Suitable Service Provider Research Articles

Efficient Matchmaking in Assignment Games with Application to Online Platforms

Online platforms that match customers with suitable service providers utilize a wide variety of platform designs: some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Services); some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork); others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these platform designs in terms of their efficiency of matchmaking, as proxied by the amount of communication needed to facilitate a good market outcome. We find that the relative performance of these designs is driven by whether the preferences of agents on each side of the market is easy to describe or satisfy. ``Easy to describe'' means that the preferences can be readily captured in a short questionnaire, and ``easy to satisfy'' means that an agent has high preferences for many potential partners. For markets with suitable characteristics, each of the above designs is able to provide performance guarantees that are asymptotically close to the best possible using any conceivable system of matchmaking. The analysis provides prescriptive insights for online platforms.

A Novel Multi-Criteria Decision-Making Model for Building Material Supplier Selection Based on Entropy-AHP Weighted TOPSIS.

The type of criterion weight can be distinguished according to different decision methods. Subjective weights are given by decision makers based on their knowledge, experience, expertise, and other factors. Objective weights are obtained through multi-step calculations of the evaluation matrix constructed from the actual information about the evaluation criteria of the alternatives. A single consideration of these two types of weights often results in biased results. In addition, in order to build an effective supply chain source, buyers must find suitable quality products and/or service providers in the process of supplier selection. Based on the above reasons, it is difficult to accurately select the appropriate alternative. The main contribution of this paper is to combine entropy weight, analytic hierarchy process (AHP) weight, and the technique for order preference by similarity to an ideal solution (TOPSIS) method into a suitable multi-criteria decision making (MCDM) solution. The TOPSIS method is extended with entropy-AHP weights, and entropy-AHP weights are used instead of subjective weights. A novel decision-making model of TOPSIS integrated entropy-AHP weights is proposed to select the appropriate supplier. Finally, we take the selection of building material suppliers as an example and use sensitivity analysis to show that the combination of the TOPSIS method based on entropy-AHP weights can effectively select the appropriate supplier.

Exploring patient choice in India: A study on hospital selection

ABSTRACT With the vital role of patients’ involvement and empowerment in selecting the most suitable service provider, this study provides an insight into the patient choice factors that affect them when choosing a hospital for treatment. The patient choice concept is essential to understand in a developing country like India, which struggles to provide public-funded health services to its citizens. This paper uses empirical analysis to evaluate data collected from a diverse patient pool of 258 patients from both public and private sector hospitals in two Indian states (New Delhi and Chandigarh). The study reveals quality of treatment, cleanliness, hospital reputation and amenities like payment and food facilities as important patient choice factors considered while choosing a health service provider in the Indian context. It was also found that service provider’s response to majority of patient choice factors was indicated unsatisfactory by the patients, and hence a need for improved service delivery is emphasised for hospitals through this study.

A Proposition of Modifications and Extensions of Cloud Computing Standards for Trust Characteristics Measures

In recent years, we have witnessed a marked rise in the number of cloud service providers with each offering a plethora of cloud services with different objectives. Gaining confidence for cloud technology adoption as well as selecting a suitable cloud service provider, both require a proper evaluation of cloud service trust characteristics. Hence, the evaluation of cloud services before used by the customer is of utmost importance. In this article, we adapt the extracted trust characteristics from both system and software quality standards and cloud computing standards, for evaluating cloud services. Moreover, we derive measures for each trust characteristics to evaluate the trustworthiness of different cloud service providers, and generalize these trust measures for any type of cloud services (e.g. Software as a Service, Platform as a Service, and Infrastructure as a Service). Our work thereby demonstrates a way to apply generalized trust measures for cloud services and therefore contributes to a better understanding of cloud services to evaluate their quality characteristics. As part of our ongoing research, the results of this study will be used to develop a comprehensive cloud trust model.

CATSWoTS: Context Aware Trustworthy Social Web of Things System.

The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.

