The ongoing digitization of Operational Technology (OT) and the integration of Industrial Internet of Things (IIoT) devices are fundamentally transforming modern industrial systems. However, the heterogeneity of hardware and software ecosystems amplifies the complexity of this transformation, making orchestration and lifecycle management especially challenging. To address these challenges, modern DevOps practices—such as Continuous Integration and Continuous Deployment (CI/CD), runtime observability, and fine-grained access control—are essential. These practices help accelerate time-to-market, maintain service quality, manage operational complexity, and uphold security standards. Advancing toward seamless integration of OT within cloud-native environments, we introduce a prototype Orchestrator based on the Kubernetes plarform, aimed at providing a comprehensive lifecycle management for OT devices in industrial contexts. Our proposal bridges the traditional divide between IT and OT by seamlessly integrating with existing CI/CD pipelines and implementing Role-Based Access Control (RBAC) tailored to device-level permissions. Through a unified abstraction layer, it simplifies the management of heterogeneous, multi-vendor hardware, enabling scalable, secure, and efficient operations across diverse industrial deployments.

The increasing reliance on Machine Learning (ML) to extract insights and value from data is driving researchers and businesses to seamlessly integrate it throughout the entire Cloud Continuum (CC), spanning from large-scale cloud infrastructures to edge computing and end devices. However, traditional service orchestration frameworks are fine-tuned to manage single-site general-purpose applications, struggling with the complexity of CC ML deployments, which seek customized strategies for enhanced performance and resiliency. To this end, we propose Machine Learning cloud Continuum Operations (MLCOps), an overlay solution that supports ML applications in CC deployments. MLCOps implements an innovative multisite orchestration layer and provides a new service runtime support, allowing one to react quickly to changing conditions. MLCOps advocates a layered approach to carry out specialized strategies, implementing global and local actions on infrastructure and environment to ensure the performance and availability of the deployed ML services. Without loss of generality, we show how the framework could be instantiated to support an anomaly detection task. In this context, we assess various (re)configuration strategies that can take place in actual deployments, assessing the ability of MLCOps to preserve the reliability of CC ML application deployments under dynamic workload conditions.

The adoption of the Cloud Continuum pattern, leveraging computational resources from both Cloud and Edge infrastructures provided by various vendors, is exacerbating the challenge of managing infrastructure and services. To forefront this complexity, Cloud providers and customers are evolving their usual approach to service development and deployment, seeking faster and easier paths that can sustain time-to-market business needs. Many Cloud providers are shaping their offerings to incorporate an ever-increasing number of Serverless Computing services, wherein they handle every aspect of customer service and infrastructure management. In this work, we aim to extend the principles of Serverless computing service development to Cloud continuum environments and put forth a proposal for enforcing smart deployment of Serverless applications over heterogeneous computing environments. Functional to this goal is the heuristic component, a founding element of our architectural proposal that is capable of building applications "footprint" in different execution environments offered by the provider infrastructure. In this preliminary work, we discuss the results obtained from testing the above-mentioned component on a sample set of functions.

The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workload scenarios in smart cities, where fast arriving geo-referenced massive amounts data streams need to be joined with archive tables, at scale. This aims at enriching streams with descriptive attributes that enable deeper insightful analytics. More applications are now relying on finding, in real-time, to which geographical region each data streaming spatially-tagged tuple belongs. This problem requires a computationally intensive stream-static join operation, where one side of join is a dynamic stream while the other is a disk-resident static table. Even with emergence of some libraries that solve this problem in static-static fashion, their adoption for live scenarios is challenging because join operations are expensive in real-time. In addition, the time-varying nature of fluctuation and skewness in the geospatial data loads arriving online calls for an approximate solution that can trade-off QoS constraints in a way which ensures that the system survives sudden spikes in data loads. In this paper, we present SpatialSSJP, an adaptive spatial-aware approximate query processing system that specifically focuses on stream-static joins in a way that guarantees achieving an agreed set of Quality-of-Service goals and maintains geo-statistics of stateful online aggregations over stream-static join results. SpatialSSJP employs a state-of-art stratified-like sampling design to select well-balanced representative geospatial data stream samples and serve them to a stream-static geospatial join operator downstream. We implemented a prototype atop Spark Structured Streaming. Our extensive evaluations on big real datasets show that our system can survive and mitigate harsh join workloads and outperform state-of-art baselines by significant magnitudes, without risking rigorous error bounds in terms of the accuracy of the output results. SpatialSSJP achieves a relative accuracy gain against plain Spark joins of approximately 10% in worst cases but reaching up to 50% in best case scenarios.

Edge cloud computing is a promising programming and deployment paradigm to empower delay-sensitive applications. By executing close to the network edge, distributed applications can have quicker reactions to event occurrence and consequently prompter dynamic adaptations. In addition, recent improvements in connectivity support allow developers to benefit from heterogeneous and alternative communication technologies (e.g., RDMA, DPDK, XDP, etc.) to meet the requirements of network-intensive edge applications. However, exploiting these technologies makes applications statically tailored to a specific network interface; this significantly limits the potential of edge cloud computing, where application components should be able to migrate seamlessly at runtime. INSANE aims at solving that issue by exposing a technology-agnostic middleware API that lets developers simply specify their QoS communication requirements; the dynamic selection of the most appropriate technology on the currently hosting edge node is delegated to INSANE. The paper also presents how it is possible to develop two different INSANE-based applications (a decentralized messaging system and an image streaming framework) with a few lines of code. Finally, an extensive performance evaluation shows that our middleware adds very limited ns-scale overhead to the raw acceleration technologies.

The continuous digitalization and application of modern ICT technologies in the Smart City and Tourism domains are paving the way to new, integrated and connected experiences with strong economic and social impact. However, the integration of services and data coming from different providers is hindered by a rapidly evolving ecosystem of tools and techniques, introducing substantial delays and costs in solution design, deployment, testing, and refinement. Serverless computing is a novel cloud computing model where the customers' business logic, structured as lightweight functions, is automatically put into execution in response to an incoming event. This emergent paradigm promises to be an appealing solution, lowering the development and management barrier for the adaptation, integration, and roll-out of services. However, the ephemeral nature of serverless functions clashes with the necessity of creating and maintaining persistent connections to the various data providers. In this work, we present the Serverless Persistence Support (SPS) service, a novel, distributed, and scalable service implementing an adaptation layer able to improve data access performance from serverless functions. To validate our proposal, we conduct a thorough analysis on a realistic testbed, contrasting various data layer supports and assessing SPS performance when compared to the classic approach where connections and operations are executed inside the business logic. The experimental analysis shows that our solution exhibits better performance both in terms of latency and throughput, while also improving resource utilization.

The recent trend of moving cloud computing capabilities to the edge of the network is reshaping the way applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of the supported application domains. Yet, mission-critical applications such as those in manufacturing, automation, or automotive, still rely on communication standards like the Time-Sensitive Networking (TSN) protocol and 5G to ensure a deterministic network behavior: in this context, virtualization might introduce unacceptable network perturbations. In this paper, we demonstrate that latency-sensitive applications can execute in virtual machines without disruptions to their network operations. We propose a novel approach to support the TSN protocol in virtual machines through a precise clock synchronization method and we implement it in integration with state-of-the-art and highly-efficient network virtualization techniques. Our experimental results show that it is possible to achieve deterministic and ultra-low latency end-to-end communication in the cloud continuum, for example providing a guaranteed sub-millisecond latency between remote virtual machines.

In smart city advanced analytical scenarios, tremendous amounts of georeferenced big time series data arrive continuously to time series databases, requiring the shared analytics on both geospatial and time dimensions. Mostly, the focus has been given to optimizing the storage and processing of each workload alone, either geospatial or time dimensions. To close this gap, in this paper, we have designed a pyramid-like indexing scheme that we term as geoTSI (short for geo time series index) which twists two dimensionality reduction geospatial encoding methods (geohash and S2) sequentially with a time series index to efficiently enable such mixed workload scenarios. This method enables geospatial and time indexes to collaborate synergistically in an aim to reduce the time required for accessing the disk and retrieving the time series data that comprises the answer for the mixed workload query. We show how our indexing scheme can be efficiently exploited to run a hybrid geospatial proximity query on time series data. Also, we evaluate our index on real-world georeferenced time series data, where we obtain, on average, a significant 34 % reduction in the query running time by applying our method against the baseline.

Serverless computing is a novel paradigm that has been widely adopted, in recent years, across many sectors due to its fine-grained scalability and fast time-to-market. This paradigm aims at offloading users from heavy burden tasks including those related to authentication and authorization. However, existing security mechanisms provided by cloud providers do not seem to be adequate to completely secure serverless platforms. In particular, typical access control solutions rely either on centralized authorization services or implement access control verification within the business logic. These approaches respectively degrade system performance and lead to security issues derived from the tight coupling among code and authorization verification. In this paper, we present a solution to address these problems with a fully decentralized architecture integrating access control verification in serverless environments. We implemented a prototype of the proposed architecture and evaluated its performance under different load conditions. Experiments show that our proposal outperforms other approaches.

Given the availability of several blockchain technologies in permissioned contexts, blockchain application designers have to cope with the increasing complexity of choosing which technology and consensus algorithm best fit a specific use case. However, the lack of a standard framework allowing to assess the scalability of permissioned blockchain platforms and to compare performances and features of consensus algorithms makes the development of a sensible evaluation a costly and difficult time-consuming problem. Throughout this article, we propose a practical scalability and applicability evaluation of the Quorum blockchain and its consensus algorithms. Although we apply our evaluation workflow to a financial use case, we define a methodology that can be generalized to any permissioned blockchain technology. We leverage Hyperledger Caliper as a benchmarking tool, and Docker as a deployment tool, making our analysis easy to be repeated, cross-platform, and cost-effective.

Industry 4.0 aims to revolutionize and digitize the manufacturing sector by enabling and facilitating interoperability, solution agility, flexible (re)configuration of production chain(s) while, at the same time, reducing costs by exploiting real time data. These capabilities require to link the plant floor with data flows from/to the enterprise borders and include as core enabling technologies the Internet of Things (IoT), cloud, and edge computing key to move and execute parts of the business logic. The new capabilities might be leveraged in an innovative way, especially in the plant floor to dynamically change the monitoring/control logic of the smart machinery. In our reference scenario, the data flows originating from the plant floor can be processed and filtered locally, creating the basis for a selective Quality of Service (QoS) mechanism allowing for the implementation of reactive services, such as predictive maintenance. To that end, we propose an innovative serverless edge processing solution used for monitoring geo-distributed industrial plants. The proposal is validated in realistic settings, under different operational regimes, exhibiting acceptable performance trends under realistic periodic and variable traffic scenarios.

