Edge service monitoring is crucial for ensuring the robustness and reliability of service executions. Predictive monitoring, in particular, enables proactive detection of potential service violations. Existing predictive monitoring approaches, often leveraging Signal Temporal Logic (STL) for requirement specification, primarily focus on deterministic signals, and thus, lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper introduces Bayesian STL (BSTL), an extension of STL that enables probabilistic reasoning over stochastic signals. Specifically, Bayesian Neural Networks (BNNs) are utilized to transform deterministic sequential predictions into sequences of posterior probability distributions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, a BSTL-based predictive monitoring framework is developed, wherein service constraints are formally specified by BSTL formulae and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of BSTL formulae are rigorously estimated. Extensive experiments on publicly available datasets demonstrate that BSTL outperforms baseline techniques in expressiveness, robustness, and applicability.

Anomaly detection has emerged as one of the core research topics to support workflow applications across various domains. To differentiate anomalies from underlying normal patterns of workflows, Graph Neural Networks (GNNs) models have been introduced. These models leverage time series data to construct graph structures, in order to explicitly capture task dependencies among industrial Internet of Things (IoT) devices, and thus, to identify deviations from predicted behaviours as anomalies. However, existing forecasting-based anomaly detection methods may not accurately detect certain anomalies, since they have seldom considered valuable information uncovered by historical sensory data, but presented as domain knowledge. To address this limitation, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Based on which, a knowledge-enhanced graph attention-based forecasting network is developed to predict the future behaviours of IoT devices. Anomalies, such as those caused by cyber-attacks in workflows, are detected by analyzing deviations from these predicted behaviours in conjunction with domain-specific knowledge. A case study is presented, along with extensive experiments conducted on publicly available datasets. Evaluation results demonstrate that KeAD outperforms the state-of-the-art techniques in terms of anomaly detection accuracy.

We propose a failure-resilient Swarm-based Drones-as-a-Service (SDaaS) framework for reliable delivery services. Our framework is specifically tailored to address soft failures, characterized by degradation in drone performance. Failures within drone swarms are an inherent part of real-world scenarios and should be factored to ensure the reliability of services. This framework emphasizes post-assessment of failures as a critical step in preplanning for subsequent service compositions. The framework detects soft service failures from tolerable behavior deviations using margin-aware pruning. Furthermore, a failure severity score is computed using signal-based heuristic. Our aim is to minimize the impact of soft failures on consumers by delivering services within the expected time. This is achieved through formation reordering and service recomposition. Experimental results prove the efficiency of the proposed model with respect to failure correlation scores and service delivery times.

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

Smart IoT devices and applications in smart cities exchange important real-time information with their environment. However, a subset of these systems may face limitations in analyzing and processing the required large amounts of data to meet ultra-low-latency criteria. This limitation could be attributed to factors such as constrained CPU and battery resources. Thanks to the 5G and edge computing capabilities, a viable solution involves migrating a subset of these latency-sensitive and computationally intensive tasks to edge nodes and servers. This strategic service placement ensures a safe continuity of the application. In this paper, autonomous cars operating in smart cities, engaging in continuous data exchange with their external environment to meet real-time and latency-sensitive requirements, serve as an illustrative example of smart applications. The car’s decision service is strategically placed on edge nodes through a proactive (re)placement approach designed for dynamic and mobile environments. This approach uses a quality of service (QoS) metric prediction degradation module, which leverages Exponential smoothing methods to identify a suitable edge node for hosting the car’s decision module, with latency as a key criterion. Multiple configurations for outlier detection techniques are evaluated. A proof-of-concept validates the chosen model by comparing it to the AutoRegressive Integrated Moving Average (ARIMA) and the proposed proactive service (re)placement approach. This approach ensures the continuity of the placed module, suggesting the feasibility of locating non-critical modules on edge nodes.