Implementing Fuzzy TOPSIS in Cloud Type and Service Provider Selection

Cloud computing can be considered as one of the leading-edge technological advances in the current IT industry. Cloud computing or simply cloud is attributed to the Service Oriented Architecture. Every organization is trying to utilize the benefit of cloud not only to reduce the cost overhead in infrastructure, network, hardware, software, etc., but also to provide seamless service to end users with the benefit of scalability. The concept of multitenancy assists cloud service providers to leverage the costs by providing services to multiple users/companies at the same time via shared resource. There are several cloud service providers currently in the market and they are rapidly changing and reorienting themselves as per market demand. In order to gain market share, the cloud service providers are trying to provide the latest technology to end users/customers with the reduction of costs. In such scenario, it becomes extremely difficult for cloud customers to select the best service provider as per their requirement. It is also becoming difficult to decide upon the deployment model to choose among the existing ones. The deployment models are suitable for different companies. There exist divergent criteria for different deployment models which are not tailor made for an organization. As a cloud customer, it is difficult to decide on the model and determine the appropriate service provider. The multicriteria decision making method is applied to find out the best suitable service provider among the top existing four companies and choose the deployment model as per requirement.

Renting Out Cloud Services in Mobile Vehicular Cloud

Vehicular cloud is a promising paradigm in which mobile vehicles can be both cloud users and service providers. It enables vehicles that have sufficient resources to act as mobile cloud servers by offering a wide range of services to user's vehicles. However, user vehicles need to discover provider vehicles along with their services, and request targeted services from them, especially with the high mobility of vehicles. Moreover, provider vehicles and their services are characterized by specific quality criteria and consumer vehicles need to select the most suitable services. In this paper, we design a new protocol that enables vehicles to discover and consume services of mobile cloud servers that are moving nearby. The protocol harnesses public buses, which provide a stability in terms of time (regular timetables) and space (fixed lines in an urban area), making them act as cloud directories, where provider vehicles can register their services. Public buses spread service registration information to the buses within the same bus line, and to buses of connected lines, as well. A consumer vehicle can discover the available services, along with their constraints, by querying the buses in their vicinity. To ensure service consumption, the buses provide also a routing protocol allowing communication between the provider and the consumer. Furthermore, we extend our protocol to enable, on the one hand, vehicle providers to select the most appropriate public bus for an efficient service registration, and users to select, on the other hand, the most satisfactory service, which satisfies both provider constraints and user preferences. Simulation results showed the efficiency of our protocol in terms of service discovery and service consuming delays, as well as its ability to select both the most suitable cloud directory and service provider for both providers and consumers, respectively.

Multi User Context-Aware Service Selection for Mobile Environments

Modern service systems build on top of service dominant designs which encompass contextualization (value-in-context) and collaboration (value-in-use) between users and service providers. Processes in this domain often require the consideration of both context information (e.g., location or time of day) and multiple participating users where each user probably has its own preferences and constraints (e.g., restricted overall budget). However, selecting a suitable service provider for each action of a process, especially when some of these actions are conducted together by several users, can be a complex decision problem in multi user context-aware service systems. Consequently, exact approaches are not fit to solve such a service selection problem in appropriate time. Thus, the paper proposes a heuristic technique applying a decomposition of the users’ global constraints and a local service selection. In this way, the aim is to determine a feasible service composition for each participating user while taking the users’ individual preferences and constraints as well as context information into account. The evaluation of the heuristic technique shows, based on a real-world scenario in the tourism domain, that the proposed approach is able to achieve close-to-optimal solutions while efficiently scaling with problem size and therefore can support decision makers in multi user context-aware service systems.

A trust centric optimal service ranking approach for cloud service selection

Cloud service selection, a promising research directive provides an intelligent solution via. service ranking based on the Quality of Service (QoS) attributes for the identification of trustworthy Cloud Service Providers (CSPs) among a wide range of functionally-equivalent CSPs. Further, the impact of objective and subjective assessment data on the accuracy of the service selection model makes the credibility of the assessment data, a major concern for the researchers in service-oriented environments. To address the challenges with respect to the identification of the user requirement compliant CSPs, data credibility, service ranking, etc. we present Hypergraph –Binary Fruit Fly Optimization based service ranking Algorithm (HBFFOA), a trust-centric approach for the identification of suitable and trustworthy cloud service providers. HBFFOA employs hypergraph partitioning, time-varying mapping function, helly property, and binary fruit fly optimization algorithm for the identification of similar service providers, credibility based trust assessment, selection of trustworthy service providers, and optimal service ranking respectively. Experiments using synthetic QoS dataset from WSDream#2 illustrates the effectiveness, practicability, scalability and computational attractiveness of HBFFOA over the existing service selection approaches in terms of precision, stability, statistical test, and time complexity analysis.