The problem of information overloading is prevalent in recommendations websites and social networks. Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation websites such as Netflix, whereas user-user connections are the interaction sought in social websites such as Twitter. Social recommender systems seek to generate recommendations for users based on similar preferences of their close friends. Because social networks do not normally contain user-item interactions, social recommender systems are typically hybridized with other recommenders (e.g., website recommenders such as Netflix) that provide such interaction. However, current systems are unaware of the user’s additional contextual information when coupled with social counterparts. In this paper, we propose a context-aware deep learning-based recommender system, US-NCF, in support for social recommender systems. Our experiments show US-NCF outperforms state-of-art counterparts.

Serverless platforms are increasingly gaining importance in the cloud computing landscape due to their benefit of shortening the time to market of solutions and capability of automatic, fast, fine-grained scaling of resources. In this context, function chaining represents an appealing feature, allowing the composition of two or more functions to create a complex computation from simpler units while incentivizing modularity and reusability of functions. In this paper, we propose a portable and transparent, container-based serverless architecture that introduces an innovative infrastructural support, enabling an efficient composition of functions co-located on the same host. The proposal relies on a shared-memory approach and a message-oriented middleware serving as a communication medium among components. The experimental assessment shows the approach comes with the benefit of optimized resource usage and performance benefits measured in terms of request completion rate and a decrease in response latency.

Current cloud-enabled NoSQL database frameworks support flexible and scalable storage of huge amounts of data arriving through various and often heterogeneous channels. However, they do not natively provide optimised processing of spatial data, thus making it more difficult to perform accurate data analytics needed in many smart city application scenarios. To improve the performance of spatial data computation in the NoSQL MongoDB storage framework, this article proposes a novel data partitioning method based on dimensionality reduction. The underlying key idea is to reduce a spatial data representation from multi to single dimensionality, by still maintaining its geometrical meaning and by employing a specific geo-encoding scheme, i.e., a geohash string. In particular, the geohash string is used as a sharding key in order to store geometrically-nearby objects into the same chunks (and consequently into the same shard). In addition, as a distinctive feature, we have extended the MongoDB framework with a custom spatial QoS-aware optimizer that exploits our novel partitioning scheme to support two, typically expensive, types of spatial queries with QoS guarantees. Those queries are containment (and consequently top-N) and proximity. The paper also contributes to the existing literature with extensive experimental results about the performance of both our partitioning method and query optimizer; the reported results show that our solutions outperform baselines by orders of magnitude.

Several relevant research and innovation activities have recently investigated the technical and economic advantages of cloud computing for the provisioning of telco service infrastructures, in particular towards all-IP next generation 5G networks. In fact, the evolution of telco service infrastructures traditionally requires a significant upfront investment (and a long adoption process). Conversely, cloud exploitation significantly lowers investment risks by potentially providing elasticity in service provisioning via flexible Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) Infrastructure. In this context, the paper presents novel solutions that we have designed, implemented, and evaluated within the EU FP7 Mobile Cloud Networking project (MCN). Their aim is to achieve cost-effective elastic provisioning of telco services over heterogeneous and federated cloud providers, with the specific focus of supporting the extreme quality levels that are demanded by traditional, non-virtualized, and dedicated telco infrastructures. In particular, we concentrate on how to effectively and efficiently automate service state migration for coarse-grained telco service (cloudified) components by leveraging industry-mature orchestration technologies and cloud management frameworks. While our proposed state migration model and procedure are general, its implementation is experimented for MCN's Rating, Charging, and Billing as a Service (RCBaaS). This MCN functionality has been chosen by purpose due to its challenging reliability and uptime requirements. The reported experimental and simulation results show the technical feasibility of the proposed solution under different and realistic load conditions for next-generation and cloudified 5G services.

The easily reachable IoT edge devices have caused the accumulation of vast amounts of geo-referenced data traces that can help in performing deep insightful analytics. Geospatial data in real geometries are normally clumped into batches and has strong autocorrelation properties which can be exploited in discovering interesting insights. Current plain Cloud computing frameworks are not attuned to the shape of data. Most importantly, data splitting is an important precursor in data parallelization mechanisms. Current systems mostly focus on general data workloads, thus are giving attention mostly to load balancing while splitting the data to Cloud computing resources. However, many benefits can be reaped by being attuned to the spatial characteristics while distributing the data, thus striking a plausible balance between load balancing and spatial data locality preservation normally leads to achieving better time-based QoS goals, which then leads to an optimized provisioning of Cloud computing resources. In this paper, we have designed a spatial batch processing engine that comprises a custom spatial data locality aware partitioning method for disseminating spatial data loads in Cloud computing clusters. We have also extended a state-of-art benchmark density-based clustering method that is known as DBSCAN-MR and implemented a standard compliant prototype on top of a best-in-breed de facto Cloud-based main memory processing framework, Apache Spark. Our results show that our partitioning method with the associated spatial query optimizers can achieve gains that significantly outperform baselines.

Cloud computing has transformed the way IT services are provisioned and consumed, shifting the burden of configuration and management to the cloud provider. A lot of research has been conducted in the field aimed at optimizing and/or devise new in-cloud service delivery models. Yet, service outages are a norm rather than an exception. A natural evolution for application developers, especially for those applications that must meet high availability requirements, is to rely on resources and services provisioned by multiple cloud simultaneously. The so-called multi-cloud, distributed deployment model coupled with a monitoring solution spanning multiple domains could help in a timely identification and isolation of faulty components, alleviating service impairment issues. To this end, we propose an extension to NoMISHAP, a Platform as a Service (PaaS) multicloud middleware, introducing a cross layer monitoring solution for use in distributed cloud environments. Experimental results show the effectiveness of our approach and its ease of adoption for management tasks.

The technological progress is leading to an increase of instrument sensitivity in the field of rotational spectroscopy. A direct consequence of such a progress is an increasing amount of data produced by instruments, for which the currently available analysis software is becoming limited and inadequate. In order to improve data analysis performance, parallel computing techniques and distributed computing technologies like Cloud and High Performance Computing (HPC) can be exploited. Despite the availability of computer resources, neither Cloud nor HPC have been fully investigated for identifying unknown target spectra in rotational spectrum. This paper proposes the design and implementation of a Highly Scalable AUTOFIT (HS-AUTOFIT), an enhanced version of a fitting tool for broadband rotational spectra that is capable of exploiting the resources offered by multiple computing nodes. Compared to the old program version, the new one is capable of scaling on multiple computing nodes, thus guaranteeing higher accuracy of the fit function and consistent boost of execution time. The result of tests conducted in real Cloud and HPC environments show that HS-AUTOFIT is a viable solution for the analysis of huge amount of data in the addressed scientific field.

Elastic resource outsourcing is a growing trend that simplifies and makes more efficient the management of resources, by embracing all the features of the execution of services over public clouds, such as high availability and automated scalability management of resources. Therefore, modern enterprise services are increasingly leveraging inter/intra-cloud deployments and the choice of the right cloud provider to support the execution of them becomes a fundamental operational choice. The paper presents our Audit4Cloud platform, an open-source tool for auditing the performance of virtual resources made available by various commercial cloud providers, with specific focus on cloud networking. In particular, we claim that Audit4Cloud is an enabling key in choosing the right cloud vendor as it not only offers the visibility of current values of some significant performance indicators about the offered cloud resources, but also provides users with a complete picture of those performance indicators over time. We have already performed a large experimental campaign by considering primary commercial cloud providers; the collected results show the feasibility of the approach and that Audit4Cloud can play the role of a solid third-party auditing tool to estimate real performance and costs of cloud resources.

This work proposes a novel mechanism for management, orchestration and flow control in the context of the device-to-device (D2D) to deal with load balancing using the deep Q-learning (DQN) technique. To do so, we implemented a D2D network simulation environment, using the ParticiptAct dataset to evaluate the load of the cell towers in a region of Italy. The Gauss-Markov and Gilbert-Elliott models were used for mobility and packet loss, respectively, where it was considered that the towers had a disconnected coverage area, hence forming a Voronoi space. We used a Gaussian process to predict the load of the towers when they receive the packet, and a DQN to perform the balance of load of the network. This proposal presents better results than the baseline, concerning the metrics used, as well as presenting some perspectives for a future unfolding of this work.

Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.

Smart factory management is going through a remarkable change, in terms of quality and diversity of services provided to customers. The companies that produce manufacturing machines now can follow the products throughout the production chain, from the project to the deployment in real scenarios. Industry 4.0 is pushing this trend forward, demanding for servitization of products and machines, mainly for the manufacturing sector where human and production machine are in strict collaboration. The data produced by the machines must be processed quickly to allow the implementation of reactive services such as predictive maintenance and remote control, always taking care of the safety of nearby people. This paper proposes a multilayer architecture to tackle the main issues in monitoring legacy manufacturing machines and to provide general guidelines to solve them. We derived some guidelines from a real Industry 4.0 transition experiment performed together with the company technical departments to accomplish an efficient system for monitoring and servitization of manufacturing machines, with a scalable platform that confirms its usefulness in many production facilities with different needs.

Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and deploying their network infrastructures, by giving the possibility to service/application providers to reconfigure and programmatically perform actions on the network. Accordingly, the dynamic management of the data center networks requires complex operations to ensure high availability and continuous reliability in order to guarantee full functionality of the virtualized resources. In this context, simulator-based approaches are helpful for planning and evaluating the deployment of the cloud data center networking, but existing cloud simulators have several limitations: they have too high overhead for wide-scale data center networks, complex configuration, and too abstract deployment models. For these motivations, we propose DCNs-2, a novel extension for the Ns-2 simulator, as a valid solution to efficiently simulate a cloud network infrastructure, with all the involved entities, such as switches, physical/virtual machines, and racks. The proposed solution not only makes configuration easier, but through extensive tests, we show that its execution overhead is limited to less than 130 MB of memory and the execution time is acceptable even for very wide-scale and complex deployment environments.

