Large Language Models (LLMs) have transformed natural language processing, but concerns have emerged about their susceptibility to ideological manipulation, particularly in politically sensitive areas. Previous research has largely focused on LLM biases through a binary Left vs. Right framework, often using explicit ideological prompts and fine-tuning with political question-answering datasets. In this work, we move beyond this binary approach to explore the extent to which LLMs can be influenced across a nuanced spectrum of political ideologies, from Progressive-Left to Conservative-Right. We introduce a novel multi-task dataset designed to reflect diverse ideological positions through tasks such as ideological question-answering, statement ranking, manifesto cloze completion, and Congress bill comprehension. By fine-tuning three LLMs—Phi-2, Mistral, and Llama-3—on this dataset, we evaluate their capacity to adopt and express these nuanced ideologies. Our findings indicate that fine-tuning significantly enhances nuanced ideological alignment, while explicit prompts provide only minor refinements. This highlights the models' susceptibility to subtle ideological manipulation, suggesting a need for more robust safeguards to mitigate these risks.

As video content on online platforms continues to increase, understanding the complex aspects of interpersonal communication becomes crucial. Central to this exploration is the pressing issue of gender bias, which manifests in multimodal interactions through visual, vocal, or verbal cues. These interactions present challenges in extracting and interpreting the subtle cues that may point to underlying biases. To tackle these challenges, we introduce a semi-automatic extraction of features and knowledge from user-generated content on video web platforms. Using 1,091 unstructured multi-participant video conversations from Shark Tank, we examine whether the multimodal cues (e.g., emotions) of a conversational participant (e.g., entrepreneur) affect another participant (e.g., investor) differently due to gender biases. Our methodology employs advanced deep learning algorithms for cues extraction and leverages Graph Neural Networks to model the multi-participant conversations. To complement our findings, we utilize textual features extracted through our methodology and employ GPT-4 to simulate decision-making scenarios, thereby assessing its analytical capabilities and potential gender biases.

The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating micro-benchmarks and an end-to-end application within this framework, we create an extensive performance and power consumption dataset, which we use to train and analyze ML models for predicting energy usage from features of the application and the CPS system. The proposed methodology and framework provide valuable insights into the energy dynamics of industrial CPS, offering practical implications for researchers and practitioners aiming to enhance the efficiency and sustainability of IIoT-driven automation.

Analyzing and modeling the performance and energy consumption of hybrid Edge Computing systems with embedded devices and Artificial Intelligence (AI) accelerators is crucial, yet challenging due to the lack of systematic methods and tools for measuring and estimating energy consumption. We address this gap by introducing a systematic methodology and a toolset to benchmark AI accelerators and their host devices with inference workloads representing mock Convolutional Neural Network (CNN) models with varying input sizes, network sizes, layer types, and kernel sizes. The primary contributions of this work include the development of the benchmarking methodology, the creation and analysis of a comprehensive dataset comprising power benchmark results, and the development of a predictive model for estimating the energy consumption of ML workloads on the Coral TPU (Tensor Processing Unit) accelerator connected to the edge device. The dataset, generated from extensive testing on the deployed topology, is released and can be used for further studies that seek to enhance the energy efficiency and performance optimization for Edge Computing applications.

Energy profiling and optimization are expected to be crucial factors impacting the realisation of the Internet of Things (IoT) as more intelligence is deployed at the network extremes to achieve better response times in the proximity of where data are harvested. To improve the performance of streaming analytics jobs, several schedulers have been designed to tackle key challenges in edge computing realms, including resource heterogeneity and highly volatile network links. However, energy-aware scheduling for streaming analytic jobs is at best, not adequately examined. In this article, we introduce PowerStorm, a scheduler for streaming analytic jobs that is designed to explore trade-offs between performance and energy consumption in geodistributed edge computing settings. We implement our scheduler for Apache Storm and show the scheduler’s energy saving capabilities over the Yahoo streaming benchmark with worker nodes featuring heterogeneous power and resource capabilities on both a physical and emulated testbed.

Summary The effort to reduce the environmental impact and carbon footprint of data‐center operations has led to the emergence of “green” data centers, which are designed to reduce energy consumption and to increase their use of Renewable Energy Sources (RES). Despite the advances demonstrated by hyper‐scale facilities in energy efficiency and the use of green energy, small and medium‐scale data centers, which contribute to over 50% of the total electricity consumption and carbon emissions of the sector, face significant challenges in the adoption and exploitation of RES. In this article, we present the steps taken to transform a medium‐scale, academic data center into a “green” one that uses solar power. In particular, we describe the design and implementation of: (i) a collocated photovoltaic facility and (ii) a cyber‐physical system comprising IoT sensor devices, a microservices platform, and a visualization and analytics dashboard that supports the configuration and monitoring of the infrastructure. Using data collected from the platform and dashboard, we show the environmental and financial advantages derived from this transformation, and the potential that arises from the availability of integrated operational data.

Micro-Datacenters (DCs) are emerging as key en-ablers for Edge computing and 5G mobile networks by pro-viding processing power closer to IoT devices to extract timely analytic insights. However, the performance evaluation of data stream processing on micro-DCs is a daunting task due to difficulties raised by the time-consuming setup, configuration and heterogeneity of the underlying environment. To address these challenges, we introduce BenchPilot, a modular and highly customizable benchmarking framework for edge micro-DCs. BenchPilot provides a high-level declarative model for describing experiment testbeds and scenarios that automates the bench-marking process on Streaming Distributed Processing Engines (SDPEs). The latter enables users to focus on performance analysis instead of dealing with the complex and time-consuming setup. BenchPilot instantiates the underlying cluster, performs repeatable experimentation, and provides a unified monitoring stack in heterogeneous Micro-DCs. To highlight the usability of BenchPilot, we conduct experiments on two popular streaming engines, namely Apache Storm and Flink. Our experiments compare the engines based on performance, CPU utilization, energy consumption, temperature, and network I/O.