A broker-based framework for standardization and management of Cloud Security-SLAs

Security is still one of the main barriers discouraging companies and businesses which deal with sensitive information and confidential data from migrating toward the Cloud. Recent efforts have tried to specify the security level of the Cloud service with the help of Security Service Level Agreements (Security-SLAs). However, Security-SLAs in their current format and with their present terms are not fully measurable and are hard to monitor. Quantification and standardization of Security-SLAs will surely speed up the Cloud adoption process and attract more customers to benefit from the advantages of Cloud computing in a more confident and secure fashion. In this paper, we propose a broker-based framework that manages the Cloud Security-SLA. We first develop a standard, quantitative, and measurable form to represent the agreement. Then we propose an evaluation and selection model that is fundamentally based on computing the adequate trade-off between the security CIA triad attributes (Confidentiality, Integrity, and Availability) in the context of a multi-objective optimization problem. Simulation results show the set of Pareto-optimal solutions and how the customer can select the most suitable service provider using higher level information that is related to the nature of the service and financial cost.

RFID service provider selection: An integrated fuzzy MCDM approach

The Radio Frequency Identification (RFID) technology supports companies to optimize business processes and reduce operational costs by knowing an item’s condition and location in the inventory. RFID applications expand on the basis of speed, security and correct information flow; however, selecting the most suitable RFID service provider presents a challenge. This article offers an integrated multi-criteria evaluation framework for selecting the most suitable RFID service provider by applying Fuzzy Axiomatic Design (Fuzzy AD) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) techniques with Group Decision Making (GDM) approach. A case study is conducted and the results are discussed through a sensitivity analysis and a comparative evaluation with fuzzy TOPSIS. It is observed that an alternative RFID service provider that does not meet the defined Functional Requirements (FRs) is directly eliminated by Fuzzy AD, whereas fuzzy TOPSIS ranks it as an eligible one. The sensitivity analysis demonstrates that if FRs are low enough, the outcome of Fuzzy AD and fuzzy TOPSIS do converge, underlining the goal-oriented advantage of Fuzzy AD. The paper contributes to literature by presenting a novel approach for incorporating subjective and often conflicting linguistic evaluations of a decision committee into a solid decision support model approach for selecting the most suitable RFID service provider.

Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information

Group decision-making approaches are very important due to the complexity and uncertainty of many real-world decision-making problems. Some of the decision-making problems are defined in qualitative frameworks. Extended hesitant fuzzy linguistic term set (EHFLTS) is proposed as a new and powerful tool for elicitation of hesitant qualitative information in group decision-making process. In this paper, we first introduced the comparison laws and a family of distance and similarity measures for extended hesitant fuzzy linguistic terms (EHFLTs) and EHFLTSs, respectively. Next, we developed the extended hesitant fuzzy linguistic (EHFL)-VIKOR method as a qualitative multi-attributes group decision-making approach based on the EHFLTS distance measures to deal with the qualitative hesitancy in group decision making. Finally, we presented an application example about selection of suitable telecommunications service provider of small- and medium-sized enterprises to verify applicability and validation of proposed method in the process of qualitative group decision making.

Survey of Random Blood Sugar Levels Amongst Leprosy-disabled People in Bangladesh

