Prioritizing and Scheduling Service Requests under Time Constraints

With the integration of Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), service providers are able to provide low-latency services to mobile users for profit. In this paper, we study the problem of service instance placement and request assignment in an MEC network for a given monitoring period, where service requests arrive into the system without the knowledge of future arrivals. Each incoming request requires a specific service with a maximum tolerable service delay requirement. The problem is to maximize the profit of the service provider by admitting service requests for the monitoring period, which can be achieved by preinstalling service instances into cloudlets to shorten service delays, and accommodating new services by removing some idle service instances from cloudlets due to limited computing resources. We then devise an efficient deep-reinforcement-learning-based algorithm for this dynamic online service instance placement problem. We finally evaluate the performance of the proposed algorithm by conducting experiments through simulations. Simulation results demonstrate that the proposed algorithm is promising.

To manage the delivery of services competitively on a large, global scale, an IT (information technology) service provider must efficiently use service delivery resources-in particular, skilled service delivery teams. Service requests form a large and important component of the management of a client's IT infrastructure. Currently, the fulfillment of IT service requests is often managed on a per-account basis. Service-delivery teams fulfill service requests according to account-specific processes by using an account-specific service-request-management environment, making it difficult to leverage the skills of the various delivery teams for multiple accounts. The service delivery management platform (SDMP) uses reusable service components that can be performed by multiple delivery teams and can be assembled into service compositions to which multiple clients can subscribe. The SDMP catalog is the information repository that manages service components, composition, providers, and subscriptions and is used by the service-request runtime environment to implement specific customer service requests. In this paper, we describe a catalog-based architecture for service delivery management and demonstrate how its use can provide a global service-delivery organization with a platform for achieving significant productivity gains.

Components within service-oriented systems require a mechanism to get in touch with offered services. A traditional approach from the field of Web services is a discovery agency provided by the infrastructure, where service providers register and service requestors submit their queries. In the field of dynamic adaptive systems we deal with systems which change their behavior according to the needs of their users during runtime based on context information. Therefore, the wiring of service providers and service requestors - known as the system configuration - may change often. As a consequence of the dynamics it is reasonable not to let the service requestor take care about the service discovery. Instead we propose a component model enabling the proactive configuration of service-oriented systems which will be described in this paper.

Components within service-oriented systems require a mechanism to get in touch with offered services. A traditional approach from the field of Web services is a discovery agency provided by the infrastructure, where service providers register and service requestors submit their queries. In the field of dynamic adaptive systems we deal with systems which change their behavior according to the needs of their users during runtime based on context information. Therefore, the wiring of service providers and service requestors - known as the system configuration - may change often. As a consequence of the dynamics it is reasonable not to let the service requestor take care about the service discovery. Instead we propose a component model enabling the proactive configuration of service-oriented systems which will be described in this paper.

A service-oriented architectural (SOA) model is proposed for power system reliability analysis especially for generating capacity adequacy evaluation. Generating system adequacy evaluation is performed to evaluate the ability of the system generating capacity to meet the total system demand. The proposed SOA model is composed of generation system reliability (GSR) service provider, power system service registry and service requester. The data pertaining to power system reliability analysis are represented in extensible markup language (XML) for exchanging the reliability data among the users and service providers. The GSR service provider describes the reliability evaluation services and publishes them to the power system service registry. The calling sequences of reliability evaluation services along with required data are configured as SOAP (Simple Object Access Protocol) messages and the proposed SOA model communicates between the service provider and the service requester with formally defined messages. The proposed model for generating system reliability analysis is highly distributed and has inherent features such as scalability and flexibility and provides a loosely coupled environment for power system reliability analysis.

Nowadays Web users have clearly expressed their wishes to receive personalized services directly. Personalization is the way to tailor services directly to the immediate requirements of the user. However, the current Web Services System does not provide any features supporting this such as consideration of personalization of services and intelligent matchmaking. In this research a flexible, personalized Rule-based Web Services System to address these problems and to enable efficient search, discovery and construction across general Web documents and Semantic Web documents in a Web Services System is proposed. This system utilizes matchmaking among service requesters', service providers' and users' preferences using a Rule-based Search Method, and subsequently ranks search results. A prototype of efficient Web Services search and construction for the suggested system is developed based on the current work.

Many workflow applications have time constraints. The serving time of the servers and the arrival intervals of users' service requests in a workflow are often stochastic. From the statistic angle, we can find a tiny proportion of service requests will be executed beyond the deadline in any case. Thus people can only require an acceptable proportion of service requests to be finished within the deadline normally. We try to determine its probability density function at a workflow network so that we will know the accurate proportion of requests that can be executed without delay. We also present a method to improve the proportion of the undelayed with the lowest additional cost. An experiment illustrates our method can be effectively utilized in practice

Mobile edge computing(MEC), as an emerging computing paradigm, pushes services away from centralized remote cloud to distributed edge servers deployed by multiple service providers(SPs), improving user experience and reducing the communication burden on core network. However, this distributed computing architecture also brings some new challenges to the network. In multi-SP MEC system, a SP prefers to use edge servers deployed by itself instead of others, which not only improves service quality but also reduces processing cost. The service placement and request scheduling strategies directly affect the revenue of SPs. Since the service popularity changes over time and the resources of edge servers are limited, the network system needs to make decisions about service placement and request scheduling dynamically to provide better service for users. Owing to the lack of long-term prior knowledge and involving binary decision variables, how to place services and schedule requests to boost the profit of SPs is a challenging problem. We formally formalize this joint optimization problem and propose an efficient online algorithm. First, we invoke Lyapunov optimization technology to convert the long-term optimization problem into a series of subproblems, then a dual-decomposition algorithm is utilized to solve the subproblem. Experimental results show that the algorithm proposed in this paper achieves nearly optimal performance, and it raises 25% and 70% profit compared to greedy and Top-K algorithms, respectively.