With the proliferation of raw Internet of Things (IoTs) data, Fog Computing is emerging as a computing paradigm for delay-sensitive streaming analytics with operators deploying big data distributed engines on Fog resources [1]. Nevertheless, the current (Cloud-based) distributed analytics solutions are unaware of the unique characteristics of Fog realms. For instance, task placement algorithms consider homogeneous underlying resources without considering the Fog nodes' heterogeneity and the non-uniform network connections, resulting in sub-optimal processing performance. Moreover, data quality can play an important role, where corrupted data, and network uncertainty may lead to less useful results. In turn, energy consumption can critically impact the overall cost and liveness of the underlying processing infrastructure. Specifically, scheduling tasks on nodes with energy-hungry profiles or battery-powered devices may temporarily be beneficial for the performance, but it may increase the overall cost, or/and the battery-powered devices may not be available when needed. A Fog-enabled analytics stack must allow users to optimize Fog-specific indicators or trade-offs among them. For instance, users may sacrifice a portion of the execution performance to minimize energy consumption or vice versa. Except for the performance issues raised by Fog, the state-of-the-art distributed processing engines offer only low-level procedural programming interfaces with operators facing a steep learning curve to master them. So, query abstractions are crucial for minimizing the deployment time, errors, and debugging.

Polarization is an alarming trend in modern societies with serious implications on social cohesion and the democratic process. Typically, polarization manifests itself in the public discourse in politics, governance and ideology. In recent years, however, polarization arises increasingly in a wider range of issues, from identity and culture to healthcare and the environment. As the public and private discourse moves online, polarization feeds in and is fed by phenomena like fake news and hate speech. The identification and analysis of online polarization is challenging because of the massive scale, diversity, and unstructured nature of online content, and the rapid and unpredictable evolution of polarizing issues. Therefore, we need effective ways to identify, quantify, and represent polarization and polarizing topics algorithmically and at scale. In this work, we introduce POLAR - an unsupervised, large-scale framework for modeling and identifying polarizing topics in any domain, without prior domain-specific knowledge. POLAR comprises a processing pipeline that analyzes a corpus of an arbitrary number of news articles to construct a hierarchical knowledge graph that models polarization and identify polarizing topics discussed in the corpus. Our evaluation shows that POLAR is able to identify and rank polarizing topics accurately and efficiently.

Internet-enabled physical devices with “smart” processing capabilities are becoming the tools for understanding the complexity of the global inter-connected world we inhabit. The Internet of Things (IoT) churns tremendous amounts of data flooding from devices scattered across multiple locations to the processing engines of almost all industry sectors. However, as the number of “things” surpasses the population of the technology-enabled world, real-time processing and energy-efficiency are great challenges of the big data era transitioning to IoT. In this article, we introduce a lightweight adaptive monitoring framework suitable for smart IoT devices with limited processing capabilities. Our framework, inexpensively and in place dynamically adjusts the monitoring intensity and the amount of data disseminated through the network based on a low-cost adaptive and probabilistic learning model capable of capturing at runtime the current evolution and variability of the data stream. By accomplishing this, energy consumption and data volume are reduced, allowing IoT devices to preserve battery and ease processing on cloud computing and streaming services. Experiments on real-world data from cloud services, internet security services, wearables and intelligent transportation services, show that our framework achieves a balance between efficiency and accuracy. Specifically, our framework reduces data volume by 74 percent, energy consumption by at least 71 percent, while maintaining accuracy always above 89 percent.

Fog Computing is emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, experimenting and evaluating IoT services is a daunting task involving the manual configuration and deployment of a mixture of geodistributed physical and virtual infrastructure with different resource and network requirements. This results in sub-optimal, costly and error-prone deployments due to numerous unexpected overheads not initially envisioned in the design phase and underwhelming testing conditions not resembling the end environment. In this paper, we introduce Fogify, an emulator easing the modeling, deployment and large-scale experimentation of fog and edge testbeds. Fogify provides a toolset to: (i) model complex fog topologies comprised of heterogeneous resources, network capabilities and QoS criteria; (ii) deploy the modelled configuration and services using popular containerized descriptions to a cloud or local environment; (iii) experiment, measure and evaluate the deployment by injecting faults and adapting the configuration at runtime to test different “what-if” scenarios that reveal the limitations of a service before introduced to the public. In the evaluation, proof-of-concept IoT services with real-world workloads are introduced to show the wide applicability and benefits of rapid prototyping via Fogify.

For more than the better parts of the last decades, we are witnessing the proliferation of IoT devices, as well as an exponential growth in the volume of data generated outside of datacenters. With the generated data at the extremes of the network and the restricted device-to-cloud bandwidth, data mitigation is becoming the major barrier of cloud-based IoT services [1]. To alleviate these challenges, Fog Computing extends the Cloud's capabilities closer to IoT devices.

In recent years, the increasing propagation of hate speech in online social networks and the need for effective counter-measures have drawn significant investment from social network companies and researchers. This has resulted in the development of many web platforms and mobile applications for reporting and monitoring online hate speech incidents. In this article, we present MANDOLA, a big-data processing system that monitors, detects, visualizes, and reports the spread and penetration of online hate-related speech using big-data approaches. MANDOLA consists of six individual components that intercommunicate to consume, process, store, and visualize statistical information regarding hate speech spread online. We also present a novel ensemble-based classification algorithm for hate speech detection that can significantly improve the performance of MANDOLA’s ability to detect hate speech. To present the functionality and usability of our system, we present a use case scenario of real-life event annotation and data correlation. As shown from the performance of the individual modules, as well as the usability and functionality of the whole system, MANDOLA is a powerful system for reporting and monitoring online hate speech.