People with leprosy-related disability in north west Bangladesh were surveyed for diabetes. According to patient reports,97 (27.1/1000) already knew they suffered from diabetes mellitus. Amongst 3573 subjects who underwent a random blood sugar test, anyone with random blood sugar level above 11.0 mmol/l was referred for confirmation of diabetes and advice (111). Unexpectedly, we also found that 30.1% asymptomatic people without a previous diagnosis of diabetes had random blood sugar in the “impaired glucose tolerance” range (i.e. 7.8- 11.0 mmol/l). These people were asked to have a second blood test for fasting blood sugar level, and if this was high (above 7.0 mmol/l) they were advised to have a review with a doctor, preferably at the local diabetic clinic. A sample of people (5%) with Blood sugar levels in the normal range were also invited to have a second test for fasting blood sugar; amongst them only 2 had elevated fasting blood sugar levels (>7.0). Thus another 14 were referred with high fasting blood sugar levels. Of those125 people (considered to be Diabetes suspects) newly-detected with hyperglycaemia, 121 attended a suitable service provider for confirmation/exclusion of diabetes, within 1month of their abnormal blood test. Of them 47 (37.6%) were diagnosed with diabetes. However, 4 people did not take action as advised, and 2 died before attending clinic. Taking into account new diagnoses and old, we estimate a minimum prevalence of 40.3/1,000 amongst leprosy-disabled people in NW Bangladesh. These findings indicate the advisability of routine screening for diabetes amongst people affected by leprosy during routine clinic reviews, and that the ability and motivation to manage their own self-care of people with leprosy related disability and diabetes should be assessed. Appropriate follow up and advice for those with blood sugar in impaired glucose tolerance (IGT) range needs consideration, to minimise their future risk.Bangladesh J Medicine Jan 2017; 28(1) : 13-23

A Cloud Service Broker Based on Dynamic Game Theory for Bilateral SLA Negotiation in Cloud Environment

In recent years, with the development of cloud computing, more and more service providers appear, making the cloud computing market more open and competitive. However, it is difficult for service consumers to choose the most suitable service provider for their own, especially for the service providers to provide the best service in the negotiated price, while the service providers and the service consumers themselves can't automatically negotiate, thus in this paper, we propose a cloud service broker framework based on dynamic game theory for bilateral SLA negotiation in cloud environment, aiming for successful negotiation among participators. This paper also proposes a Nash equilibrium point and a satisfaction degree difference algorithm, aiming to find the optimal value of SLA attributes (price and bandwidth) at the minimum satisfaction degree difference among the participators. Experimental results show that the service consumers and the service providers are able to achieve the same satisfaction degree on price and bandwidth.

A Comprehensive Web Service Selection Algorithm on Just-in-Time Scheduling

This paper proposes a comprehensive service selection algorithm (CSA) for improving performance and real-time arrangement in service selection. The algorithm integrates the service provider’s elicited preference and requestor’s planned time. This innovative CSA autonomously aggregates multiple requestors’ preferences over multicriteria from multiple providers, selecting the most suitable service provider on the basis of voting. The sequencing portion of the algorithm also runs based on time scheduling, where the extra cost of the system is minimal due to its small time window. The service arrangement process functions by identifying and utilizing the best solution for the 0-1 knapsack problem; the improved genetic algorithm finds solutions via coding and evolution. Experiment results show that the proposed CSA effectively simplifies the preference convergence of service requestors and optimizes the time scheduling of the service provider. The algorithm also enhances selection efficiency and reduces selection time, and it can be applied feasibly to multi-provider and multi-requestor service selection.

Where are we now as we merge genomics into plant breeding and what are our limitations? Experiences from RosBREED

The complete genome sequences of apple, peach, and diploid strawberry - one member of each of the three main fruit-producing branches of the Rosaceae tree - were available in 2010. Despite this achievement, virtually none of this genomics knowledge was being used to assist breeding efforts of these crops. Four years later, this gap has been bridged, with genetic information routinely used in many US apple, peach, and cherry breeding programs. For example, DNA tests predict apple crispness, peach maturity date, and cherry fruit size, enabling breeders to determine the best parents to combine and the best seedlings to advance. This application significantly reduces the wasted effort to eliminate entirely poor families and reduces the costs to grow and evaluate thousands of seedlings genetically destined to have unacceptable fruit quality or maturity date. This achievement was enabled by international community efforts, including the RosBREED project, funded by the USDA-National Institute of Food and Agriculture Specialty Crop Research Initiative (SCRI). DNA tests are now applied for high-value attributes where the targeted loci explain a large proportion of the trait variation. However, limitations to widespread adoption of these predictive tests still exist. Some limitations are due to lack of knowledge, such as an understanding of genotype by environment (G×E) interactions and loci associated with variation for other valuable attributes. Technical limitations include streamlined phasing of alleles from multiple families of pedigree-connected breeding germplasm and access to suitable commercial service providers.

