Dynamic Service Selection Research Articles

A decentralized agent‐based semantic service control and self‐adaptation in smart health mobile applications

AbstractIn this article, we propose a decentralized agent‐based autonomic semantic service adaptation controller and reconfiguration (ASSACR) to solve the problem of context‐aware service selection and deployment for distributed healthcare mobile applications, where functional and nonfunctional users' needs are expressed using domain‐independent ontology. The main objective of this approach is to provide optimal personalized services as fast as possible. Indeed, a new ontology for semantic description and parallel management of the contextual services is presented. At first, a new dominance operator‐based constraints violation degree is used to reduce the search space. Then, optimal personalized paths that meet user's needs, preferences and devices' availability are discovered and selected in a smaller search space more effectively and efficiently using dynamic services selection algorithm, based on new ASSACR multiagent strategies. To validate the performance and efficiency of the proposed approach, we compared the proposed approach with existing multiagent strategies of the diabetic follow‐up use cases. Experimental results show that the proposed approach effectively addressed both the semantic service representation and agent aspects in terms of performance and quality service selection.

Optimal web service composition using hybrid optimization algorithm in cloud environment

In recent years, service-based applications are deemed to be one of the new solutions to build an enterpriseapplication system. In order to answer the most demandingneeds or adaptations to the needs of changed servicesquickly, service composition is currently used to exploitthe multi-service capabilities in the Information Technologyorganizations. While web services, which have been independently developed, may not always be compatible with each other, the selection of optimal services and compositionof these services are seen as a challenging issue. In this paper we propose optimal web service composition (OWSC) model, we introduce an improved Dragonfly Algorithm (IDA) to compute services whose network positions are closer to each other and to the users, which ensures the QoS of web service and network. The rule search swarm searcher (RSSA) is used to obtain the desirable characteristics from composite web services for further optimal selection of service composition set in composite planner. Then, we propose optimal tree based replanning mechanism to adapt the execution plan to the actual behavior of already executed services by a dynamic service selection at runtime.Finally, the proposed OWSC model is going to develop by cloudsim toolkit and the performance is compared with the existing models similar to our contributions in terms of QoS parameters.

Cloud providers ranking and selection using quantitative and qualitative approach

Cloud computing is basically an internet based computing, whereby shared configurable resources are provided to cloud service consumers as services on demand. As an increasing growth of cloud computing, many enterprises provide different cloud services to cloud service consumers. From cloud service consumer’s perspective, it is difficult to choose an appropriate cloud service that satisfies their QoS requirements. As requirements of one cloud service consumer will vary from another, dynamic ranking has to be used to satisfy the requirements of different cloud service consumers. A simple model is needed to address the dynamic ranking of cloud services. The dynamic ranking and selection of cloud services is solved using Linear Programming (LP) model. This project considers quantifiable attributes such as processor speed, cost, etc. and some non-quantifiable attributes like feedback to rank various cloud services according to the requirements of cloud service consumer using Linear Programming (LP) technique.

Integrating recurrent neural networks and reinforcement learning for dynamic service composition

In the service oriented architecture (SOA), software and systems are abstracted as web services to be invoked by other systems. Service composition is a technology, which builds a complex system by combining existing simple services. With the development of SOA and web service technology, massive web services with the same function begin to spring up. These services are maintained by different organizations and have different QoS (Quality of Service). Thus, how to choose the appropriate service to make the whole system to deliver the best overall QoS has become a key problem in service composition research. Furthermore, because of the complexity and dynamics of the network environment, QoS may change over time. Therefore, how to adjust the composition system dynamically to adapt to the changing environment and ensure the quality of the composed service also poses challenges. To address the above challenges, we propose a service composition approach based on QoS prediction and reinforcement learning. Specifically, we use a recurrent neural network to predict the QoS, and then make dynamic service selection through reinforcement learning. This approach can be well adapted to a dynamic network environment. We carry out a series of experiments to verify the effectiveness of our approach.