With consumers embracing the prevalence of ubiquitously connected smart devices, Edge Computing is emerging as a principal computing paradigm for latency-sensitive and in-proximity services. However, as the plethora of data generated across connected devices continues to vastly increase, the need to query the "edge" and derive in-time analytic insights is more evident than ever. This paper introduces our vision for a rich and declarative query model abstraction particularly tailored for the unique characteristics of Edge Computing and presents a prototype framework that realizes our vision. Towards this, the declarative query model enables users to express high-level and descriptive analytic insights, while our framework compiles, optimizes and executes the query plan decoupled from the programming model of the underlying data processing engine. Afterwards, we showcase a number of potential use-cases which stand to benefit from the realization of query-driven descriptive analytics for edge computing. We conclude by elaborating on the open challenges that still must be addressed to realize our vision and potential research opportunities for the academic community to further advance the current State-of-the-Art.

Over the past few years, we have been witnessing the rise of misinformation on the Internet. People fall victims of fake news continuously, and contribute to their propagation knowingly or inadvertently. Many recent efforts seek to reduce the damage caused by fake news by identifying them automatically with artificial intelligence techniques, using signals from domain flag-lists, online social networks, etc. In this work, we present Check-It, a system that combines a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, while respecting user privacy. In this paper, we present the design, implementation and performance evaluation of Check-It. Experimental results show that it outperforms state-of-the-art methods on commonly-used datasets.

With the widespread adoption of temporal graphs to study fast evolving interactions in dynamic networks, attention is needed to provide graph metrics in time and at scale. In this paper, we introduce ATMoN, an open-source library developed to computationally offload graph processing engines and ease the communication overhead in dynamic networks over an unprecedented wealth of data. This is achieved, by efficiently adapting, in place and inexpensively, the temporal granularity at which graph metrics are computed based on runtime knowledge captured by a low-cost probabilistic learning model capable of approximating both the metric stream evolution and the volatility of the graph topology. After a thorough evaluation with real-world data from mobile, face-to-face and vehicular networks, results show that ATMoN is able to reduce the compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.

Publish/subscribe (pub/sub) mechanisms constitute an attractive communication paradigm in the design of large-scale notification systems for Online Social Networks (OSNs). To accommodate the large-scale workloads of notifications produced by OSNs, pub/sub mechanisms require thousands of servers distributed on different data centers all over the world, incurring large overheads. To eliminate the pub/sub resources used, we propose SELECT - a distributed pub/sub social notification system over peer-to-peer (P2P) networks. SELECT organizes the peers on a ring topology and provides an adaptive P2P connection establishment algorithm where each peer identifies the number of connections required, based on the social structure and user availability. This allows to propagate messages to the social friends of the users using a reduced number of hops. The presented algorithm is an efficient heuristic to an NP-hard problem which maps workload graphs to structured P2P overlays inducing overall, close to theoretical, minimal number of messages. Experiments show that SELECT reduces the number of relay nodes up to 89% versus the state-of-the-art pub/sub notification systems. Additionally, we demonstrate the advantage of SELECT against socially-aware P2P overlay networks and show that the communication between two socially connected peers is reduced on average by at least 64% hops, while achieving 100% communication availability even under high churn.

Automatic resource provisioning is a challenging and complex task. It requires for applications, services and underlying platforms to be continuously monitored at multiple levels and time intervals. The complex nature of this task lays in the ability of the monitoring system to automatically detect runtime configurations in a cloud service due to elasticity action enforcement. Moreover, with the adoption of open cloud standards and library stacks, cloud consumers are now able to migrate their applications or even distribute them across multiple cloud domains. However, current cloud monitoring tools are either bounded to specific cloud platforms or limit their portability to provide elasticity support. In this article, we describe the challenges when monitoring elastically adaptive multi-cloud services. We then introduce a novel automated, modular, multi-layer and portable cloud monitoring framework. Experiments on multiple clouds and real-life applications show that our framework is capable of automatically adapting when elasticity actions are enforced to either the cloud service or to the monitoring topology. Furthermore, it is recoverable from faults introduced in the monitoring configuration with proven scalability and low runtime footprint. Most importantly, our framework is able to reduce network traffic by 41 percent and consequently the monitoring cost, which is both billable and noticeable in large-scale multi-cloud services.

As more knowledge is vastly added to the devices fuelling the Internet of Things (IoT) energy efficiency and real-time data processing are great challenges that must be tackled. In this paper, we introduce ADMin, a low-cost IoT framework that reduces on device energy consumption and the volume of data disseminated across the network. This is achieved by efficiently adapting the rate at which IoT devices disseminate monitoring streams based on run-time knowledge of the stream evolution, variability and seasonal behavior. Rather than transmitting the entire stream, ADMin favors sending updates for its estimation model from which values can be inferred, triggering dissemination only when shifts in the stream evolution are detected. Results on real-life testbeds, show that ADMin is able to reduce energy consumption by at least 83%, data volume by 71%, shift detection delays by 61% while maintaining accuracy above 91% in comparison to other IoT frameworks.