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Abstract Service providers provision more and more Internet-of-Things (IoT) services in the cloud for dynamicity and cost-effectiveness purposes. This is made possible thanks to the introduction of edge computing that brings additional computing and resources for analytics close to the data sources and thus enables meeting the low latency requirement. Edge nodes should support (i) the heterogeneity of IoT devices (e.g., sensor, actuator) and (ii) characteristics (e.g., mobility, location awareness). IoT is already integrated to the hybrid cloud/edge environment. However, the ecosystem lacks of automation due to the previously mentioned characteristics. Indeed, edge nodes are often manually selected during deployment time, and most of the regular quality-of-service (QoS) management procedures remain difficult to implement. This paper introduces a comprehensive semantic model called EdgeOnto. It encompasses all concepts related to IoT applied in the context of edge computing. The ultimate goal of EdgeOnto is to automate the several steps that make up the IoT services lifecycle in hybrid cloud/edge environment. On the one hand, semantics enable an automatic discovery of the relevant edge nodes that are suitable to host and execute IoT services considering their requirements. On the other hand, it allows supporting the specific QoS procedures that are related to such setting (e.g., low latency, mobility, jitter). The core ontology was designed with the Protégé open-source tool. A smart strawberry farming use case was implemented and evaluated for illustration purposes. The results validate the accuracy and the precision of the designed semantic matchmaker.

The Internet of Things (IoT) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiableIoTservices. Considering the fact that the capacities ofIoTdevices may change dynamically, whether or not, and to what extent, certain constraints can be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to achieveIoTservice monitoring at runtime. Specifically, we formulate the problem ofIoTservice monitoring as a constraint satisfaction problem, where multiple constraints, including spatial-temporal constraints, energy limitation, and capacity restrictions, are considered. Specification-based monitoring is developed based onSignalTemporalLogic (STL), where a novel accumulativerobustnessmetric is proposed, denotedAccumulativeSTL(ASTL), to emphasize the robust satisfaction over the entire time domain. Thereafter,IoTservice monitoring is converted toASTLformulae, and its constraint satisfaction is interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets, where various influential factors are considered. Experimental results show that ourASTLperforms better than the state-of-the-art's techniques with more robust satisfaction.

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

With the service-oriented encapsulation of Internet of Things (IoT) devices, IoT services, which are functionally compatible and non-functionally satisfiable, are composed to support domain applications. The execution of IoT services may last for a relatively long time duration in which their capacities may vary significantly. In this setting, whether or not, and to what extent, certain constraints specified upon certain IoT services can always be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to conduct IoT service monitoring at runtime. Specifically, the requirement of IoT service monitoring is formulated as a constraint satisfaction problem. Specification-based monitoring is developed leveraging Signal Temporal Logic (STL), where a novel accumulative robustness metric, denoted Accumulative STL (ASTL), is proposed to emphasize the robust satisfaction over the entire time domain. Hence, an ASTL-based mechanism is proposed to support IoT service monitoring, where prescribed constraints are converted to ASTL formulae, and interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets. Experimental results show that ASTL performs better than the state-of-the-art techniques with more robust satisfaction.

Multi-domain federations support shared network services. Many orchestrators manage the service’s lifecycle. For the shared VNF Forwarding Graph (VNF-FG) reconfiguration, orchestrators update the graph’s logical information, ensuring a consistent behavior for replicas. Only one work in the literature considers sharing the VNF-FG. However, it offers weak consistency guarantees, without considering the VNF-FG’s non-functional dependencies. In case of a conflict, while updating the VNF-FG, the orchestrators solve consensus. However, this adds latency, undermining the goal of Network Function Virtualization. This paper introduces the first coordination-free multi-domain orchestration algorithm for consistent shared VNF-FG reconfiguration. Unlike the current state of the art, the proposed algorithm skips the coordination phase, offers strong eventual consistency, and supports non-functional dependencies. We present two variants: the preventive, where transient inconsistent states are prevented; the corrective, where intermediary inconsistent states, during the updating process, are tolerated. We prove the correctness of our algorithm and evaluate it. The variants, unlike the state of the art, reconfigure consistently the shared VNF-FGs without solving consensus. They offer stronger guarantees compared to the literature, like allowing orchestrators to reject ongoing reconfigurations without a high impact on performance.