Enhancement of the Dynamic Computation-Offloading Service Selection Framework in Mobile Cloud Environment

In the era of cloud computing, any mobile device can augment its capabilities by using Cloud computation service. There are different services provided by different cloud service providers. The mobile device has to access the cloud service with minimum response time. So many a times, instead of a distant cloud, nearest cloudlet is chosen to access the service. But according to the mobility of the user, choosing the right service provider is a herculean task. Hence this paper suggests a framework to choose a cloudlet service provider in a multi-user computation offloading environment and accommodate the service that is adaptive based on the movement of the mobile device. This paper defines a framework which comprises of basically two components. The foremost one is Fuzzy KNN component which classifies the mobile device based on the access range of the device with a nearby cloudlet. The later component provides a dynamic service depending on the changes in the mobile device location. The framework exploits Fuzzy K nearest neighbour (KNN) and Hidden Markov Model to enhance the Dynamic computation-offloading service selection (EDCOSS) framework. The EDCOSS framework is analysed and tested in a simulation environment to verify the efficiency of the framework in terms of convergence of the algorithm towards computation cost with respect to different number of clients and communication channels.

Techno-economic framework for dynamic operator selection in a multi-tier heterogeneous network

Tremendous growth of the mobile network market driven by technology evolution requires a corresponding paradigm shift of the economic aspects of mobile networks. The high heterogeneity and flexibility of 5G mobile networks have led operators to shift their business models towards an open spectrum market. In this paper, we first introduce a comprehensive techno-economic framework to investigate a heterogeneous network (HetNet) that consists of two operators: a macro cell operator and a small cell operator, operating in conditions of an open spectrum market. Both operators coexist in such a way that the small cell operator leases the spectrum from the macro cell operator. In contrast to other similar studies, in our framework, we apply the concept of evolutionary stable equilibrium (ESE), which provides some level of intuition to the behaviour of the operators. In addition, we implement the method of dynamic service selection by the end users based on a utility function, which is sensitive to both the service price and the expected throughput. To analyse the techno-economic performance of the system, we develop a real-world simulation model that encompasses a 3D urban environment using real network topology from the OpenCellID database; this model includes a large number of end users with realistic mobility patterns. The simulation results show that, from the user’s perspective, the network switching mode provides better service at a lower price due to high market competition. From the view of the operator, the most profitable mode is the static contract because there is stable service demand regardless of the price. The multi-homing mode is a promising intermediate solution that can satisfy both the end users and the operators.

Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability.

A dynamic cloud service selection model based on trust and SLA in cloud computing

For high quality and trusted service selection problem, we propose a Dynamic Cloud Services Selection Model (DCSSM). Cloud service resources are divided into different service levels by Service-Level Agreement Management (SLAM). Each SLAM manages some cloud service registration information. In order to make the final trust evaluation values more practical, the model performs a comprehensive trust, which consists of direct trust and indirect trust. Firstly, combining weights consist of subjective weight and objective weight. Using rough sets to calculate objective weight, an Analytic Hierarchy Process (AHP) method is used to calculate subjective weight. The direct trust also considers transaction time and transaction amount, and then gets an accurate direct trust. Secondly, indirect trust considers user trust evaluation similarity, and it contains indirect trust of friends and indirect trust of strangers. Finally, when the transaction was completed, a direct trust dynamic update was proposed. The model is simulated using CloudSim. It was compared with three other methods, and the experimental results showed that the DCSSM performs better than the other three methods.

Towards a Security Enabled and SOA-based QoS (for the Smart Grid) Architecture

QoS and Security features are playing an important role in modern network architecures. Dynamic selection of services and by extension of service providers are vital in today’s liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with an in depth analysis on existing authentication and authorization protocols. The authors believe that QoS and security can work in parallel without adding extra burden in the Smart Grid infrastructure. This is feasible by building an in advance system for placing, scheduling, and assigning of the requests for energy consumption or production, thus decongesting the traffic in the whole network.