In 2010 the popular paper by Kwak et al. [11] presented the first comprehensive study of Twitter as it appeared in 2009, using most of the Twitter network at the time. Since then, Twitter's popularity and usage has exploded, experiencing a 10-fold increase. As of 2015, it has more than 500 million users, out of which 316 million are active, i.e. logging into the service at least once a month.1In this study we revisit the network observed by Kwak et al. to examine the changes exhibited in both the graph and the behavior of the users in it. Our results conclude to a denser network, showing an increase in the number of reciprocal edges, despite the fact that around 12.5% of the 2009 users have now left Twitter. However, the network's largest strongly connected component seems to be significantly decreasing, suggesting a movement of edges towards popular users. Furthermore, we observe numerous changes in the lists of influential Twitter users, having several accounts that where not popular in the past securing a position in the top-20 list as new entries.

Ubiquitous Internet connectivity enables users to update their Online Social Network profile from any location and at any point in time. These, often geo-tagged, data can be used to provide valuable information to closely located users, both in real time and in aggregated form. However, despite the fact that users publish geo-tagged information, only a small number implicitly reports their base location in their Online Social Network profile. In this paper we present a simple yet effective methodology for identifying a user's key locations, namely her home and work places. We evaluate our methodology with Twitter datasets collected from the country of Netherlands, city of London and Los Angeles county. Furthermore, we combine Twitter and LinkedIn information to construct a work location dataset and evaluate our methodology. Results show that our proposed methodology not only outperforms state-of-the-art methods by at least 30% in terms of accuracy, but also cuts the detection radius at least at half the distance from other methods.

Real-time data processing while the velocity and volume of data generated keep increasing, as well as, energy-efficiency are great challenges of big data streaming which have transitioned to the Internet of Things (IoT) realm. In this paper, we introduce AdaM, a lightweight adaptive monitoring framework for smart battery-powered IoT devices with limited processing capabilities. AdaM, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the metric stream. Results on real-world testbeds, show that AdaM achieves a balance between efficiency and accuracy. Specifically, AdaM is capable of reducing data volume by 74%, energy consumption by at least 71%, while preserving a greater than 89% accuracy.

With the rapidly growing demand for DNA analysis, the need for storing and processing large-scale genome data has presented significant challenges. This paper describes how the Genome Analysis Toolkit (GATK) can be deployed to an elastic cloud, and defines policy to drive elastic scaling of the application. We extensively analyse the GATK to expose opportunities for resource elasticity, demonstrate that it can be practically deployed at scale in a cloud environment, and demonstrate that applying elastic scaling improves the performance to cost tradeoff achieved in a simulated environment.

To optimize the cost and performance of complex cloud services under dynamic requirements, workflows and diverse cloud offerings, we rely on different elasticity control processes. An elasticity control process, when being enforced, produces effects in different parts of the cloud service. These effects normally evolve in time and depend on workload characteristics, and on the actions within the elasticity control process enforced. Therefore, understanding the effects on the behavior of the cloud service is of utter importance for runtime decision-making process, when controlling cloud service elasticity. In this paper, we present a novel methodology and a framework for estimating and evaluating cloud service elasticity behaviors. To estimate the elasticity behavior, we collect information concerning service structure, deployment, service runtime, control processes, and cloud infrastructure. Based on this information, we utilize clustering techniques to identify cloud service elasticity behavior, in time, and for different parts of the service. Knowledge about such behavior is utilized within a cloud service elasticity controller to substantially improve the selection and execution of elasticity control processes. These elasticity behavior estimations are successfully being used by our elasticity controller, in order to improve runtime decision quality. We evaluate our framework with three real-world cloud services in different application domains. Experiments show that we are able to estimate the behavior in 89.5% of the cases. Moreover, we have observed improvements in our elasticity controller, which takes better control decisions, and does not exhibit control oscillations.

Over the past decade, Cloud Computing has rapidly become a widely accepted paradigm with core concepts such as elasticity, scalability and on demand automatic resource provisioning emerging as next generation Cloud service-must have-properties. Automatic resource provisioning for Cloud applications is not a trivial task, requiring for both the applications and platform, to be constantly monitored, capturing information at various levels and time granularity. In this paper we describe the challenges that occur when monitoring elastically adaptive Cloud applications and to address these issues we present JCatascopia, a fully automated, multi-layer, interoperable Cloud Monitoring System. Experiments on different production Cloud platforms show that JCatascopia is a Monitoring System capable of supporting a fully automated Cloud resource provisioning system with proven interoperability, scalability and low runtime footprint. Most importantly, JCatascopia is able to adapt in a fully automatic manner when elasticity actions are enforced to an application deployment.

This paper introduces a novel service oriented pilot platform developed to support Tele-Supervised rehabilitation programs for patients after hospitalization in Intensive Care Units. The platform is developed under the framework of the TeleRehabilitation project funded by the Cross Border Cooperation Programme Greece Cyprus 2007-2013 in order to successfully meet the main technological and clinical objectives of the project. The design and development of the platform is based on composite service architecture (aggregates smaller and fine-grained services such as Web Based applications, Clinical Information Systems and Video Communication Systems). The platform delivers sustainable, maintainable and high quality services and enables multiparty, interregional bidirectional audio/visual communication between clinical practitioners and post-ICU patients, enables patient group-based vital sign real time monitoring, individualized and group-based patient online training and patients clinical record bookkeeping.

With the rapid emergence of the internet world, a lot of information networks become available every day. In many cases, these information networks contain objects connected by multiple links and described by different attributes. In this paper the problem of clustering homogeneous information networks in groups with similar attributes and connections is studied. Clustering such networks is a challenging task due to different importance of links and attributes. In addition, it is not straightforward how to balance the links and attributes information. In this article we describe these challenges and propose a fuzzy clustering model as well as a fuzzy clustering algorithm, HASCOP. Extensive experimentation on real world datasets has shown that HASCOP can be successfully applied in such networks, demonstrating its efficacy and superiority against the state-of-the-art attributed graph clustering methods.