Network Function Virtualization (NFV) enables running Virtualized Network Functions (VNF) on top of any generic, commercial off-the-shelf hardware. VNFs rely on the VNF-Forwarding Graph (VNF-FG) concept to describe and implement network topologies. VNF-FGs are provisioned and managed by appropriate orchestrators. In the multidomain approach, and for complex and sophisticated network topologies, VNF-FGs are managed by multiple orchestrators. Despite being a key task in the lifecycle of network services, the reconfiguration of VNF-FGs under multi-domain orchestration has not been thoroughly explored in the literature, focused mostly on the initial VNF-FG placement and embedding. Reconfiguration enables the providers to answer to unforeseen changes in the environment. However, such distributed orchestration might lead to inconsistencies, and thus partial or total failure of network services. In turn, such inconsistencies increase the cost for providers. This paper proposes the use of causal dependencies among orchestrators to provision consistent VNF-FG reconfiguration in distributed multi-domain environments under asynchronous communication channels. The proposed model is implemented and compared with the current ETSI VNF-FG reconfiguration algorithm. Results indicate that, with this proposal, the inconsistencies are reduced compared to the reconfiguration algorithm according to the ETSI standard for VNF-FGs in multi-domain orchestration.

The European Telecommunications Standards Institute (ETSI) established the key reference architectural framework for Network Function Virtualization (NFV). The NFV framework supports several phases (e.g., design, deploy, and manage) that make up the life-cycle of Virtual Network Functions (VNFs) along with their connectivity through the Network Service (NS) concept. Furthermore, coupled with Software-Defined Networking (SDN), NFV provides a dynamic reshaping of the network control plane. For a proper NS provisioning, network providers should anticipate and handle all the possible and practical VNFs composition scenarios, even those that are either rarely or never used. This results in costly, tedious, and time-consuming procedures when designing, deploying, and managing NSs. To overcome these limitations, NSs can be further improved by using techniques that have not been deeply explored yet. In this paper, we advocate for routing VNFs with re-configurable wiring capabilities as a novel concept that would allow agile and dynamic VNFs wiring from design-time to run-time. To validate this work, we developed a proof-of-concept as a plugin on top of ETSI Open Source MANO, as well as, a running vehicle platooning prototype. The performed experiments show that our approach: (i) performs better than the SDN-based solution in terms of latency and Mean Time-To-Operation (MTTO), and (ii) shows that the associated cost, with regard to SDN-based solution. remains acceptable.

The collaboration of Internet of Things (IoT) devices is promising nowadays to achieve complex requests in edge networks. In this setting, the functionalities of IoT devices are usually encapsulated as IoT services. A request can be fulfilled by the composition of data- or computation-intensive IoT services, which require to either consume a relatively large amount of sensory data or mandate a heavy computation capacity. Discovering functionally complementary IoT services, while satisfying their pre-specified spatial constraints, is a challenge, since certain IoT services may non-exist with respect to current IoT services deployment situation. To remedy this issue, we propose an energy-aware Data- and Computation-intensive service Migration and Scheduling mechanism (DCMS) to re-schedule certain services from their hosting devices to the ones within the geographical region prescribed by the request. Extensive experiments are conducted and evaluation results show that our DCMS is promising in reducing the energy consumption and average delay, in comparison with the state of the art's techniques.

IoT search engines have attracted increasing attention from both academia and industry, since they are capable of crawling heterogeneous data sources in highly dynamic environment. To process tens of thousands of spatial-temporal-keyword queries per second, query efficiency and communication cost in IoT search engines become critical issues. To address these challenges, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n-hop neighboring activity regions, is proposed in this paper. Thereafter, frequent query results can be achieved quickly leveraging the spatial-temporal-keyword filtering index of n-hop neighbor regions through modeling keywords relevance and uncertain traveling time. Besides, we adopt STK-tree proposed previously to directly answer non-frequent queries. Extensive experiments on real-life dataset demonstrate that our method outperforms the state-of-the-art's techniques in terms of the reduction of the query time and the number of transmitted messages.