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well‐developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS‐aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation‐based schemes. To attack this challenge, this paper proposes a simulation‐based approach of QoS‐aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS‐aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

Dynamic Service Provisioning and Selection for Satisfying Cloud Applications and Cloud Providers in Hybrid Cloud

The paper presents a hybrid cloud service provisioning and selection optimization scheme, and proposes a hybrid cloud model which consists of hybrid cloud users, private cloud and public cloud. This scheme aims to effectively provide cloud service and allocate cloud resources, such that the system utility can be maximized subject to public cloud resource constraints and hybrid cloud users constraints. The paper makes use of a utility-driven approach to solve interaction among private cloud user, hybrid cloud service provider and public cloud provider in hybrid cloud environment. The paper presents hybrid cloud service provisioning and selection algorithm in hybrid cloud. The hybrid cloud market consists of hybrid cloud user agent, hybrid cloud service agent and hybrid cloud agent, which represent the interests of different roles. The experiments are designed to compare the performance of proposed algorithm with the other related work.

Towards an adaptive SOA-based QoS & Demand-Response Provisioning Architecture for the Smart Grid

Dynamic selection of services and by extension of service providers are vital in today’s liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, throughput, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with a data analytics/machine learning architecture to further enrich the provisioning aspect this work is bringing to the Smart Grid market of energy.

Dynamic service selection with QoS constraints and inter-service correlations using cooperative coevolution

Building business processes by Web services in cloud computing has become the hotspot of service applications. Due to the complexity and uncertainty of business environment, QoS violations of service processes often take place at run-time. To rapidly recover from failures and minimize their impacts on the original execution plan of service processes, dynamic service selection is urgently needed once potential QoS violations are detected. However, existing research works do not fully investigate QoS constraints and inter-service correlations, as well as the breach penalty caused by service adjustment. In this paper, we present a new cooperative coevolutionary approach for dynamic service selection with QoS constraints and inter-service correlations. First, a novel formal model for the dynamic service selection problem with QoS constraints and inter-service correlations is presented. Second, a Double Information based Cooperative Coevolutionary algorithm (DICC) is proposed which uses Potter’s cooperative coevolutionary framework and provides both local and global knowledge for the dynamic service selection optimization. Finally, we develop a prototype system to apply our approach and adopt different test cases to show that our DICC approach performs more effectively and efficiently than existing algorithms.

Toward an execution system for self-healing workflows in cyber-physical systems

Cyber-physical systems (CPS) represent a new class of information system that also takes real-world data and effects into account. Software-controlled sensors, actuators and smart objects enable a close coupling of the cyber and physical worlds. Introducing processes into CPS to automate repetitive tasks promises advantages regarding resource utilization and flexibility of control systems for smart spaces. However, process execution systems face new challenges when being adapted for process execution in CPS: the automated processing of sensor events and data, the dynamic invocation of services, the integration of human interaction, and the synchronization of the cyber and physical worlds. Current workflow engines fulfill these requirements only to a certain degree. In this work, we present PROtEUS—an integrated system for process execution in CPS. PROtEUS integrates components for event processing, data routing, dynamic service selection and human interaction on the modeling and execution level. It is the basis for executing self-healing model-based workflows in CPS. We demonstrate the applicability of PROtEUS within two case studies from the Smart Home domain and discuss its feasibility for introducing workflows into cyber-physical systems.

Event-Aware Framework for Dynamic Services Discovery and Selection in the Context of Ambient Intelligence and Internet of Things

The Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users’ requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment.

Modeling Location Preferences in Service Composition Using Distributed Knowledgebase

Successful Composition of a web service in dynamic environment is a big challenge and important research issue. Several service designers may produce similar or different services. In automatic service selection environment service may fail due to functional dissimilarity and non-functional property of the services (QoS) attributes. In this context, any service can be preferred for particular location. These preferences act as QoS attribute and play important role to minimize failure of the service. Knowledge of these location preferences also helps in dynamic service selection. In composition, services are present in heterogeneous environment with different location preferences. In this paper, we have developed a knowledge base for the location preferences, which we have termed as Location Affinity. We have also incorporated semantic matching phenomenon along with affinity matching of the service for distributed environment using distributed description logic.