This paper introduces a novel, open architecture cloud oriented framework named Intensive Care Cloud, ICCloud. ICCloud main objective is to provide a) a common repository to store anonymized vital sign parameters retrieved from intensive care bedside medical devices and b) exploit massive computational resources to analyze vital sign parameters. ICCloud uses a simplified data model (cloud tables) to reduce the complexity of the deployment and the cost of usage of the infrastructure. These cloud tables are accessed only within the cloud infrastructure (cloud jobs) using only inbound network traffic. Pre-allocated working nodes (virtual machines used only for processing) are used to execute jobs implementing scoring functions or data analysis algorithms.

A key challenge for Grid and Cloud infrastructures is to make their services easily accessible and attractive to end-users. In this paper we introduce tagging capabilities to the Miner soft system, a powerful tool for software search and discovery in order to help end-users locate application software suitable to their needs. Miner soft is now able to predict and automatically assign tags to software resources it indexes. In order to achieve this, we model the problem of tag prediction as a multi-label classification problem. Using data extracted from production-quality Grid and Cloud computing infrastructures, we evaluate an important number of multi-label classifiers and discuss which one and with what settings is the most appropriate for use in the particular problem.

During the last decade, the scientific community has witnessed an unprecedented deployment of large-scale, federated e-Infrastructures such as Grid Computing, primarily for supporting data-intensive scientific exploration and coordinated problem solving. However, practical experience and user studies have indicated that the adoption of such e-Infrastructures is lagging behind original expectations, a fact which is mainly attributed to the limited support that available tools provide for user collaboration and information sharing. The goal of this paper is twofold, first to lay down the foundations for building a collaboration environment in the form of abstractions and second to show the effectiveness of these abstractions through g-Social, an Eclipse-based, open-source environment as an extension to g-Eclipse, that provides a powerful, user-friendly, platform-independent toolset for users, application developers and administrators of Grid infrastructures. g-Social enables user collaboration and resource sharing through Online Social Networking services, capitalizing on the success that these services have.

Consider a centralized query operator that identifies to every smartphone user its k geographically nearest neighbors at all times, a query we coin Continuous All k-Nearest Neighbor (CAkNN). Such an operator could be utilized to enhance public emergency services, allowing users to send SOS beacons out to the closest rescuers, allowing gamers and social networking users to establish ad-hoc overlay communication infrastructures, in order to carry out complex interactions. In this paper, we study the problem of efficiently processing a CAkNN query in a cellular or WiFi network, both of which are ubiquitous. We introduce an algorithm, coined Proximity, which answers CAkNN queries in O(n(k + λ)) time, where n denotes the number of users and λ a network-specific parameter (λ <;<; n). Proximity does not require any additional infrastructure or specialized hardware and its efficiency is mainly attributed to a smart search space sharing technique we introduce. Its implementation is based on a novel data structure, coined k+-heap, which achieves constant O(1) look-up time and logarithmic O(log(k*λ)) insertion/update time. Proximity, being parameter-free, performs efficiently in the face of high mobility and skewed distribution of users (e.g., the service works equally well in downtown, suburban, or rural areas). We have evaluated Proximity using mobility traces from two sources and concluded that our approach performs at least one order of magnitude faster than adapted existing work.

We present a query formulation language (called MashQL) in order to easily query and fuse structured data on the web. The main novelty of MashQL is that it allows people with limited IT skills to explore and query one (or multiple) data sources without prior knowledge about the schema, structure, vocabulary, or any technical details of these sources. More importantly, to be robust and cover most cases in practice, we do not assume that a data source should have - an offline or inline - schema. This poses several language-design and performance complexities that we fundamentally tackle. To illustrate the query formulation power of MashQL, and without loss of generality, we chose the Data web scenario. We also chose querying RDF, as it is the most primitive data model; hence, MashQL can be similarly used for querying relational databases and XML. We present two implementations of MashQL, an online mashup editor, and a Firefox add on. The former illustrates how MashQL can be used to query and mash up the Data web as simple as filtering and piping web feeds; and the Firefox add on illustrates using the browser as a web composer rather than only a navigator. To end, we evaluate MashQL on querying two data sets, DBLP and DBPedia, and show that our indexing techniques allow instant user interaction.

One of the main goals of Cloud and Grid infrastructures is to make their services easily accessible and attractive to end-users. In this article we investigate the problem of supporting keyword-based searching for the discovery of software files that are installed on the nodes of large-scale, federated Grid and Cloud computing infrastructures. We address a number of challenges that arise from the unstructured nature of software and the unavailability of software-related metadata on large-scale networked environments. We present Minersoft, a harvester that visits Grid/Cloud infrastructures, crawls their file systems, identifies and classifies software files, and discovers implicit associations between them. The results of Minersoft harvesting are encoded in a weighted, typed graph, called the Software Graph. A number of information retrieval (IR) algorithms are used to enrich this graph with structural and content associations, to annotate software files with keywords and build inverted indexes to support keyword-based searching for software. Using a real testbed, we present an evaluation study of our approach, using data extracted from production-quality Grid and Cloud computing infrastructures. Experimental results show that Minersoft is a powerful tool for software search and discovery.

In this work we describe VIVAGr, a graphical-oriented real time visualization tool for vehicular ad-hoc network connectivity graphs. This tool enables the effective synthesis of structural, topological, and dynamic characteristics of VANET graphs, with a variety of parameters that affect the shape and characteristics of a vehicular ad hoc network (wireless range, mobility models, road-network topology, market penetration ratio, and exhibited interference). Our design allows researchers to explore and understand problems and issues related with vehicular ad-hoc networks that face today significant design challenges. The tool is able to present all active connection of the network in real-time mode using mobility traces. A visual encoding syntax is used to represent semantic meanings and highlight the effect of mobility and topology on vehicular network specific properties.