Several works in the literature have proposed migration mechanisms for VNFs, however they only consider the migration of isolated Virtual Network Functions (VNFs) instead of the migration under a shared and chained scenario. Reconfiguration of VNFs, like in the case of migration, is necessary to handle dynamic requirements for the service. However, it is not a straightforward operation. On one hand, it is necessary to coordinate VNFs to achieve migration while maintaining the end-to-end service availability. On the other hand, the new deployment can disrupt the chain and violate predefined services constraints. Moreover if there is no access to global references, migration can introduce inconsistent services due to a lack of knowledge from orchestrators. This paper focuses on the problem of coordinating orchestrators in a NFV federation to achieve migration of shared VNFs with only local domain information. It introduces a novel coordination algorithm that relies on ETSI/MANO specifications. The proposed algorithm is implemented and evaluated for validation purposes. Results show that migration satisfies the constraints for seamless migration and the algorithm obtains better performance compared to state of the art solution with a slight overhead cost when considering the shared VNFs instead of isolated VNFS.

This paper proposes one more slice of the next-generation self-driving automobile. It introduces an innovative and smart architecture that enables collaborative interactions between the autonomous cars and the urban devices available in the city. The aim of this architecture is to achieve the smart city vision where the different involved entities - such as cars, homes, citizens - exchange useful information and collaborate to reach common goals like decrease the traffic congestion. The proposed architecture sits at edges of 5G mobile telecommunication network and relies on 5G key enablers such as multi-access edge computing. A realistic use case that illustrates information exchange between an autonomous car and neighboring devices during a trip is provided. As for validation, a prototype implementing the use case was developed and evaluated. The lessons learned from the performed experiments are discussed.

User Generated Videos (UGV) are the dominating content stored in scattered caches to meet end-user Content Delivery Networks (CDN) requests with quality of service. End-User behaviour leads to a highly variable UGV popularity. This aspect can be exploited to efficiently utilize the limited storage of the caches, and improve the hit ratio of UGVs. In this paper, we propose a new architecture for Data Analytics in Cloud-based CDN to derive UGVs popularity online. This architecture uses RESTful web services to gather CDN logs, store them through generic collections in a NoSQL database, and calculate related popular UGVs in a real time fashion. It uses a dynamic model training and prediction services to provide each CDN with related popular videos to be cached based on the latest trained model. The proposed architecture is implemented with k-means clustering prediction model and the obtained results are 99.8% accurate.

Internet of Things (IoT) is expected to enable a myriad of applications by interconnecting objects - such as sensors and robots - over the Internet. IoT applications range from healthcare to autonomous vehicles and include disaster management. Enabling these applications in cloud environments requires the design of appropriate IoT Infrastructure-as-a-Service (IoT IaaS) to ease the provisioning of the IoT objects as cloud services. This paper discusses a case study on search and rescue IoT applications in large-scale disaster scenarios. It proposes an IoT IaaS architecture that virtualizes robots (IaaS for robots) and provides them to the upstream applications as-a-Service. Node- and Network-level robots virtualization are supported. The proposed architecture meets a set of identified requirements, such as the need for a unified description model for heterogeneous robots, publication/discovery mechanism, and federation with other IaaS for robots when needed. A validating proof of concept is built and experiments are made to evaluate its performance. Lessons learned and prospective research directions are discussed.

Due to the recent surge in end-users demands, value-added video services (e.g. in-stream video advertisements) need to be provisioned in a cost-efficient and agile manner in Content Delivery Networks (CDNs). Network Function Virtualization (NFV) is an emerging technology that aims to reduce costs and bring agility by decoupling network functions from the underlying hardware. It is often used in combination with Software Defined Network (SDN), a technology to decouple control and data planes. This paper proposes an NFV and SDN-based architecture for a cost-efficient and agile provisioning of value-added video services in CDNs. In the proposed architecture, the application-level middleboxes that enable value-added video services (e.g. mixer, compressor) are provisioned as Virtual Network Functions (VNFs) and chained using application-level SDN switches. HTTP technology is used as the pillar of the implementation architecture. We have built a prototype and deployed it in an OPNFV test lab and in SAVI, a Canadian distributed test bed for future Internet applications. The performance is also evaluated.