Nowadays, most users carry high computing power mobile devices where speech recognition is certainly one of the main technologies available in every modern smartphone, although battery draining and application performance (resource shortage) have a big impact on the experienced quality. Shifting applications and services to the cloud may help to improve mobile user satisfaction as demonstrated by several ongoing efforts in the mobile cloud area. However, the quality of speech recognition is still not sufficient in many complex cases to replace the common hand written text, especially when prompt reaction to short-term provisioning requests is required. To address the new scenario, this paper proposes a mobile cloud infrastructure to support the extraction of semantics information from speech recognition in the Social Care domain, where carers have to speak about their patients conditions in order to have reliable notes used afterward to plan the best support. We present not only an architecture proposal, but also a real prototype that we have deployed and thoroughly assessed with different queries, accents, and in presence of load peaks, in our experimental mobile cloud Platform as a Service (PaaS) testbed based on Cloud Foundry.

The widespread adoption of information and communication technologies (ICT) is profoundly changing manufacturing. Several Internet-of-Things (IoT) and industry 4.0 solutions deployed in production environments have pushed for standardization efforts, most notably reference architecture model industrie 4.0 (RAMI 4.0), typically focusing on smart factory environments. However, the ICT evolution is also enabling novel smart appliance scenarios, where relatively cheap machines, connected and integrated, are deployed outside the typical industrial environment with a wide range of stakeholders involved. The paper reports about a real world use case composed of more than 12000 ice cream machines connected worldwide and shows how, anticipating the state of the art, the underlying design of the ICT platform presents many interesting similarities with RAMI 4.0. The integration of appliances in a smart value chain enables to develop novel services for different stakeholders, ranging from ice cream manufacturer and maintenance technicians to ice cream shop owners and final consumers. The important synergies with RAMI 4.0 and the extensive on-the-field validation make the proposed solution a compelling reference application, from which to draw useful and generally applicable guidelines for the development of future Industry 4.0 smart appliance platforms.

Cloud computing nowadays is the cornerstone for all the business applications, mainly because of its high fault tolerance characteristic. High resilience and availability typical of cloud-native applications are achieved using different technologies. Regarding the file system, the main fault tolerant application examples are distributed file systems, such as HDFS, Ceph, GlusterFS, and XtremeFS. These file systems have different architectures and deployment models than the Traditional Distributed File Systems (TDFSs), such as NFS. The primary goal of this work is to analyze and compare different Cloud Distributed File Systems (CDFSs) in terms of characteristics, architecture, reliability, and components. As a key feature, the paper benchmarks them considering as use case an IaaS platform.

A key aspect that Telco operators must carefully consider when deploying Network Function Virtualization (NFV) solutions is the level of performance that cloud computing software platforms can guarantee in support of the offered network services. OpenStack is widely considered as one of the most relevant open-source frameworks that could accelerate the NFV adoption, also because the evolution of its networking components sees a progressive integration of SDN-based solutions that could significantly improve the performance of cloud-based connectivity services. In this paper, we discuss some of the most recent OpenStack innovations that enable a native SDN-like approach to firewalling functions in the data plane, as well as a native SDN-oriented control of the virtual network infrastructure. Then we present a detailed performance analysis of the aforementioned innovations at both the data and control/management plane, showing the potentials of native SDN adoption within OpenStack toward an integrated solution for production-level NFV deployments.

The ever-progressing advancements in urban growth and technological development in recent decades have caused a noticeable increase of the phenomenon of socialenvironmental deterioration, leading to a decline in quality of life, reduction of social welfare and difficult urban mobility for people living in cities. The concept of Smart City can be used to mitigate several of the challenges arising from the aforementioned issues, relying on multiple tools and techniques (such as crowdsensing) to gather essential context data about how actual citizens consume resources and commute throughout their everyday lives. In this paper, we show how an urban mobility data analytics tool may help to determine the most visited regions and interconnections in an urban area. This information has been obtained using data gathered from a pool of users participating in a crowdsensing campaign, using the ParticipAct Brazil platform. The obtained results confirm the reliability of the information produced, highlighting the regions with the highest concentration of people during the geolocation monitoring process and their connections; therefore, this data may be used to plan possible future changes to how the city allocates its resources, to better suit the mobility needs of its citizens.

The advancement of networking and sensor-enabled devices have motivated the emergence of unprecedented initiatives, including Industry 4.0 and smart cities. Those are entwined in a way that makes their operation duly interconnected. Industry 4.0 will sooner become the biggest consumer of smart city big data. That data is geo-referenced, and its storage and processing need spatial-awareness, which is currently absent within the constellation of biggest big data management players of the market. We aim to fill this gap by providing spatial-aware big data management strategies in support for Industry 4.0 main principles. Our experimental results show that our strategies outperform those of state-of-the-art by orders of magnitude.

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.

Some recent research projects, inspired by the widespread availability of sensor-provided smartphones, have built harvesting experiments to collect large quantities of data in urban areas. These efforts produced new real-world datasets, typically focusing on different technological aspects (GPS and Bluetooth mobility traces or WiFi indicators) and, more recently, also on user-related data, from low-level accelerometer samples to higher-level social networking data. At the same time, Mobile Crowd Sensing (MCS) blossomed with a few very recent project, with the goal to efficiently coordinate user participation, both to collect sensor data and to allow active collaboration in participatory tasks. This paper aims to shed some light and to propose new research directions on the MCS by employing the notable results already obtained in the Mobile Social Network area to the study of human dynamics. The reported results, comparing three MCS datasets available in the literature, lead to an in-depth discussion of some lessons we learned about sociotechnical management aspects of MCS. The results we present are valuable for the MCS community to design new MCS campaigns and to refine the whole MCS process to the purpose of better efficiency and scalability.

The exponential amount of geospatial data that has been accumulated in an accelerated pace has inevitably motivated the scientific community to examine novel parallel technologies for tuning the performance of spatial queries. Managing spatial data for an optimized query performance is particularly a challenging task. This is due to the growing complexity of geometric computations involved in querying spatial data, where traditional systems failed to beneficially expand. However, the use of large-scale and parallel-based computing infrastructures based on cost-effective commodity clusters and cloud computing environments introduces new management challenges to avoid bottlenecks such as overloading scarce computing resources, which may be caused by an unbalanced loading of parallel tasks. In this paper, we aim to fill those gaps by introducing a generic framework for optimizing the performance of big spatial data queries on top of Apache Spark. Our framework also supports advanced management functions including a unique self-adaptable load-balancing service to self-tune framework execution. Our experimental evaluation shows that our framework is scalable and efficient for querying massive amounts of real spatial datasets.

Mobile Crowd Sensing (MCS) is a technique that aims to obtain the participation of volunteers willing to use their smartphones to harvest large quantities of data as they move in urban areas. Those volunteers typically move inside a limited area and can encounter other volunteers during their day activity. From the number and duration of their encounters, it is possible to categorize relations between volunteers. From this knowledge, we classified volunteers in communities that will cooperate to complete a data collection. The main idea is that users inside a cooperation group are more willing to participate in a sensing campaign. The paper presents results of an implementation of our solution in a real testbed, an ongoing experiment that involves more than 170 students from Bologna University campus. In particular, this article focuses on community identification and cooperative task execution. Shown results confirm the feasibility of the proposed approach and report how user activity can be increased leveraging cooperation among them.

New sensors embedded into modern smartphones has led into a new data collection prospective in which people directly collect all the sensitive data. This feature has found different applications, in particular in the Smart Cities area, in order to establish dynamic communications between the citizens and the city government. This category of application is nestled into the Mobile Crowd Sensing (MCS) application group, due to their final purpose of sharing sensing data to an open platform that includes a huge number of people. This paper presents an extension of the general-purpose ParticipAct platform, a MCS application developed by the University of Bologna, focused on the needs of people with impaired mobility. The goal is specializing ParticipAct to enable a crowdsourcing platform that guarantees a solid support for their lifetime allowing reviewing and sharing opinions regarding public and private places and architectonic barriers of a city area. Showed results confirm the effectiveness of the developed application in terms of both its viability via integration with existing and widely diffused Geographical Information Systems (GIS), and its feasibility in terms of system and user-perceived performances.

Technical and economic opportunities of cloud computing become the focus for Internet applications and at the same time also for telco support infrastructures and network services. In fact, many telco providers are consolidating their service infrastructures towards converged and all-IP next generation networks providing typical telco services within LTE (and soon 5G) and also fixed network environments, e.g., often still adopting IP Multimedia Subsystem (IMS) architecture solutions. This telco service infrastructure evolution requires a significant upfront investment in the necessary hardware and software, thereby slowing down the adoption process significantly more than any other Internet application. Cloud computing applied to telco infrastructures can allow pay-per-use business models and significantly lower investment risks by providing telco infrastructure functionality as Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) platform. For this purpose we propose a quality audit and resource brokering framework that is fully NFV-compliant. Its current and reported implementation specifically targets IMS services because of the still relevant role played by IMS in converged provisioning and the wide availability of IMS deployment testbeds to validate the proposal. In particular, the proposed solution can monitor the quality offered by VNFs and scale in/out depending on dynamic requirements; it is fully based on industrial standards and open-source reference implementations, thus enabling rapid adoption in real industrial environments.

Efficient protocols for data packet delivery in Vehicular Ad-hoc NETworks (VANETs) are crucial to guarantee the correct forwarding of data. However, communication in VANETs is a challenging task not only for the high mobility of the vehicles, but as recent studies have shown, there is a negative impact on protocol performances caused by an obstacle such as another vehicle in the line-of-sight (LOS). Many different routing protocols were presented in the last years, but only few of them were considering the effect of non line-of-sight (NLOS) propagation. In this work, we present a new solution to improve the data packet delivery ratio considering the problems caused by a high mobility and NLOS condition. Our proposal can be integrated in every position-based routing protocol. In fact, our method creates a support context awareness of neighbors before letting the protocol choose the next hop, without interfering with the specific logic. Simulation results of our context-aware version have been compared with the original protocols and with a modified version in accordance with the principle that every violation of LOS involves a discard of the packet: our solution always perform better in terms of packet delivery ratio and delay.

Recent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces challenging issues due to the need for fast and continuous processing of these Big Data Streams in the backend of next generation crowdsensing platforms. This paper presents our practical experiences and lessons learnt in deploying the ParticipAct platform and living lab, an ongoing experiment at University of Bologna that involves 300 students for one year. Among all management issues addressed in ParticipAct, this article shows the integration of MongoDB in the ParticipAct backend, as a powerful NoSQL storage and processing engine to fasten the identification of POIs; the reported performance results confirm the feasibility of the approach by quantifying its advantages for city managers.