Toward Vehicle-Assisted Cloud Computing for Smartphones

Mobile cloud computing is an emerging technology for facilitating complex application execution on smartphones. Cloud services are utilized not only to speed up the running of mobile applications but to save energy for smartphones as well. In this paper, we propose to combine the vehicular cloud with the infrastructure-based cloud to expand the current available resources for task requests from smartphones. In our proposed architecture, the vehicular cloud acts as a cloud service provider for smartphones. Moreover, we propose a flexible offloading strategy (FOS) to carry out task migration. The vehicular cloud is able to discover and utilize the underutilized resources in vehicles to accomplish application offloading for smartphones. The FOS estimates the efficiency of various cloud service providers based on current resource conditions and then selects the suitable cloud service provider to perform the requested task. Experimental results show that the proposed approach can improve the performance of mobile applications on smartphones in terms of task response time and energy consumption.

Personalized QOS based Ranking Approach for Cloud Service Selection

Cloud computing is an Internet-based computing model. This model enables access to resources and services on demand. Cloud computing users have applications with different Quality of Service requirements. On the other hand, there are different cloud service providers offering services with different qualitative characteristics. Determining the best cloud computing service for a specific application is a significant research problem. Ranking of cloud service providers compares different services offered by different providers based on quality of service, in order to select the most suitable cloud service provider. QoS parameters provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, realworld invocations on the service candidates are usually required. This project proposes a QoS ranking prediction framework for cloud services that eliminates delay and expenses involved in real-world service invocations. It makes use of the past service usage experiences of other users. This framework does not require any additional invocations of cloud services while making QoS ranking prediction. The algorithm is implemented by considering both cost and benefit parameters such as Response time and throughput respectively using a database containing response time and throughput values of 300 users for 10 different cloud providers. Also, Sensitivity analysis is done by varying weights of individual QoS parameters to verify the correctness of the algorithm. It is observed from the results that the proposed cloud service selection algorithm is able to appropriately choose the best cloud service provider depending on the weights of the respective QoS parameters. General Terms Algorithms

Trust-based group services selection in web-based service-oriented environments

Multi-agent technologies have been widely employed for the development of web-based systems, including web-based e-markets, web-based grid computing, e-governments and service-oriented software systems. In these service-oriented systems, service provider agents and service consumer agents are autonomous and rational entities, which can enter and leave the environments freely. For simplicity, we use terms `provider' and `consumer' to represent this two types of agents. How to select the most suitable service providers according to a service request from a consumer in such an open environment is a very challenging issue. In this paper, we propose an innovative trust model, called the GTrust model, for service group selection in general service-oriented environments. In the GTrust model, the trust evaluation for a service group is based on the functionality coverage of the group, the dependency relationships among individual services in the group, the ratings of individual services on the attributes of the service request and a similarity measurement of the extent to which reference reports can reflect the service request in terms of the priority distribution of attributes. The experimental results and analysis demonstrate the good performance of the GTrust model on the service group selection in service-oriented environments.

Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition

Aiming to provide satisfying and value-added cloud composite services, suppliers put great effort into providing a large number of service providers. This goal, achieved by providing the “best” solutions, will not be guaranteed unless an efficient composite service composer is employed to choose an optimal set of required unique services (with respect to user-defined values for quality of service parameters) from the large number of provided services in the pool. Facing a wide service pool, user constraints, and a large number of required unique services in each request, the composer must solve an NP-hard problem. In this paper, CSSICA is proposed to make advances toward the lowest possible service time of composite service; in this approach, the PROCLUS classifier is used to divide cloud service providers into three categories based on total service time and assign a probability to each provider. An improved imperialist competitive algorithm is then employed to select more suitable service providers for the required unique services. Using a large real dataset, experimental and statistical studies are conducted to demonstrate that the use of clustering improved the results compared to other investigated approaches; thus, CSSICA should be considered by the composer as an efficient and scalable approach.