Using Service Clustering and Self-Adaptive MOPSO-CD for QoS-Aware Cloud Service Selection

A promising way to effectively manage the composition of services in a heterogeneous and dynamic environment is to make workflow management able to self-adapt at runtime to react to changes in its environment by autonomously reconfiguring itself. Most of the proposed methodologies address this issue as a QoS-aware service selection problem in which a web service broker can dynamically select the “right” service that takes part in the composition, and adaptively change the bound service when the delivered QoS has changed.In this paper, we propose a self-organizing framework that uses a service clustering based discovery approach to effectively and efficiently support the selection of services in which runtime changes in the QoS of the services are taken into account. Two bio-inspired algorithms are designed to support a QoS-aware dynamic service selection mechanism. An ant-based clustering algorithm enhanced with a template mechanism that guides the artificial ants to move data items to construct and maintain a specific topology is adopted as a method for efficient service discovery. As a consequence, services can dynamically be discovered in a shorter time and with lower network traffic. To select the actual concrete services that best meet the user QoS requirements a Self-Adaptive Multi-Objective Particle Swarm Optimization Algorithm using crowding distance technique (MOPSO-CD) is executed using the topological map generated by the ant algorithm. In the end, simulation results show the effectiveness of the method proposed.

IoT-enabled dynamic service selection across multiple manufacturing clouds

Cloud manufacturing can manage mass manufacturing resources and capabilities, and provide them as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, reliability, location, etc. Selecting and using services from multiple MCs is a natural evolution in the best interests of service consumers. On the other side, various uncertainties in today’s highly-dynamic business environment can easily disrupt manufacturing activities, rendering original schedules obsolete. However, little work has been done to take advantages of abundant services from MCs and to effectively deal with uncertainties. To address this requirement, we propose a dynamic service selection (SS) method across multiple MCs. The proposed method uses IoT’s real-time sensing ability on service execution, Big-Data’s knowledge extraction ability on services in MCs, and event-driven dynamic SS optimization to deal with disturbances from users and service market and to continuously adjust SS to be more effective and efficient.

Cloud Service ranking and Selection using Linear Programming

Cloud computing is basically an internet based computing, whereby shared configurable resources are provided to cloud service consumers as services on demand. As an increasing growth of cloud computing, many enterprises provide different cloud services to cloud service consumers. From cloud service consumer's perspective, it is difficult to choose an appropriate cloud service that satisfies their QoS requirements. As requirements of one cloud service consumer will vary from another, dynamic ranking has to be used to satisfy the requirements of different cloud service consumers. A simple model is needed to address the dynamic ranking of cloud services. The dynamic ranking and selection of cloud services is solved using Linear Programming(LP) model. This project considers quantifiable attributes such as processor speed, cost, etc. and some non-quantifiable attributes to rank various cloud services according to the requirements of cloud service consumer using Linear Programming(LP) technique.

A Community-Based Semantic Social Context-Aware Driven Adaptation for Multimedia Documents

This paper presents an approach to enhance users experience through the use of recommendations and social networks for on-the-fly (at runtime) adaptation of multimedia documents. This paper presents also CSSAP, a dynamic service selection and assembly tool based on new user profiles and community profiles defined as set of semantic metadata, which context, quality of service and quality of experience parameters. The tool is based on community-aware semantic services and offer architecture, with three layers (semantic query, community management and semantic services). The most innovative characteristic of the tool is that it profits from the potential of semantic representation techniques to express context constraints and community's interests, while they may be useful to generate and manage of complex dynamic adaptation process. This tool improves assembly of relevant adaptation services for communities inferred social influence from a Facebook as virtual P2P environment. The proposed approach has been validated through a prototype for mobiles user of multimedia contents exchanges. The goal is to improve assembly of potential adaptation services and the efficiency and effectiveness of the authors' approach.