Cloud businesses need comprehensive visibility on hardware and software components, their utilization and their configuration. In addition, they need to integrate such information with their asset management systems and publicly available information such as hardware specifications. In this paper, we present an approach for cloud management and monitoring based on a semantic layer that unifies different interfaces and representations, and makes all relevant information accessible from a single point. We show a proof-of-concept based on OpenStack and Linked Data technologies and evaluate it in terms of overhead, query execution times, and effectiveness in the data gathering phase. Our findings indicate that a semantics-based approach is indeed feasible and advantageous for providing uniform access across different cloud environments and levels.

Mobile Crowd Sensing (MCS) aims to coordinate and activate the participation of volunteers willing to use their smartphones to harvest large quantities of data as they move in urban areas. One of the most important requirements in MCS is maximizing the effectiveness of the data gathering campaign. In fact, also due to the initial low penetration rate of MCS apps and to avoid making the MCS process cumbersome to users, only a small portion of the whole citizenship can be involved in such campaign, while most citizens are not part of the process. This paper proposes a novel approach that combines participatory and opportunistic techniques to amplify the amount of data harvested from the crowd. The core idea is that people with similar interests, such as employees of the same company, students or friends tend to meet more frequently with respect to people with different interests. Accordingly, it is possible to opportunistically involve in the crowd sensing loop people in the volunteers' neighborhood. Following that main design guideline, our work assesses the SOcial amplification FActor (SOFA) that allows to increase the number of samples retrievable during a crowd sensing campaign. We show the benefits of SOFA by using the ParticipAct MCS platform and we analyze three different application scenarios. Highly realistic simulation results, based on ParticipAct mobility traces, show the advantages of SOFA, with an amplification factor increasing up to 4.45.

Novel sensor-equipped smartphones have enabled the possibility of harvesting large quantities of data in urban areas by opportunistically involving citizens and their portable devices, as mobile sensors widely available and distributed over Smart Cities areas, typically defined as Mobile Crowd Sensing (MCS). Although some existing efforts have already tackled some of the several MCS issues, to the best of our knowledge, active experiments addressing the challenging issue of the assignment of MCS data collection campaigns to users, namely, MCS scheduling, in a large-scale crowdsensing real-world experiment are still missing. This paper presents the ParticipAct platform and living lab, an ongoing crowdsensing experiment at University of Bologna that involves 300 students for one year. In particular, this article focus on ParticipAct intelligent MCS scheduling of future crowdsensing campaigns based on user mobility history and powered by NoSQL technologies for fast processing of the large amount of mobility traces in the ParticipAct backend. Showed results confirm the feasibility of the proposed approach and quantify its cost.

In the last few years, we witnessed a growing interest in interoperability and portability topics within the Cloud Computing area. In fact, most heterogeneous PaaS solutions have been developed with no standard APIs, and with different models and levels of service; in this scenario, vendor lock-in becomes an issue. Cloud4SOA project aims to design and develop a Reference Architecture that could solve interoperability and portability problems within the Cloud domain. This paper describes our work on Cloud4SOA Semantic and SOA layer presenting a common knowledge base framework and a standardized set of harmonized APIs to overcome diversities among PaaS solution and report interesting experimental results collected for two real-world PaaS deployments.

A growing number of applications require continuous pro-cessing of high-throughput data streams, e.g., financial anal-ysis, network traffic monitoring, or Big Data analytics for smart cities. Stream processing applications typically re-quire specific quality-of-service levels to achieve their goals; yet, due to the high time-variability of stream characteris-tics, it is often inefficient to statically allocate the resources needed to guarantee application Service Level Agreements (SLAs). In this paper, we present LAAR, a novel method for adaptive replication that trades fault tolerance for in-creased capacity during load spikes. We have implemented and validated LAAR as a middleware layer on top of IBM In-foSphere Streamsr. We have performed a wide set of exper-iments on an industrial-quality 60-core cluster deployment and we show that, under the assumption of only statistical knowledge of streams load distribution, LAAR can reduce resource consumption while guaranteeing an upper-bound on information loss in case of failures.

The ever increasing diffusion of smartphones today equipped with several physical and virtual sensors allow to directly collect information about surrounding physical and logical context that range from monitoring current social pulse of individuals and entire communities to detecting user current physical activity. Enabling those advanced sensing capabilities requires complex signal processing, machine learning, and resource management algorithms that are often beyond the skills of many mobile app developers. This paper describes the relevance of these facilities for mobile crowdsensing applications in Smart City scenarios and presents our solution for activity detection, comparing it with the reference implementations provided by Google as part of the Google Play Services library.

Platform as a Service (PaaS) systems fully exploit the potential of elastic Cloud computing Infrastructure as a Service (IaaS) layer, by providing computational platforms for the developers characterized by a set of frameworks and runtimes. In these scenarios, the developers could focusing only on the implementation side of web applications without having to deal with configuration of the environment the web apps require for running properly. Services represent a central point of PaaS systems, providing external features for web applications such as SQL databases, messaging systems, and any kind of external software required by the developer. The monitoring of the availability and the performances of these Services plays an essential role in PaaS environments. The paper tackles above issues focusing on the real use case of the Cloud Foundry PaaS; collected results assess the effectiveness of the proposed monitoring function and confirm its feasibility and low overhead.

Open Data initiatives push public administrations to publish an increasing number of raw datasets, achieving a more transparent and open governance. The availability of new data raises opportunities for the development of new services, but also open new challenging issues such as the increasing volume of datasets publication available and their integration in large-scale public data ecosystems. The last decade has witnessed the spreading and the consolidation of the Semantic Web technologies that have been proposed as an opportunity to ease data integration through new semantic representation and interrogation languages. By using these technologies, we propose an analysis of the different phases that bring from raw data to mashups over these data through a deployment of standard Linked Data inside a triplestore and by exploiting a Geografic Information System tool for creating mashups with cartographic data, focusing on the real case of Bologna Open Data. We also report experimental results that point out the performances of different triplestores with the goal of supporting informed choices in this emerging new area.

Context awareness, intended as providing the current execution environment at the service level, is a fundamental capability in future mobile systems. Unfortunately, the real-world realization of such scenarios is currently undermined by inefficient context data delivery mechanisms, which introduce excessive overhead over bandwidth-constrained wireless fixed infrastructures. To efficiently offload context access from fixed infrastructures to mobile nodes, this paper presents a new data caching algorithm that exploits peculiar aspects of context distribution, mainly limited data lifetime and interests similarity between nodes in physical proximity, to properly select the data to evict when necessary. Our solution considers a history over past data accesses and information over data replication to better exploit the limited available space. Extensive simulation results, collected in NS2 simulator, support our assumptions and demonstrate that our caching solution improves system scalability while adding a limited management overhead.

Data distributions systems with guaranteed Quality of Service (QoS) levels, such as the data-centric Data Distribution Service (DDS) standard specification, have gained more and more success in the last decade. These systems represent suitable solutions for effective and high-performance data communication for challenging application scenarios with real-time requirements, such as air traffic management, industrial automation, smart grids, and, more recently, financial applications. Notwithstanding the last decade has witnessed the diffusion and consolidation of some major implementations, only a very few, in some sense obsolete, performance analysis studies are available in the literature. To fill that gap and to facilitate future IT decision processes, we propose a thorough analysis of the DDS implementations proposed by the two main stakeholders in the DDS market, namely, PrismTech and Real-Time Innovations (RTI). The reported experimental results point out the pros and cons of both solutions in terms of data delivery performance, also by precisely evaluating bottlenecks and overhead, for instance in terms of CPU and memory resource usage.

Despite its great promises, current Cloud offering is still typically rather static and does not support very dynamic execution patterns. In fact, while dynamic resource allocation is typically required to ensure efficient and effective usage of the Cloud resources, Cloud providers have to deal with complex services, usually treated as black-boxes; hence, the estimation of the maximum number of resources that could improve service execution is a big challenge. This paper proposes and explores a novel automatic service rescaling approach to solve the deployment scaling problem. The proposed scheme, called Dynamic Cloud Infrastructure (DCI), has been designed and verified as a new architecture for the IBM Cloud infrastructure. DCI provides hints useful to understand if a particular service could take full advantage from additional resources enabling automatic discovery of the deployment configuration that jointly addresses high service scalability and low resource consumption. We detail the lessons learnt from the application of the proposed solution in the IBM Smart Bay project and present experimental results that demonstrate our approach as a viable first step toward run-time service scaling.

ABSTRACT In the last years, context awareness, namely the provisioning of the current execution context to the application level, has received an increasing attention up to becoming a core capability in next generation mobile scenarios. Context awareness intrinsically forces a continuous delivery of context data to resource‐constrained mobile devices, such as mobile phones and personal digital assistants, to allow application adaptation, and that can become too severe a constraint even for modern platforms (Android, iOS, etc.). This paper focuses on the realization of a context data distribution support for Android‐based mobile phones with guaranteed quality levels on the context data delivery time. Android, notwithstanding its great potential, puts harsh constraints on the implementation of specific context distribution primitives, thus preventing the realization of a wide set of significant deployment scenarios. At this stage, we face that a large campaign of tests are necessary. We have collected main experimental results in a real testbed to highlight noteworthy details on the runtime performance obtainable with a real Android deployment. Copyright © 2012 John Wiley & Sons, Ltd.

IP multimedia subsystem (IMS) and IMS-based services are increasingly providing interoperable session control and mobility for next-generation all-IP networks. However, clear design guidelines and techniques for the support of scalable IMS-based deployment, especially for data-intensive services such as mobility management, presence, and instant messaging, are still missing. That could block or at least relevantly slow down IMS acceptance by network operators and application providers. To address these challenges, this paper thoroughly analyzes IMS scalability with special attention to intradomain deployment issues. Then, it proposes a novel solution with three core original contributions toward intradomain scalability: 1) data-centric dissemination of session state with limited overhead; 2) service-aware routing for fast intradomain load balancing; 3) service-aware load monitoring and component de-/activation for long-term intradomain load partitioning at both service and infrastructure levels. The reported experimental results point out that our solution can significantly increase intradomain scalability with very limited costs.

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.

Context awareness, namely the provisioning of the current execution context at the application level, forces the continuous delivery of context data to resource-constrained mobile devices, and that can become too severe a constraint even for modern support (Android, iOS, etc.). This article focuses on the realization of a context data distribution infrastructure for Android-based mobile phones, and highlights important details on the implementation of specific context distribution primitives. Finally, we present new experimental results to assess the runtime performances obtainable with a real Android deployment.