Content Delivery Networks (CDN) interconnect several surrogate a.k.a replica servers that use geographical proximity as a criteria for delivering multimedia content. The delivered content may be enriched by value-added video services (e.g. in-stream video advertisements). The purpose of this demo is to show the prototype we developed in order to enable a cost-efficient and agile provisioning of value-added video services in CDNs. The prototype implements our NFV and SDN-based architecture. The application-level middleboxes that enable value-added video services (e.g. mixer, compressor) are provisioned as Virtual Network Functions (VNFs) and chained using application-level SDN switches. On one hand, we will highlight how value-added video applications can be provisioned in CDN domain. On the other hand, we will show concretely the advantages and overheads of the application-level service function chaining.

Fog computing brings cloud closer to end-users and data sources by enabling computation at the edge of the network. Low latency is the main benefit. IoT applications are often latency-sensitive. Such applications may be provisioned as component-based in a hybrid cloud/fog environment with components spanning cloud and fog. This will enable placing some of its components in the fog domain closer to the IoT devices, and consequently reduce the latency. However, provisioning applications in hybrid cloud/fog environment is still manual today. Existing PaaS do not support interacting with fog nodes, at the edge, for applications' components provisioning. This demo shows the key features of the hybrid Platform as-a-Service (PaaS) we have designed for IoT applications provisioning in cloud and fog environments. Three goals are assigned to the demo: (1) How applications can be designed and developed in such environments, (2) how the hybrid PaaS deploys the applications' components across cloud and fog nodes, and (3) how it executes and manages them using appropriate orchestration techniques.

Internet of Things (IoT) is expected to enable a myriad of end-user applications by interconnecting physical objects. Cloud computing is a promising paradigm for provisioning IoT end-user applications in a cost-efficient manner. IoT end-user applications are provisioned in cloud settings using PaaS and offered as SaaS. This paper focuses on the PaaS aspects of IoT end-user applications provisioning. It critically reviews the state of the art. The critical review discusses the PaaS on the whole spectrum of IoT verticals and also the PaaS dealing with specific IoT verticals.

Multimedia conferencing is the real-time exchange of multimedia content between multiple parties. It is the basis of a wide range of applications (e.g., multimedia multiplayer game). Cloud-based provisioning of the conferencing services on which these applications rely will bring benefits, such as easy service provisioning and elastic scalability. However, it remains a big challenge. This paper proposes a PaaS for conferencing service provisioning. The proposed PaaS is based on a business model from the state of the art. It relies on conferencing IaaSs that, instead of VMs, offer conferencing substrates (e.g., dial-in signaling, video mixer and audio mixer). The PaaS enables composition of new conferences from substrates on the fly. This has been prototyped in this paper and, in order to evaluate it, a conferencing IaaS is also implemented. Performance measurements are also made.

This demo will show the key features of a Platform as-a-Service (PaaS) we have proposed in a research paper accepted for presentation at IEEE LANMAN 2016 conference. The proposed PaaS enables IoT applications provisioning in hybrid cloud/fog environments. Two goals are assigned to the demo. On the one hand, we will highlight how IoT applications can be provisioned in such environments. On the other hand, we will show concretely the advantages these hybrid environments have over traditional cloud environments. The provisioning in these hybrid environments enables latency reduction and processing performance enhancement. Indeed, in order to reduce latency, the applications can have some of its components running in a distant cloud and interacting with the other components running in the fog, closer to IoT devices.

Fog computing brings cloud close to end-users and data sources by enabling computation and storage at the edges of the network. An application can have some of its components running in a “distant” cloud and interacting with the other components running in the fog, closer to end-users and data sources such as wireless sensors. Low latency is the main benefit. Applications spanning cloud and fog, such as Internet of Things (IoT) applications, are still provisioned manually nowadays. This paper proposes an architecture for a Platform as-a-Service (PaaS) to automate applications provisioning in a hybrid cloud/fog environment. Cloud Foundry is used as the basis for its implementation. As a use case, the proposed PaaS was employed to provision a simple component-based IoT application that detects fire and dispatches robots to fight the fire. A prototype is built and measurements are made.