This paper introduces a novel, open-source middleware framework for communication with medical devices and an application using the middleware named intensive care window (ICW). The middleware enables communication with intensive care unit bedside-installed medical devices over standard and proprietary communication protocol stacks. The ICW application facilitates the acquisition of vital signs and physiological parameters exported from patient-attached medical devices and sensors. Moreover, ICW provides runtime and post-analysis procedures for data annotation, data visualization, data query, and analysis. The ICW application can be deployed as a stand-alone solution or in conjunction with existing clinical information systems providing a holistic solution to inpatient medical condition monitoring, early diagnosis, and prognosis.

A server farm is examined, where a number of servers are used to offer a service to impatient customers. Every completed request generates a certain amount of profit, running servers consume electricity for power and cooling, while waiting customers might leave the system before receiving service if they experience excessive delays. A dynamic allocation policy aiming at satisfying the conflicting goals of maximizing the quality of users' experience while minimizing the cost for the provider is introduced and evaluated. The results of several experiments are described, showing that the proposed scheme performs well under different traffic conditions.

VANETs have been envisioned as an infrastructure for deploying Vehicular Information Systems (VIS) that among others provide drivers with an up-to-date view on the prevailing traffic conditions. In this work we evaluate the benefits of caching vehicular information obtained from such VIS through VITP, a location-aware, application-layer communication protocol that we extend to support caching. We present an evaluation study of our approach conducting extensive simulation on large scale vehicular networks under different realistic urban traffic conditions Our results identify the critical parameters that affect information quality in VANETs as well as demonstrate the viability and effectiveness of the cache-enabled VITP.

Modern e-Health systems require advanced computing and storage capabilities, leading to the adoption of technologies like the grid and giving birth to novel health grid systems. In particular, intensive care medicine uses this paradigm when facing a high flow of data coming from intensive care unit's (ICU) inpatients just like demonstrated by the ICGrid system prototyped by the University of Cyprus. Unfortunately, moving an ICU patient's data from the traditionally isolated hospital's computing facilities to data grids via public networks (i.e., the Internet) makes it imperative to establish an integral and standardized security solution to avoid common attacks on the data and metadata being managed. Particular emphasis must be put on the patient's personal data, the protection of which is required by legislations in many countries of the European Union and the world in general. In this paper, we extend our previous research with the following contributions: 1) a mandatory access control model to protect patient's metadata; 2) a major security revision to our previously proposed privacy protocol by contributing with a "quality of security" quantitative metric to improve fragmented data's assurance; and finally, 3) a set of early results to demonstrate that our protocol not only improves a patient personal data's security and privacy but also achieves a performance comparable with existing approaches.

Grid infrastructures are in operation around the world, federating an impressive collection of computational resources and a wide variety of application software. In this context, it is important to establish advanced software discovery services that could help end-users locate software components suitable to their needs. In this paper, we present the design, architecture and implementation of an open-source keyword-based paradigm for the search of software resources in Grid infrastructures, called Minersoft. A key goal of Minersoft is to annotate automatically all the software resources with keyword-rich metadata. Using advanced Information Retrieval techniques, we locate software resources with respect to users queries. Experiments were conducted in EGEE, one of the largest Grid production services currently in operation. Results showed that Minersoft successfully crawled 12.3 million valid files (620 GB size) and sustained, in most sites, high crawling rates.

Vehicular ad hoc networks have emerged recently as a platform to support intelligent inter-vehicle communication and improve traffic safety and performance. The road-constrained and high mobility of the vehicles, their unbounded power source, and the emergence of roadside wireless infrastructures make VANETs a challenging research topic. A key to the development of protocols for intervehicle communication and services lies in the knowledge of the topological characteristics of the VANET communication graph. This article provides answers to the general question: how does a VANET communication graph look like over time and space? This study is the first one that examines a very large-scale VANET graph and conducts a thorough investigation of its topological characteristics using several metrics, not examined in previous studies. Our work characterizes a VANET graph at the connectivity (link) level, quantifies the notion of ¿qualitative¿ nodes as required by routing and dissemination protocols, and examines the existence and evolution of communities (dense clusters of vehicles) in the VANET. Several latent facts about the VANET graph are revealed and incentives for their exploitation in protocol design are examined.

In this paper, we present TrafficModeler, an open-source, graphical tool for the rapid high-level modeling and generation of vehicular traffic. TrafficModeler supports a variety of traffic definition models representing a wide range of traffic patterns. A set of traffic generation algorithms are implemented to convert high-level models to output compatible with SUMO, a popular open-source microscopic traffic simulator. TrafficModeler drastically reduces the time and effort required to generate traffic for SUMO. Furthermore, it can be easily extended to support other traffic simulators and to incorporate new types of traffic.

In this paper, we investigate the problem of supporting keyword-based searching for the discovery of software resources that are installed on the nodes of large-scale, federated Grid computing infrastructures. We address a number of challenges that arise from the unstructured nature of software and the unavailability of software-related metadata on Grid sites. We present Minersoft, a Grid harvester that visits Grid sites, crawls their file-systems, identifies and classifies software resources, and discovers implicit associations between them. The results of Minersoft harvesting are encoded in a weighted, typed graph, named the Software Graph. A number of IR algorithms are used to enrich this graph with structural and content associations, to annotate software resources with keywords, and build inverted indexes to support keyword-based searching for software. Using a real testbed, we present an evaluation study of our approach, using data extracted from a production-quality Grid infrastructure. Experimental results show that our approach achieves high search efficiency.