Software as a Service (SaaS) applications fully-exploiting the potential of elastic Cloud computing infrastructures naturally are enabling new ubiquitous access scenarios for nomadic users, such as market salesmen and home healthcare medical assistants. SaaS applications typically require to transfer data and resources to the Cloud infrastructure site; that raises several challenging issues spanning from access control to resources to privacy protection, ownership, and security of the data of the final SaaS users. However, although encryption of personal and enterprise data is strongly recommended by existing Cloud infrastructures, such as Amazon Web Services (AWS), typically they do not provide yet adequate encryption and key management support. This paper presents a real use case of Vitaever, a home healthcare SaaS application deployed on Amazon AWS, and discusses the challenges and changes needed to add cryptography and key management capabilities to the standard AWS Web/database offer so to enable SaaS data protection. We also show experimental results that benchmark the new security functions over Amazon, demonstrating their applicability to SaaS production deployments.

Cloud computing has become an essential technology not only for web provisioning, but also in mobile scenarios. Mobile devices are usually resource constrained due to processing and power limitations, so typical applications are not easy portable. Battery draining and application performance (resource shortage) have a big impact on the experienced quality, so shifting applications and services to the Cloud may improve mobile user's satisfaction. However, available Cloud solutions are mostly focused on scenarios with slowly changing provisioning, which are unable to support and promptly react to short-term provisioning requests. To address the new scenario, this paper proposes a novel Cloud monitoring and management architecture based on the data-centric publish-subscribe Data Distribution Service (DDS) standard. We present not only an architecture proposal, but also a real prototype that we have deployed in our experimental testbed. The experimental results show that our architecture is able to support the scheduling of highly dynamic tasks in the Cloud while maintaining low overheads.

Context-aware scenarios require the distribution of large amounts of context data over fixed wireless infrastructures. The opportunistic usage of mobile nodes as data carriers has received a great deal of interests in recent years in order to increase system scalability. In this paper, we focus on the overhead introduced by context distribution on mobile devices, and we highlight the need of resource-aware solutions to trade off run-time overhead with available resources. Next, we propose a novel algorithm to dynamically adapt data distribution task processing load at mobile devices. Our experimental results, obtained using a real testbed, demonstrate that our approach ensures correct resource management under a wide range of different working conditions.

Disaster area scenarios are the consequence of sudden and unexpected disasters due to either human or natural causes, such as terrorist attacks and earthquakes. Towards the main goal of saving as many human lives as possible, context-aware services are emerging as standard-de-facto solutions to improve the coordination of involved rescue teams. Unfortunately, the scarce resources (communication bandwidth, memory, etc.) lead to very tough deployment scenarios to deal with. This paper presents our real-world quality-based context data distribution infrastructure for context-aware services in disaster areas. The main contribution is a quality-based caching solution that self-adapts by using quality requirements associated to close neighbors. Collected experimental results confirm that our proposal improves both system scalability and data availability.

There is a clear and widely recognized trend toward a growing and unprecedentedly large amount of user-generated content, which users are willing to share in an easy, cheap, and immediate way. This poses novel hard technical challenges for Peer-to-Peer (P2P) content distribution. We claim that a crucial technical factor to spread even more P2P distribution of multimedia content, is the availability of effective solutions to make rich metadata promptly accessible to users. However, state-of-the-art research and industrial practices are still too weakly addressing the problem and, to the best of our knowledge, none of the existing solutions offers an adequate support for metadata distribution in P2P networks. This paper presents the design and implementation of a prototype (called Metis and available for download) for metadata dissemination in P2P overlay networks. Metis proposes several original contributions: it is fully decentralized; it exploits a set of dynamically selectable/configurable epidemic dissemination protocols; it can be easily integrated on top of existing P2P overlays, such as Tribler. The reported experimental results show the feasibility of our approach, which achieves good dissemination coverage and promptness with very limited overhead.

Advances in wireless communications have motivated the development of Wireless Sensor Networks (WSNs) for low-cost and easy-deployable physical and environmental monitoring. At the same time, the evolution of portable mobile devices is enabling novel fully integrated internetwork computing scenarios by making viable to opportunistically exploit Mobile Ad-hoc NETwork (MANET) devices traversing the WSN and equipped with a WSN radio to relay part of the WSN traffic over the MANET. The paper proposes an original solution to enable reliable WSN communications between mobile MANET devices and fixed WSN sensors; the primary design guideline is to exploit a local beaconing mechanism together with low-level WSN link monitoring techniques and data retransmissions so to grant reliable communications notwithstanding MANET node mobility. The reported experimental results, collected over a real testbed, confirm the effectiveness of the proposed solution, even under challenging channel contention and device mobility conditions.

In the recent years, Cloud computing is emerging as the next big revolution of both computer networks and web provisioning. Due to its enormous promises, several vendors, such as Amazon and IBM, started designing, developing, and deploying Cloud solutions to optimize the usage of their own data centers. Unfortunately, several management issues of the Cloud are still open and deserve additional research. Among them, and fuelled by the emerging Green Computing research, Cloud architectures have to consolidate virtual machines in the minimal number of physical servers to reduce the run-time power consumption. In this paper, we present a project on power saving through server consolidation conducted at the IBM Innovation Centre in Dublin. Our experimental results, collected on a real testbed, show that server consolidation can effectively save energy, while introducing minimum performance degradation.

Ubiquitous smart environments equipped with low-cost and easily-deployable wireless sensor networks (WSNs) and ever-increasing widespread Mobile Ad hoc NETworks (MANETs) are opening brand new opportunities in environmental monitoring. This paper proposes an original solution for WSN/MANET integration based on the primary design guideline of opportunistically exploiting MANET overlays impromptu formed over the WSN to improve and boost the data collection task of a typical WSN. On the one hand, we adopt a cross-layer approach that exploits MANET connections to differentiate and fasten the delivery of sensed urgent data by pushing them over low-latency MANET paths. On the other hand, we take advantage of local cross-layer visibility of the WSN data collection procedures and protocols to carefully control and limit WSN–MANET coordination overhead. We claim that our proposed solution can obtain significant quality of service improvements via differentiation, by granting faster delivery times to urgent data with a very limited cost in most common execution scenarios.

The widespread availability of mobile devices with multiple wireless interfaces, such as UMTS/GPRS, IEEE 802.11a/b/g and Bluetooth, is pushing for the support of multi-homing and multi-channel connectivity, also enabled by multi-hop cooperative paths to the Internet. The goal is to transparently allow the synergic exploitation of "best" connectivity opportunities available at runtime, by enabling cooperative connectivity, extended wireless coverage, and effective load balancing (for both energy and bandwidth consumption). To this purpose, we claim the need for innovative, lightweight, and proactive evaluation metrics for connectivity management by exploiting application-level awareness of expected node mobility, path throughput, and energy availability. To demonstrate the effectiveness of these solution guidelines for Multi-hop Multi-path Heterogeneous Connectivity (MMHC), we have designed, implemented, and thoroughly evaluated our evaluation metrics on top of the MMHC middleware, which are original because they i) enable the management of multiple multi-hop paths, also made up by heterogeneous wireless links, ii) support connectivity management decisions depending on dynamically gathered context indicators, and iii) can proactively trigger management operations with limited overhead. The extensive set of reported results, from both simulations and real testbed, provides a useful guide for the full understanding of how, to what extent, and which context-based evaluation metrics can enable effective MMHC management in differentiated application/deployment scenarios.

The possibility of distributing data to all interested entities connected to a mobile system is a fundamental core function in future mobile ubiquitous class based applications. Unfortunately, its realization poses many problems especially due to the typically scarce resources. With the goal of enhancing scalability, this paper presents a novel data distribution infrastructure for wireless mobile environments. After the introduction of quality-based indicators useful to manage data distribution, we present an adaptive data batching technique that exploits load information to reduce wireless channel congestion. Our results indicate that our data distribution infrastructure scales well also under high loads.

Spontaneous (or opportunistic) networks are multi-hop ad-hoc networks where nodes opportunistically exploit peer-to-peer contacts to share content and available resources in an impromptu way. Even if spontaneous networking has recently received growing interest, there is still the lack of impactful and wide-scale applications fully exploiting its potential. We claim that this is due to the intrinsic complexity of spontaneous network management, unsuitable to be directly handled by application developers. Therefore, this paper proposes a novel easy-to-use middleware, called RAMP, for the autonomic, cross-, and application-layer management of spontaneous networks. RAMP enables the dynamic sharing of all resources available via multiple, heterogeneous, intermittent, infrastructure-based, and ad-hoc links, which are orchestrated in a lightweight way to compose the multi-hop paths needed by sharing applications at runtime. The RAMP prototype is a useful tool for the community of researchers in the field and can be rapidly deployed over real execution environments. The reported experimental results demonstrate the feasibility of our approach and the limited RAMP overhead over common deployment scenarios.

Recent advances in wireless communications have motivated the development of Wireless Sensor Networks (WSNs) for low-cost and easy-deployable physical and environmental monitoring. WSNs were typically accessible only through special WSN nodes acting as WSN data sinks and gateways towards standard IP-based networks. The recent diffusion of low-power IP network protocol implementations has (potentially) made all WSN nodes directly reachable over IP. However, the wide variety of communication platforms makes it difficult to glue together different access types. We propose an original solution for WSN gateway-/IP-based access integration based on the primary design guideline of exploiting a proxy component to facilitate WSN access management. Our proposal is fully compliant with latest communication standards and adopts a novel (translucent) approach to enable either fully-transparent or fully-aware WSN access control. Our experimental results show good data delivery performances with different WSN access types and evaluate the cost of our management support.

Recent trends in data-centric systems have motivated significant standardization efforts, such as the Data Distribution Service (DDS) to enable data dissemination with guaranteed Quality of Service (QoS). However, clear design guidelines and techniques for the support of reliable and scalable DDS-based deployments, especially for (mobile) data intensive services, such as Internet-wide information dissemination for breaking news or financial analysis services, are still missing. After an analysis of main DDS fault-tolerance and scalability deployment issues, this paper proposes a novel solution with two core original contributions: i) DDS-compliant routing substrate to facilitate reliable data dissemination between mobile devices; ii) relay-based DDS support infrastructure with limited overhead to enable scalable Internet-wide data dissemination. Our solution, especially tailored for mobile computing scenarios, is lightweight and requires neither persistency nor heavy operations at mobile device side. Reported experimental results confirm that our proposal can guarantee desired scalability requirements with a limited network, CPU, and memory resource overhead.