The proliferation of Cloud Computing platforms has led to the emergence of a large number of heterogeneous platform resources, services, application programming interfaces and supported frameworks and technologies. On one hand, supported frameworks vary from one Platform-as-a-Service (PaaS) to another depending on their implementations and capacities. On the other hand, each PaaS provides a specific and proprietary scenario to provision adequate resources and to deploy applications. In this paper, we propose a new PaaS-independent approach to provision in a unified way, appropriate PaaS resources for applications deployment in Cloud platforms. Our approach aims at addressing vendor lock-in restrictions and applications portability issues in existing Cloud platforms. To that end, we define a unified description model based on the Open Cloud Computing Interface (OCCI) core model that allows describing and managing an abstract Cloud resource. Our defined model consists of two main parts: (1) an OCCI platform extension which describes PaaS resources that can be provisioned by a PaaS to set-up an appropriate environment and (2) an OCCI application extension which describes application resources to deploy an application in this environment. We show the feasibility of our approach with an implementation and a use case of a realistic deployment scenario in Cloud Foundry PaaS.

With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements.

Summary Cloud computing is becoming more and more an essential component in the cyberspace system. In this context, Cloud providers offer a multitude of resources and services that constitute a heterogeneous ecosystem and a new economic model. However, several issues remain to be addressed so that the Cloud becomes an efficient part of the cyber‐physical system. In this paper, we propose to address a platform as‐a‐service (PaaS) drawback that we noted as part of our work on service‐based applications deployment in Cloud platforms. To deal with that, we propose a new approach that we called SPD approach to provision appropriate platform resources and ensure hosting and deployment of applications based on Service Component Architecture or business process orchestration (Business Process Execution Language) specifications in Cloud platforms. The SPD approach stands for the three main steps of the process that we have defined, that is, (1) slice the application deployables into a set of elementary services, (2) package them into our already developed service micro‐containers, and then (3) deploy the obtained service micro‐containers in a target PaaS provider. Our approach is a PaaS‐independent approach that allows developers to deploy service‐based applications on any PaaS regardless of its capabilities. To illustrate our approach and to experiment its performances, we provide two realistic use cases scenarios, that is, a Service Component Architecture‐based application and a Business Process Execution Language‐based process deployment in Cloud Foundry PaaS. Copyright © 2014 John Wiley & Sons, Ltd.

The study we have conducted of existing cloud platforms shows that their operating requires the use of specific and proprietary APIs. This PaaS providers' policy is hampering the interactions between different clouds. If appropriate solutions are not considered, this issue would for instance slow down the democratization of clouds federation and cooperation. In this paper, we propose (i) a unified description model that allows the representation of applications independently of the targeted PaaS for their hosting and (ii) a generic PaaS application provisioning and management API (called COAPS API). Our proposed solution applies the separation of concerns principle by separating the provisioning and the management API from the defined description model. We motivate our solution with real use case scenarios and an implementation to show its feasibility.

Cloud Computing involves typically provisioning of virtualized and often dynamically scalable resources for IT services operating. Study of developed Cloud platforms shows that they present limitation related to deployment and running of service-based applications that are built from heterogeneous services (programming languages, communication protocols and/or hosting frameworks). In this paper, we present CloudServ, a Platform as-a-Service (PaaS), that is dedicated to service-based applications. Its model extends the Open Cloud Computing Interface (OCCI) specification that is mainly dedicated to IaaS resources. In CloudServ, PaaS resources are continuously provisioned along with the arrival of deployment requests of service-based applications. Experiments of CloudServ demonstrate its good behaviour and highlights its scalability for a huge number of deployed services in Cloud context comparing to classical Cloud platforms.

Cloud Computing is a new supplement, consumption, and delivery model for IT services based on Internet protocols. It typically involves provisioning of dynamically scalable and often virtualized resources. In this environment, there are several issues related to the inadequacies of hosting platforms and mechanisms to ensure the smooth running of service-based applications (communication protocols, ESB, Service containers, etc.). In particular, architectures and implementations of service containers are not adapted to Cloud environments. In this paper, we present a new service container dedicated to one deployed service that avoids the processing limits of classical services containers. Our approach addresses scalability by reducing memory consumption and response time. The proposed service container is evaluated in several situations against well known services containers within a real Cloud Computing network.