of terminal devices, network channels and user’s preferences. The system is based on a reconfigurable architecture which is able to dynamically and automatically update the system response to user’s actual needs and preferences, by extending descriptor classes that are considered more relevant by the users. New innovative algorithms are presented in this paper both at each system module as well as in the system integration. The system supports efficient content adaptation mechanisms, textual and visual summarization (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.