Self-managing solutions have recently attracted a lot of interest from the database community. The need for self-* properties is more evident in distributed applications comprising heterogeneous and autonomous databases and functionality providers. Such resources are typically exposed as Web Services (WSs), which encapsulate remote DBMSs and functions called from within database queries. In this setting, database queries are over WSs, and the data transfer cost becomes the main bottleneck. To reduce this cost, data is shipped to and from WSs in chunks; however the optimum chunk size is volatile, depending on both the resources' runtime properties and the query. In this paper we propose a robust control theoretical solution to the problem of optimizing the data transfer in queries over WSs, by continuously tuning at runtime the block size and thus tracking the optimum point. Also, we develop online system identification mechanisms that are capable of estimating the optimum block size analytically. Both contributions are evaluated via both empirical experimentation in a real environment and simulations, and have been proved to be more effective and efficient than static solutions.

In this paper we present FailRank, a novel framework for integrating and ranking information sources that characterize failures in a grid system. After the failing sites have been ranked, these can be eliminated from the job scheduling resource pool yielding in that way a more predictable, dependable and adaptive infrastructure. We also present the tools we developed towards evaluating the FailRank framework. In particular, we present the FailBase Repository which is a 38GB corpus of state information that characterizes the EGEE Grid for one month in 2007. Such a corpus paves the way for the community to systematically uncover new, previously unknown patterns and rules between the multitudes of parameters that can contribute to failures in a Grid environment. Additionally, we present an experimental evaluation study of the FailRank system over 30 days which shows that our framework identifies failures in 93% of the cases. We believe that our work constitutes another important step towards realizing adaptive Grid computing systems.

Nowadays, Web Services (WS) play an important role in the dissemination and distributed processing of large amounts of data that become available on the Web. In many cases, it is essential to retrieve and process such data in blocks, in order to benefit from pipelined parallelism and reduced communication costs. This article deals with the problem of minimizing at runtime, in a self-managing way, the total response time of a call to a database exposed to a volatile environment, like the Grid, as a WS. Typically, in this scenario, response time exhibits a concave, nonlinear behavior depending on the client-controlled size of the individual requests comprising a fixed size task. In addition, no accurate profiling or internal state information is available, and the optimum point is volatile. This situation is encountered in several systems, such as WS Management Systems (WSMS) for DBMS-like data management over wide area service-based networks, and the widely spread OGSA-DAI WS for accessing and integrating traditional DBMS. The main challenges in this problem apart from the unavailability of a model, include the presence of noise, which incurs local minima, the volatility of the environment, which results in moving optimum operating point, and the requirements for fast convergence to the optimal size of the request from the side of the client rather than of the server, and for low overshooting. Two solutions are presented in this work, which fall into the broader areas of runtime optimization and switching extremum control. They incorporate heuristics to avoid local optimal points, and address all the aforementioned challenges. The effectiveness of the solutions is verified via both empirical evaluation in real cases and simulations, which show that significant performance benefits can be provided rendering obsolete the need for detailed profiling of the WS.

Intensive Care Units are widely considered as the most technologically advanced environments within a hospital. In such environments, physicians are confronted with multiple medical devices that monitor the inpatients. The capability to collect, store, process, and share inpatient monitoring data along with the remarks of the treating physicians can bring tremendous benefits to all aspects of Intensive Care Medicine (practice, research, education). The IC-Window makes it feasible for physicians to extract, view, store, and replay Clinically Interesting Episodes through simple, intuitive user interfaces.

Grids have emerged as wide-scale, distributed infrastructures providing enough resources for always more demanding scientific experiments. EGEE is one of the largest scientific grids in production operation today, with over 220 sites and more than 30,000 CPU all over the world. A further evolution of EGEE needs to be based on knowledge of deficiencies and bottleneck of the current infrastructure and software. To provide this knowledge we analyzed nine months of job submissions on the south-east federation of EGEE. We provide information on how users submit their jobs: throughput, bursts, requirements, VO. We study the current behavior of EGEE middleware too, by evaluating its performance and the retry policy. We finally show that even if the middleware provides advanced functionality, most submissions are still embarrassingly parallel jobs.

Abstract Grid benchmarking is an important and challenging topic of Grid computing research. In this paper, we present an overview of the key challenges that need to be addressed for the integration of benchmarking practices, techniques, and tools in emerging Grid computing infrastructures. We discuss the problems of performance representation, measurement, and interpretation in the context of Grid benchmarking, and propose the use of ontologies for organizing and describing benchmarking metrics. Finally, we present a survey of ongoing research efforts that develop benchmarks and benchmarking tools for the Grid. Copyright © 2006 John Wiley & Sons, Ltd.

Recent advances in wireless inter-vehicle communication systems enable the establishment of vehicular ad-hoc networks (VANET) and create significant opportunities for the deployment of a wide variety of applications and services to vehicles. In this work, we investigate the problem of developing services that can provide car drivers with time-sensitive information about traffic conditions and roadside facilities. We introduce the vehicular information transfer protocol (VITP), a location- aware, application-layer, communication protocol designed to support a distributed service infrastructure over vehicular ad- hoc networks. We describe the key design concepts of the VITP protocol and infrastructure. We provide an extensive simulation study of VITP performance on large-scale vehicular networks under realistic highway and city traffic conditions. Our results demonstrate the viability and effectiveness of VITP in providing location-aware services over VANETs.

In this paper, we propose a Core Grid Ontology (CGO) that defines fundamental Grid-specific concepts, and the relationships between them. One of the key goals is to make this Core Grid Ontology general enough and easily extensible to be used by different Grid architectures or Grid middleware, so that the CGO can provide a common basis for representing Grid knowledge about Grid systems, including Grid resources, Grid middleware, services, applications, and Grid users. The Core Grid Ontology is designed and developed based on a general model ofGrid infrastructures, and described in the Web Ontology Language OWL. Such an ontology can play an important role in building Gridrelated Knowledge bases and in supporting the realization of the Semantic Grid.