The most important topic about the Web recently is semantic Web services. The current trend of the software development is to reduce the development time, but at the same time increase the functionality, reusability and stability of the building block without worrying about how the service is implemented. As the number of services available on the Web increases, the difficulty for both the service requester and provider to look for the most suitable potential counterpart will also increase. Matchmaking therefore is an important aspect of the Web interactions, which enable both service requester and provider trim down the amount of time to meet the most suitable potential counterpart. We describe how to blend the matchmaking algorithm to the typical Web service architecture.

Problem definition: We study customer-centric privacy management in service systems. Academic/practical relevance: We explore the consequences of extended control over personal information by customers in such systems. Methodology: We adopt a stylized queueing model to capture a service environment that features a service provider and customers who are strategic in deciding whether to disclose personal information to the service provider—that is, customers’ privacy or information disclosure strategy. A customer’s service request can be one of two types, which affects service time but is unknown when customers commit to a privacy strategy. The service provider can discriminate among customers based on their disclosed information by offering different priorities. Results: Our analysis reveals that, when given control over their personal data, strategic customers do not always choose to withhold them. We find that control over information gives customers a tool they can use to hedge against the service provider’s will, which might not be aligned with the interests of customers. More importantly, we find that under certain conditions, giving customers full control over information (e.g., by introducing a privacy regulation) may not only distort already efficiently operating service system but might also backfire by leading to inferior system performance (i.e., longer average wait time), and it can hurt customers themselves. We demonstrate how a regulator can correct information disclosure inefficiencies through monetary incentives to customers and show that providing such incentives makes economic sense in some scenarios. Finally, the service provider itself can benefit from customers being in control of their personal information by enticing more customers joining the service. Managerial implications: Our findings yield insights into how customers’ individually rational actions concerning information disclosure (e.g., granted by a privacy regulation) can lead to market inefficiencies in the form of longer wait times for services. We provide actionable prescriptions, for both service providers and regulators, that can guide their choices of a privacy and information management approach based on giving customers the option of controlling their personal information.

A formal approach for matching and ranking trustworthy context-dependent services

While Web service models become increasingly popular and many service providers start to offer all kinds of Web services, the quality of service (QoS) for Web services has become an important factor for service requesters to consider during the selection process of appropriate Web services. In this paper, we present an evaluation and selection framework (CosmosQoS) for QoS-required Web services. A novel architecture and reputation appraisal model to monitor, store and evaluate the physical usage and post-condition of requested Web services in a requester-located network is proposed to better support service requesters such that they can select suitable Web services based on their QoS requirements, trustworthiness of Web services, and effect of network connection and configuration between service requesters and providers.

Web services are increasingly utilized in people’s daily lives to achieve various functionalities. Trustworthiness has become a critical factor for service provision and governance. The lack of effective trust establishment mechanisms impedes the deployment of diverse trust models for web services. One important issue is that collaborating organizations need mechanisms to bridge extant relationships among cooperating parties. Meanwhile, a trust establishment mechanism for web services must ensure privacy and owner control at all times due to the subjectivity of trust relationships. As an alternative and complementary approach to direct trust establishment, we describe an indirect trust establishment mechanism to bridge and build new trust relationships from extant trust relationships with privacy protection. Another issue is the lack of mechanisms that can directly establish trust relationships with privacy-preserving capabilities for web services. Current web service technologies encourage a service requester to reveal all its private attributes in a pre-packaged digital credential to the service provider to fulfill the requirements for verification. This may lead to privacy leakage. We propose a mechanism whereby the service requester discovers the service provider’s requirements from a web service policy document, then formulates a trust primitive by associating a set of attributes in a pre-packaged credential with a semantic name, signed with the requester’s digital signature, to negotiate a trust relationship. Thus the service requester’s privacy is preserved because only those attributes required to build a trust relationship are revealed.

Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.

Web service is a growing technology that has been widely adopted by many organizations. It has provided a medium for communication between service provider and service requestor in the business environment. The role of semantic in web service has played an important role in this communication by allowing automatic discovery, selection and composition between the service provider and requestor. However, both service provider and the requestor are unable to achieve their business goals when there are miscommunications between their processes. This paper presents an overview of the process incompatibility between Web Services and the ways to automatically resolve them. Several approaches have been analyzed to understand the existing process mediation framework. These approaches have been presented in three categories namely Rule-based, Pattern-based and Planner-based. Moreover, we have compared these approaches based on few criteria such as expressiveness, automation, correctness and completeness.