Disaster areas typical to deal with unexpected and sudden disasters, such as earthquakes and terrorist attacks, can take advantage of context-awareness, namely the capability of providing applications with full awareness of current execution context, to increase the possibility of saving human lives. Unfortunately, context-aware services in disaster areas require efficient and reliable context data distribution. Towards this direction, we present our quality-based context data distribution infrastructure, and we introduce two optimizations useful to improve both distribution efficiency and reliability. Experimental results, obtained by means of simulations, support our solutions.

Recent trends in data-centric systems have motivated significant standardization efforts, such as the Data Distribution Service (DDS) to support data dissemination with guaranteed Quality of Service (QoS) in heterogeneous Internet environments. Notwithstanding the central relevance of DDS in that scenario, DDS-based pub/sub solutions still exhibit limited support for reliability, by omitting advanced techniques to reduce/eliminate QoS-degradations and data losses due to possible network and DDS system faults. We propose an original solution for fault- tolerance and prompt recovery of DDS-based pub/sub systems based on a DDS-compliant P2P routing substrate that continuously achieves a guaranteed data delivery with expected QoS-levels. In contrast with similar solutions in the field, our proposal neither requires support for data persistency nor implies heavy client-side operations. We exploit a DDS-compliant data dispatching infrastructure to reliably disseminate events and to balance data distribution load. The reported experimental results point out that our solution can guarantee desired requirements together with a limited overhead: the paper reports also performance indicators for our proposal CPU and memory resource usage.

The provisioning of multimedia streaming towards wireless mobile devices, even while they change their point/technology to access the Internet, is of growing relevance in the converged mobile world. Several technical challenges have to be faced for seamlessly supporting horizontal/vertical handoffs without interrupting on-going service sessions and without endangering service continuity. The paper specifically focuses on innovative techniques to rapidly take proper content adaptation decisions in case of vertical handoffs. To avoid service interruptions, we originally adopt a combination of context-aware proactive management based on vertical handoff predictions and of innovative multiple-criteria decisions that effectively exploit user/device profiling and multimedia adaptor descriptions. These original techniques are implemented in a support facility integrated in our mobile multimedia middleware and available for download. The reported experimental results, collected in a real testbed at our university, demonstrate the suitability of the proposed approach.

Recent advances in portable client devices are enabling new scenarios where mobile users assume to continuously access to various services, such as email, printing, and social computing applications, by opportunistically exploiting any computing resource and any wireless connectivity possibility encountered during their roam. That requirement calls for suitable context-aware middlewares capable of retrieving and using context data to personalize service provisioning. However, existing solutions still exhibit very limited support for context data management and distribution, especially in wide-scale and highly heterogeneous systems. The paper proposes a novel context-aware middleware to achieve scalability in context data dissemination. The primary design guideline is to reduce context data traffic by using a hierarchical distributed architecture and pervasive physical/logical caching techniques. To validate our design choices, we exploited our context-aware middleware to realize an advertisements service and a service discovery facility in our university campus. Obtained performance results demonstrate that our solution positively affects system scalability and average context dissemination time.

The emerging, ubiquitous Internet of Services scenario discloses a radical change in the process of content creation and service consumption. Thus, platforms for service and content provisioning should cope with several issues concerning mobility, context awareness and content adaptation. However, traditional programming paradigms, though proving their effectiveness in mastering a good separation of concerns, fall short when it comes to capture concerns that span and orthogonally crosscut several system components. In service-oriented architecture models that face the extremely dynamical and ever-changing above scenario, it is common and usual an undesirable tangling of functionalities which induces poor flexibility, accuracy and consistency. This paper investigates how an innovative and emerging methodology, Aspect-Oriented Programming, can address these issues in a middleware platform for ubiquitous dynamic context-driven service provisioning and configuration. We also distill some design principles and implementation details of the reengineering activities we accomplished.

The increasing request for mobile multimedia services have motivated relevant standardization efforts, such as the IP multimedia system (IMS) to support session control, mobility, and interoperability in all-IP next generation wireless networks. Notwithstanding their increasing diffusion, IMS solutions still exhibit limited support for service scalability, especially for data intensive services such as mobility management, presence, and instant messaging, by omitting clear design guidelines and techniques to (re-)distribute incoming load dynamically. The contribution of this paper is twofold. First, it thoroughly analyzes the state-of-the-art literature in the field to clarify all main IMS scalability issues. Second, it proposes a novel and widely applicable architecture of solution based on three original guidelines: data-centric session management; differentiated management of intra-/inter-domain communications; service-aware load-balancing at both infrastructure and service levels. Preliminary performance results collected in the IMS-enabled wireless infrastructure at our campus demonstrate the effectiveness of the proposal.

Recent advances in wireless client devices and multimedia communications have motivated relevant standardization efforts, such as the IP Multimedia Subsystem (IMS) to support session control, mobility, and interoperability in all-IP next generation networks. Notwithstanding the central relevance of IMS for novel mobile multimedia services, IMS-based solutions still exhibit limited support for service continuity during handoffs, by omitting advanced techniques to reduce/eliminate handoff delays and quality degradations, especially during vertical handoffs. We propose an original solution for service continuity and dynamic multimedia content tailoring based on the primary design guideline of exploiting terminal-based decentralized handoff predictions to proactively activate application-level management operations (flow quality downscaling). The proposal is fully compliant with standard IMS and exploits infrastructure media gateways to execute content tailoring actions, without requiring heavy client-side adaptation operations. The reported experimental results point out that our solution can avoid streaming playout interruptions and significantly increase user-perceived service quality, without negative effects on handoff delay.

In the modern Internet of services scenarios, users need to create, share and access contents and services in extremely personalized ways and by means of heterogeneous devices and interaction channels. The most promising architectural solutions to cope with such dynamic and ever-growing scenario adopt a service oriented approach and leverage service composition platforms to flexibly arrange new value-added services made up of basic off-the-shelf components. Though, current solutions for service composition target rather static scenarios and scarcely support today's dynamic Web, where new services and contents continually become available to satisfy novel user needs and service compositions may become obsolete and need reconfiguration. We propose a novel composition model that aims at being extremely flexible and extensible, yet remaining easily usable by hiding complexity to users.

The diffusion of wireless terminals with multiple communication interfaces, e.g., IEEE 802.11, Bluetooth, and UMTS on the same device, is pushing towards the necessity of middleware solutions to dynamically and seamlessly select the proper connectivity technology to exploit at any time. That selection should consider several elements, at very different abstraction layers, from application bandwidth to energy consumption requirements, from connectivity costs to user preferences, i.e., it should be context-dependent. The paper presents our context-aware MAC middleware for multi-interface wireless terminals that enables the dynamic determination and selection of the most suitable interface and connectivity provider among the available ones. MAC novelty is primarily in two crucial challenging elements. On the one hand, it considers not only infrastructure-based connectivity providers, e.g., UMTS base stations, but also peer nodes, e.g., neighbor nodes accessible via Bluetooth and connected via Wi-Fi to the Internet infrastructure. On the other hand, MAC can evaluate both infrastructure and peer connectivity providers not only based on usual parameters such as available bandwidth and energy consumption, but also taking into account innovative and crucial indicators such as the degree of mobility, even relatively to mobile connecting clients.

Underwater environment represents a challenging and promising application scenario for sensor networks. Due to hard constraints imposed by acoustic communications and to high power consumption of acoustic modems, in underwater sensor networks (USN) energy saving becomes even more critical than in traditional sensor networks. In this paper the authors propose delay-tolerant data dolphin (DDD), an approach to apply delay-tolerant networking in the resource-constrained underwater environment. DDD exploits the mobility of a small number of capable collector nodes (namely dolphins) to harvest information sensed by low power sensor devices, while saving sensor battery power. DDD avoids energy-expensive multi-hop relaying by requiring sensors to perform only one-hop transmissions when a dolphin is within their transmission range. The paper presents simulation results to evaluate the effectiveness of randomly moving dolphins for data collection.

The rapid diffusion of heterogeneous forms of wireless connectivity is pushing the tremendous growth of the commercial interest in mobile services, i.e., distributed applications to portable wireless terminals that roam during service provisioning. In the case of both location-dependent mobile services and mobile services with session continuity requirements, there is a growing need for decentralized and lightweight solutions to predict cell handovers, in order to enable proactive service management operations that anticipate actual terminal reconnections at their newly visited cells. The paper discusses how to predict client handovers between IEEE 802.11 cells in a portable and completely decentralized way, only by exploiting RSSI monitoring and with no need of external global positioning systems. In particular, the paper focuses on proposing and comparing different filtering techniques for mitigating Received Signal Strength Indication abrupt fluctuations. Experimental results point out that i) filtering techniques can relevantly improve the efficiency and effectiveness of handover prediction, and ii) the choice of the most appropriate filtering solution to adopt should be made at provisioning time depending on specific service/system requirements, e.g., privileging minimum overhead vs. greater prediction proactivity.

New challenging deployment scenarios are accommodating limited and heterogeneous portable devices that roam among wireless access localities during service provisioning with session maintenance and continuity requirements, such as in multimedia streaming. That calls for novel middlewares able to dynamically personalize service quality, with no interruptions while clients move in wired-wireless integrated networks at provision time. The paper proposes a middleware-level proactive buffering solution for streaming continuity based on mobile proxies. Mobile proxies execute in the wired network locally to their wireless clients and proactively migrate to maintain colocality with associated roaming devices. In addition, apart from traditional client-side buffering, mobile proxies proactively manage pre-fetching buffers of multimedia contents by dynamically adapting buffer size to the current context such as handoff probability, client/streaming characteristics, and user service class. Experimental results show that, notwithstanding portable Java-based implementation, our context-aware proactive and adaptive buffering does not experience streaming interruptions in most common wireless Internet deployment scenarios

The widespread diffusion of mobile computing calls for novel services capable of providing results that depend on both the current physical position of users (location) and the logical set of accessible resources, subscribed services, preferences, and requirements (context). Leaving the burden of location/context management to applications complicates service design and development. In addition, traditional middleware solutions tend to hide location/context visibility to the application level and are not suitable for supporting novel adaptive services for mobile computing scenarios. The article proposes a flexible middleware for the development and deployment of location/context-aware services for heterogeneous data access in the Internet. A primary design choice is to exploit a high-level policy framework to simplify the specification of services that the middleware dynamically adapts to the client location/context. In addition, the middleware adopts the mobile agent technology to effectively support autonomous, asynchronous, and local access to data resources, and is particularly suitable for temporarily disconnected clients. The article also presents the case study of a museum guide assistant service that provides visitors with location/context-dependent artistic data. The case study points out the flexibility and usability of the proposed middleware that permits automatic service reconfiguration with no impact on the implementation of the application logic.