An important factor that needs to be taken into account by end-users and systems (schedulers, resource brokers, policy brokers) when mapping applications to the Grid is the performance capacity of hardware resources attached to the Grid and made available through its Virtual Organizations. In this article, we examine the problem of characterizing the performance capacity of Grid resources using benchmarking. We examine the conditions under which such characterization experiments can be implemented in a Grid setting and present the challenges that arise in the Grid context. We specify a small number of performance metrics and propose a suite of microbenchmarks to estimate these metrics for sites that belong to large Virtual Organizations. We describe benchmarking experiments conducted with, and published through GridBench, a tool that we built to manage benchmarking experiments over the Grid and to publish and analyze performance metrics. Finally we show how results derived from GridBench can help end-users assess the performance capacity of resources belonging to a large Virtual Organization.

In this paper, we present the design and implementation of Ovid, a browser for grid-related information. The key goal of Ovid is to support the seamless navigation of users in the grid information space. Key aspects of Ovid are: (i) a set of navigational primitives, which are designed to cope with problems such as network disorientation and information overloading; (ii) a small set of Ovid views, which present the end-user with high-level, visual abstractions of grid information; these abstractions correspond to simple models that capture essential aspects of a grid infrastructure; (iii) support for embedding and implementing hyperlinks that connect related entities represented within different information views; (iv) a plug-in mechanism, which enables the seamless integration with Ovid of third-party software that retrieves and displays data from various grid information sources; and (v) a modular software design, which allows the easy integration of different visualization algorithms that support the graphical representation of large amounts of grid-related information in the context of Ovid's views.

Intermediaries are software entities deployed on Internet hosts of the wireline and wireless Web that intervene in the flow of information from clients to origin servers at the application level of the WWW. Intermediary systems fall under the general term of "middleware". Intermediaries represent a useful abstraction for the design and study of emerging software infrastructures for "next-generation" Web services. Their importance is increasing with the increasing demand for personalization, localization, and support for ubiquitous access over different physical media and protocols. We present an overview of a wide range of systems that can be described as intermediaries, classifying them in a number of broad categories according to their basic functionalities. Going beyond simple WWW proxies, we examine the requirements arising from the need to support personalization, mobility and ubiquity under high loads. We identify and refine a set of important properties and characteristics of intermediary systems. Based on these properties, we introduce a detailed taxonomy of characteristic systems and identify a number of key components of emerging intermediary infrastructures.

We study Web multicasting, a service offered by satellite operators to Internet Service Providers around the world. This service employs satellite connections for disseminating periodically Web content to regional and "institutional" WWW caches. We propose a theoretical framework for Web multicasting and formalize the notions of utility and QoS perceived by customers of Web multicasting services. We explore two alternative charging schemes, usage- and subscription-based pricing, and propose a framework for negotiating the provision of the Web multicasting service between a satellite operator and its potential customers. We use this negotiation framework to compare theoretically the two pricing schemes at hand. We show that a given level of QoS can be guaranteed under subscription-based pricing at a cost at least as low as under usage-based pricing. Our theoretical framework can be used further by satellite operators and prospective customers to define the content provided through Web multicasting, estimate its quality, negotiate its price and assess its overall effectiveness.

In recent years, a lot of research has focused on the study of scientific applications running on high-performance systems. So far, however, little concern has been expressed over performance issues related to interactive visualization and analysis. This is due to the lack of standardization of scientific visualization software, as many tools are designed and developed on an ad-hoc basis, coping with specific applications. On the other hand, general-purpose packages for data analysis and visualization are of a proprietary nature, making instrumentation hard. Furthermore, analyzing performance behavior on networked environments in the presence of resource contention from many users is a new and complex field of experimental computer science. In this paper, we study the workload characteristics of TIPSY (Theoretical Image Processing SYstem), which is a portable, client-server, interactive package for visualization and analysis of astrophysics and astronomy simulations. The purpose of our research is to identify workload characteristics of interactive visualization tools and understand performance bottlenecks that arise when running TIPSY on networked environments.

In thki paper we describe PKDGRAV, a parallel hierarchical tree-structured code used to conduct cosmological simulations on shared-memory and message-passing multiprocessors.We explore performance traits of cosmological N-Body simulations on 32K to 1.3 million particles, running PKD-GRAV on KSR-2 and Intel Paragon multiprocessors with up to 128 nodes.We quantify the computation and communication requirements of PKDGRAV and study its scalability.We show that the shared-memory implementation performs and scales better than the message-passing.We investigate the causes of poor scalability of the Paragon implementation and identify an implement at ion-specific performance bottleneck in the software cache mechanism pertinent to the Paragon implementation. 1

This paper shows that mathematical models of biological pattern formation are ideally suited to data parallelism.We present two new algorithms, one for simulating the dynamic structure of fibroblasts, and the other for studying the self-organization of motile bacteria.We describe implementations of these algorithms using a high level data parallel language called ZPL, and we give performance results for the Kendall Square Research KSR-2 and the Intel Paragon that include comparisons against sequential Fortran.

Achieving efficient and reliable synchronization is a critical problem in building long systolic arrays. This problem is addressed in the context of synchronous systems by introducing probabilistic models for two alternative clock distribution schemes: tree and straight-line clocking. Analytic bounds are presented for the probability of failure, and an examination is made of the tradeoffs between reliability and throughput in both schemes. The basic conclusion is that as the one-dimensional systolic array gets very long, tree clocking becomes preferable to straight-line clocking.>