Recent advances in mobile ad-hoc networks (MANET) technologies promote new opportunities for anytime and anywhere impromptu collaboration and leverage the provisioning of novel collaborative services, such as emergency rescue, e-care, and troop car management. However, the design and deployment of collaborative services in MANET environments raise new group management challenges. In particular, unpredictable users/devices mobility, frequent disconnection/reconnection of devices and continuous changes in network topology call for novel middleware solutions to handle properly the transient and dynamic formation of ad-hoc groups. The paper proposes a context-aware group membership middleware (AGAPE) that bases group management decisions depending on context information, such as user location, user attributes and preferences, and access device properties. User location determines the scope of group member visibility within a network locality, whereas user requirements and device properties govern the joining to a group and influence the played role of a user within a group. AGAPE provides a set of support services to arrange/dissolve and manage ad-hoc groups on demand and propagates the visibility of available group members and of their context up to the application level to allow applications to adapt collaborative decisions and actions accordingly. The paper also presents a MANET-enabled emergency rescue application scenario to show and to evaluate the functioning of AGAPE.

There is an emerging market interest in service provisioning over dense mobile ad-hoc networks (MANETs), i.e., limited spatial regions, such as shopping malls, airports, and university campuses, where a high number of mobile wireless peers can autonomously cooperate without exploiting statically deployed network infrastructures. We claim that it is possible to exploit the high node population of dense MANETs to simplify the replication of common interest resources, in order to increase availability notwithstanding unpredictable node exits from dense regions. To this purpose, we have developed the REDMAN middleware that supports the lightweight and dense MANET-specific management, dissemination and retrieval of replicas of data/service components. In particular, the paper focuses on the presentation of different solutions for replica retrieval and for dissemination of replica placement information. We have compared and quantitatively evaluated the presented solutions by considering their ability to retrieve available replicas and their communication overhead. The original SID solution has demonstrated to outperform the others in dense MANETs and has been integrated in the REDMAN prototype.

Recent advances in Mobile Ad-hoc NETworks (MANET) technologies promote new opportunities for users to share resources from ubiquitous points of attachment, when changing physical locations and even when no statically deployed network infrastructure is available. However, the highly dynamic nature of Mobile Ad-Hoc environments causes users to experience continuous changes in the set of the locally accessible resources, thus increasing the complexity of resource sharing. Novel middleware solutions are required to support the various management issues involved in resource sharing in MANETs environments. In particular, it is crucial to handle and to propagate up to the application level the visibility of both the users that are willing to group together to share their resources and of the resources they decide to share. The paper proposes a group management middleware (AGAPE) that, as a key feature, exploits the visibility of context information, e.g., user location, user attributes and preferences, access device properties, to create and discover groups of interest for resource sharing, to monitor the availability of groups members, and to dynamically arrange/requalify group members bindings to shared resources as changes in context operating conditions occur. Application developers can exploit the AGAPE support to build on top of it various application-specific resource sharing strategies and mechanisms, such as Global Virtual Data Structures. Finally, the paper presents a MANET-enabled emergency rescue application scenario to show and to evaluate the functioning of AGAPE.

New challenging deployment scenarios are accommodating portable devices with limited and heterogeneous capabilities that roam among wireless access localities during service provisioning with session continuity requirements, such as in multimedia streaming. The paper proposes an original two-level buffering strategy to maintain streaming continuity independently of client roaming at provision time. In particular, it focuses on a specific component of the proposed support infrastructure, i.e., the pure Java buffering component, which has been shown to outperform the standard Java Media Framework in both streaming initialization time and imposed overhead.

The increasing diffusion of wireless-enabled portable devices is pushing towards service provisioning over dense mobile ad-hoc networks (MANETs), i.e., in limited spatial regions, such as shopping malls, railway stations and airports, where a high number of mobile wireless peers autonomously cooperate, without the need for statically deployed network infrastructures. Dense MANET deployment scenarios can take advantage of high node population to replicate common-interest resources to increase their availability, by overcoming the unpredictable node exit from the dense region. The paper proposes a lightweight middleware, called REDMAN (replication in dense MANETs), to manage, retrieve and disseminate replicas of data/service components made available by cooperating nodes in a dense MANET. In particular, the paper focuses on the REDMAN original solutions to determine the nodes belonging to dense MANETs without exploiting any positioning system and also to elect dynamically a suitable replica manager node in charge of enforcing the desired resource replication degree in a lazy consistent way. Experimental results show that REDMAN solutions are lightweight and effective in dense MANET scenarios with almost constant node density and even high node mobility.

Pervasive user mobility, wireless connectivity and the widespread diffusion of portable devices raise new challenges for ubiquitous service provisioning. In particular, mobility of users/devices causes frequent and unpredictable changes in physical user location and in consequently available resources and services. Users can also change portable access devices, with different capabilities, even at runtime and during the same service session, thus forcing us to consider very dynamic aspects even due to client heterogeneity. Access control to resources is crucial to leverage the provision of ubiquitous services and calls for novel solutions based on various context information, e.g., user/device location, device properties, user needs, local resource visibility. This work presents a novel access control model built upon the concept of context as the first-class design principle to rule access to resources. As key features, this model allows to associate access control permissions with contexts where users operate and users acquire/lose their permissions when entering/leaving a specific context. Unlike traditional access control solutions where user identity/role triggers policy evaluation when requesting resource access, this model exploits the user context to fully determine the set of available permissions. In addition, the proposed model allows to express context-based access control policies at a high level of abstraction cleanly separate from service logic implementation, thus promoting dynamic policy modification with no impact on the service code. The paper shows the implementation of the proposed model in the UbiCOSM framework and presents a mobile office service provisioning scenario.

New challenging service scenarios are integrating mobile wireless devices with limited and heterogeneous capabilities. This not only calls for novel solutions to support different forms of mobility and connectivity in wired-wireless integrated networks, but also stresses the necessity of quality of service (QoS) tailoring and adaptation, especially in resource-consuming applications such as multimedia distribution. The work presents mobile ubiQoS, a mobile agent (MA) middleware to manage the QoS provisioning of multimedia content to client devices that roam in the wireless Internet. In particular, the paper focuses on how mobile ubiQoS provides a portable solution to predict inter-cell mobility, without any external global positioning system. Mobility prediction permits to migrate personalized MAs, which tailor/adapt multimedia contents to both device profiles and user preferences, by anticipating user movements. This is crucial to proactively rearrange personalized sessions by maintaining service continuity. The adopted prediction solution is lightweight and decentralized, by exploiting the client-side monitoring information about the received signal strength of IEEE 802.11 base stations in a completely portable way. Both simulation and experimental results show that, notwithstanding the portable application-level approach, our middleware can predict the. next user cell location enough in advance to support continuous multimedia services for the wireless Internet.

Advances in wireless solutions and portable devices are enabling new challenging service scenarios where mobile users are willing to access ubiquitous and continuous services. This calls for novel middleware capable of tailoring service contents to client characteristics and of following client movements at provision time. The paper proposes MUM, a dynamic and flexible middleware to support continuous services to mobile users in ubiquitous scenarios. MUM performs service configuration by dynamically distributing middleware components to intermediate nodes along the client-server path and provides service session continuity by automatically migrating the session state in response to user movements during service provisioning. MUM exploits mobile agents to move both middleware components and session state, where and when needed, while it allows service developers to continue using the traditional client/server model for MUM-based application components. In addition, The work presents the implementation of a Video-on-Demand service on top of MUM, with the goal of verifying the feasibility of our approach when applied to the challenging multimedia application area. First experimental results show that, notwithstanding the application-level approach, the MUM configuration/session migration times are compatible even with the strict requirements imposed by multimedia distribution over the best-effort Internet.

The growing diffusion of wireless-enabled portable devices and the recent advances in mobile ad-hoc networks (MANET) open a new scenario where users can benefit from anywhere/anytime impromptu collaboration. However, the development of collaborative services in MANET environments raises new challenges and calls for novel middleware solutions to handle properly the communication between transiently collaborating partners. The paper proposes AGAPE, a context-aware group communication middleware that permits to select collaborating partners, to schedule incoming messages and to tailor their presentation on the basis of group members context, e.g. depending on member's location, attributes, and device properties.

Wireless connectivity and the widespread diffusion of portable devices raise new challenges for ubiquitous service provisioning. Mobility of users causes frequent and unpredictable changes in user location and in consequently available resources. Access control to resources is crucial to leverage the provision of ubiquitous services and calls for novel solutions based on various context information, e.g., user location, device properties, user needs, local resource visibility. This work presents a novel access control model that proposes the adoption of context as a first-class design principle to rule access to resources. The paper proposes a context-centric access control middleware, called UbiCOSM, that dynamically determines the contexts of mobile users and effectively rules the access to them, by taking into account different types of metadata: user profiles and system/user-level authorization policies. The paper also presents a context-dependent movie-info service to evaluate the functioning of UbiCOSM.

The widespread diffusion of mobile computing along with the integration of telecommunication systems and the Internet enables a scenario where the promise of ubiquitous computing is starting to be realised. This scenario calls for novel services that can be deployed on-demand close to the user and customised to not only client needs, but also to the client current location. In particular, group membership management services should exploit the visibility of client location to organise effective solutions for group communication and interoperation and to promote the design and development of advanced collaborative applications, such as traffic management and e-care ones. The paper describes a middleware for group membership management (AGAPE) that exploits both the visibility of users position and the heterogeneous characteristics of the access terminals to facilitate interoperation in a pervasive computing scenario.

The provisioning of Web services over the wireless Internet introduces novel challenging issues for service design and implementation: from user/terminal mobility during service execution, to wide heterogeneity of portable access devices and unpredictable modifications in accessible resources. In this scenario, there are frequent provision-time changes in the context, defined as the logical set of accessible resources depending on client location, access terminal capabilities, and system/service management policies. The development of context-dependent services requires novel middlewares with full context visibility. We propose a middleware for context-aware resource management, called CARMEN, capable of supporting the automatic reconfiguration of wireless Internet services in response to context changes without any intervention on the service logic. CARMEN determines the context on the basis of metadata, which include declarative management policies and profiles for user preferences, terminal capabilities, and resource characteristics. In addition, CARMEN exploits the mobile agent technology to implement mobile middleware components that follow the provision-time movement of clients to support locally their customized service access. The proposed middleware shows how metadata and mobile agents can favor component reusability and automatic service reconfiguration, by reducing the development/ deployment complexity.

The increasing diffusion of mobile computing and of portable devices with wireless connectivity identifies new challenging scenarios for service provisioning. The access from devices with limited heterogeneous capabilities to traditional and novel Internet services requires new infrastructures capable of integrating with the fixed network and of supporting service tailoring/adaptation. The paper presents a mobile agent-based middleware for the distribution of video on demand (VoD) to portable devices. Mobile agents can act as device proxies over the fixed network, can negotiate the proper QoS level and can dynamically tailor VoD flows depending on profiles of terminal characteristics and user preferences. The paper also describes the design and implementation of a motion picture-information service prototype, built on top of the proposed middleware. The prototype shows the feasibility of distributing motion picture trailers ubiquitously even to portable devices with strict constraints on computing power and visualization capabilities, e.g., Palm personal digital assistants hosting the Java KVM/CLDC/MIDP software suite.

The convergence of mobile telecommunications and the Internet global system forces us to reconsider traditional client/server solutions for network and systems management. The paper claims that accounting in the mobility-enabled Internet requires support infrastructures hosted in the fixed network. These infrastructures should monitor, control and register resource consumption locally within the domains where users/terminals dynamically move to, without requiring continuous connectivity with remote and centralized accounting home managers. In addition, the paper shows that the mobile agent (MA) technology is suitable to overcome the limits of traditional accounting solutions in several mobility-enabled usage scenarios. MA can maximize locality in accessing monitoring data, can enable accounting even in case of temporary disconnection, can install new monitoring/control behavior dynamically, and can support session-dependent solutions. The paper finally presents the design and implementation of the MA-based Middleware for Mobility Accounting Management (MAM/sup 2/), together with some use cases showing the advantages of the MA adoption.

The control and management of Web-based service quality require the extension of the Internet infrastructure with monitoring functions to ascertain dynamically the state of networked resources. We describe the design and implementation of the Monitoring Application Programming Interface (MAPI), a Java-based tool for the on-line monitoring of Internet heterogeneous resources, which provides monitoring indicators at different levels of abstraction. At the application level, it instruments the Java Virtual Machine (JVM) to notify several different types of events triggered during the execution of Java applications, e.g. object allocation and method calls. At the kernel level, MAPI inspects system-specific information generally hidden by the JVM, e.g. CPU usage and incoming network packets, by integrating with Simple Network Management Protocol agents and platform-dependent monitoring modules. MAPI is the core part of a portable tool for distributed monitoring, control and management in the Internet environment. The tool is implemented in terms of mobile agents that move close to the monitored resources to enforce distributed management policies autonomously, with a significant reduction in both reaction time and traffic overhead.

Mobile code (MC) technologies provide appealing solutions for the development of Internet applications. For instance, Java technology facilitates dynamic loading of application code from remote servers on to heterogeneous clients distributed all over the Internet. However, executing foreign code that has been loaded from the network raises significant security concerns which limit the diffusion of these technologies. Substantial work has already been done to provide security solutions for protecting both hosting nodes and MC. For example, the Java security architecture evolved from a rigid sandbox model to a more flexible solution where downloaded code can perform any kind of operation, depending on its source location and signature. However, the most widespread security solutions for MC platforms today do not support the sophisticated security policies required in modern inter-organisational environments. This requires expressive languages to specify the policy and flexible mechanisms for policy implementation which cater for code mobility. This paper shows how access control policies for MC-based applications can be specified in a concise and declarative language called Ponder, and how these policies can be implemented within the Java security architecture.

The telecommunication and the Internet scenarios have pointed out the possibility of accessing resources and services while moving in open distributed global systems. Mobility should allow users to access services and to maintain their preferred working environment independently of their current point of attachment, and has motivated the investigation of new models and solutions. The mobile agent technology is intrinsically suitable to describe, model and implement mobility. The paper describes how a mobile agent framework, called SOMA, can provide an infrastructure to support not only the traditional concepts of terminal and user mobility but also the mobility of resources in general. SOMA permits terminal mobility by introducing the mobile place abstraction that represents a mobile host for agent execution, and user mobility by supporting the virtual home environment service. SOMA supports resource mobility via the resource discovery service that can preserve client/server relationships among SOMA resources and users independently of current positions. The paper also gives experimental results about the costs associated with the main mechanisms for supporting terminal, user, and resource mobility.

Technological and human factors have contributed to increase the complexity of the network management problem. Heterogeneity and globalization of network resources, on one hand, have increased user expectations for flexible and easy-to-use environments; on the other hand, they have suggested entirely novel ways to face the management problem. Several research efforts recognize the need for integrated solutions to manage both network resources and services in open, global, and untrusted environments. In addition, these solutions should permit the coexistence of different management models and should interoperate with legacy systems. In the paper, we define a general architecture based on a distributed processing environment (DFE) that offers a large set of facilities to the application level. We have developed the MESIS management environment shaped after the above architecture and its DPE facilities with mobile agents technology. MESIS handles, in a uniform way, both resources and services, and focuses on two crucial properties: interoperability to overcome heterogeneity, and security to grant users safe and protected operations. The Agent Interoperability Facility supports compliance with CORBA-based management systems and with MASIF agent platforms. The Agent Security Facility provides authentication, integrity, privacy, authorization, and secure interoperation with CORBA systems.

The Mobile Agent (MA) paradigm seems to be a promising technology for developing applications in open, distributed and heterogeneous environments, such as the Internet. Mobile agents can overcome some of the limits of the traditional client/server model and can easily integrate with the Web to improve application accessibility. Many application areas, such as electronic commerce, mobile computing, network management and information retrieval can benefit from the application of the MA technology. However, a wider diffusion of MA is currently limited by the lack of a comprehensive security framework. Answering to the requirement of protection for both execution sites and mobile agents can boost the acceptance of the MA paradigm in the Internet environment. The paper describes an MA environment, called Secure and Open Mobile Agent (SOMA), that is based on a thorough security model and provides a wide range of tools and mechanisms to build and enforce flexible security policies. In particular, we focus on the problem of how mobile agents can be protected from malicious behavior of execution sites and we propose a distributed multiple-hops integrity protocol for mobile agent protection, fully integrated in SOMA.

The Mobile Agent technology is suitable for applications in open, distributed and heterogeneous environments such as the Internet and the Web, because it can overcome some limits of traditional approaches. The paper describes a Secure and Open Mobile Agent (SOMA) programming environment with two main design objectives that are security and interoperability. On the one hand SOMA is based on a thorough security model and provides a wide range of tools and mechanisms to build and enforce flexible security policies. On the other hand, the SOMA framework can interoperate with different application components designed with different programming styles. SOMA grants interoperability by closely considering compliance with CORBA, the most diffused standard in the area of Object-Oriented components. SOMA has been adopted as a platform to develop several distributed applications in the area of network and systems management, CSCW, and distributed and heterogeneous information systems.

Within the framework of distributed object-oriented programming, this paper illustrates the main features of a communication micro-kernel able to perform, in a transparent way, both local and remote communications among objects located on a network of closely coupled microcomputers. The communication micro-kernel allows one to address an object by specifying its virtual location, which usually corresponds to a set of microcomputers considered as a whole. By default, an object is allowed to migrate within the bounds of a given virtual location; hence, any migration activity may occur without overloading the network with broadcast messages. As a high-level environment, a non-standard CLOS-like interpreter, enhanced with message-passing capabilities, has been defined and used to test the communication micro-kernel. Due to the virtualization capabilities provided by such an environment, both an application and the network where the application is intended to be run can be decomposed into several layers and then merged together, thus making it possible to meet hardware and software requirements at a suitable level of abstraction. We believe that the described communication micro-kernel will turn out to be very useful in building adaptive systems and in improving their load-balancing by dynamically performing object/method migration. © 1999 John Wiley & Sons, Ltd.

The tuple space abstraction is a powerful and general coordination model for parallel and distributed programming. However, this model is based on the abstraction of a global space, difficult to implement in distributed memory parallel systems with high-performance and scalability. The paper discusses the possible distribution strategies to implement a tuple space server and proposes a new replication policy suitable for massively parallel systems. The proposed strategy achieves scalability by organising the system in a hierarchical way and by integrating coherence protocols of limited overhead. Moreover, the chosen organisation encourages the presence of multiple tuple spaces each with a constrained scope. The paper describes and analyses a transputer-based implementation: the hierarchical organisation of the tuple space makes the access time proportional to the locality of the references and bounded by the logarithm of the system size. © 1998 John Wiley & Sons, Ltd.

Monitoring tools are necessary components in the support of distributed applications and can be used to provide dependability, debugging and testing, to enhance the performance and to make possible the run-time steering of applications. These tools are needed to exploit in the best way all the available high performance computing resources of a heterogeneous environment. The paper describes HOLMES, an on-line monitoring system designed to support dynamic management of resources that requires run-time measurement. HOLMES identifies the evolving system state and provides the necessary information to any dynamic policy to assign resources by following application evolution. HOLMES makes possible to control and steer an application even distributed across heterogeneous architectures, from parallel machines to clusters of workstations and PCs.

Cellular automata are used for designing high performance applications in many areas. The paper describes CARPET, a high level programming language based on the cellular automata model. CARPET is a programming language designed to support the development of parallel high performance software. It exploits the computing power of a highly parallel computer releasing a user from using explicit parallel constructs. A CARPET implementation has been used for programming cellular algorithms in the CAMEL parallel environment. With CARPET, a user might write programs to describe the actions of thousands of simple active agents interacting locally, then the CAMEL environment allows a user to observe the global complex evolution that arises from their parallel execution and their local interactions.

The paper presents the Abstract Configuration Language (ACL) implemented within the Parallel Objects object oriented parallel programming environment. ACL defines a set of directives that allow users to specify the allocation needs of his/her application components without being aware of the architectural details. ACL directives drive the allocation decisions of the run time support, by adapting its general purpose behaviour to follow an applications particular allocation needs. The effectiveness of the ACL approach in increasing the performances of parallel applications is confirmed by a testbed application.