Kenneth Kofi Fletcher

Web Service Selection for Resolving Conflicting Service Requests

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the users own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.

Elastic Personalized Nonfunctional Attribute Preference and Trade-off Based Service Selection

For service users to get the best service that meet their requirements, they prefer to personalize their nonfunctional attributes, such as reliability and price. However, the personalization makes it challenging for service providers to completely meet users’ preferences, because they have to deal with conflicting nonfunctional attributes when selecting services for users. With this in mind, users may sometimes want to explicitly specify their trade-offs among nonfunctional attributes to make their preferences known to service providers. In this article, we present a novel service selection method based on fuzzy logic that considers users’ personalized preferences and their trade-offs on nonfunctional attributes during service selection. The method allows users to represent their elastic nonfunctional requirements and associated importance using linguistic terms to specify their personalized trade-off strategies. We present examples showing how the service selection framework is used and a prototype with real-world airline services to evaluate the proposed frameworks application.

Service Selection Based on Personalized Preference and Trade-Offs among QoS Factors and Price

With the number of services with similar functionalities rising, service users place more emphasis on non-functional attributes of services such as quality of service (QoS) and price during service selection. However, previous QoS-driven service selection methods seldom consider relationships among non-functional requirements and do not allow explicit specification of personalized preferences and trade-offs from user perspectives to select services. In addition, most of them use weighted summation functions to aggregate multiple QoS factors to rank services for selection. This paper presents a novel service selection method, in which users can represent their non-functional requirements using linguistic terms and use their personalized trade-off strategies for service selection. The method employs fuzzy propositions in conjunction with QoS and price information to compute satisfaction degree of individual non-functional requirements. Based on the personalized trade-off strategy of a user, it then computes the overall satisfaction degree using fuzzy connective operators. The overall satisfaction degree of services is then used to rank services with equivalent functionalities and top-ranked services are recommended. An application example is presented to show the methods effectiveness.

Security Requirements Analysis, Specification, Prioritization and Policy Development in Cyber-Physical Systems

In recent past, the security of cyber-physical systems (CPSs) has been the subject of major concern. One of the reasons is that, CPSs are often applied to mission-critical processes. Also, the automation CPSs bring in managing physical processes, and the detail of information available to them for carrying out their tasks, make securing them a prime importance. Securing CPSs is a difficult task as systems are interconnected. In order to achieve a continuous secured CPS environment, there is the need for an integrated methodology to analyze, specify and prioritize security requirements and also to develop policies to meet them. First, CPS assets are represented using high-order object models. Second, swim lane diagrams are extended to include malactivities and prevention or mitigation options to decompose use cases. We analyze security threats pertaining to the hardware components, software components and the hardware-software interaction. Security requirements are then specified, and an analytical prioritization approach, based on relative priority analysis is employed to prioritize them. Finally, security policies are then developed to meet the requirements. To demonstrate its effectiveness and evaluate its application, the proposed methodology is applied in a structured approach to a test bed - Ayushman, a Pervasive Health Monitoring System (PHMS).

Personal Preference and Trade-Off Based Additive Manufacturing Web Service Selection

The growing number of Additive Manufacturing Web (AMW) services, offered by different providers over the Internet, makes it challenging for consumers to compare these AMW services to select a service of their choice. In addition, it is even more challenging for consumers to compare these AMW services against their personal preferences. This is because, consumers personal preferences on multiple non-functional attributes such as price, material, accuracy and schedule, should be considered for AMW service selection. The decentralized nature of AMW services coupled by the need to consider consumers personal preferences during AMW service selection, requires a system that will serve as a broker between AMW services and consumers. In this paper, we propose a service broker system for AMW services that provides consumers with a single point of access to a large number of AMW services from many additive manufacturing service providers. This broker system also incorporates the first real application of service selection with fuzzy logic based personalized preferences and trade-off. We develop a method to generate fuzzy membership functions for each non-functional attribute. This makes it easy for consumers to specify their fuzzy membership functions. Finally, we present an application case study to demonstrate the feasibility of brokerage in AMW services and also evaluate our method in terms of performance.

A SOA Approach to Improve Performance of Metal Additive Manufacturing Simulation

Metal additive manufacturing (AM) processes are very complex and the process parameters required to fabricate quality parts can be very complicated and challenging to determine. For this reason, there is a continuous demand for AM simulations which can assist users to determine optimal process parameters in a timely manner. However, current commercial simulation packages are expensive and not designed with AM processes in mind, which makes it challenging to simulate an AM process. In addition, they are computationally intensive and usually require some high performance computing (HPC) system to run effectively. Besides, they take relatively more time, usually from several days to months, to simulate an AM process. This paper presents additive manufacturing simulator (AMS), a simulation tool built specifically with AM processes in mind, to address the challenges with current AM simulation tools. AMS is based on a three-tier client-server architecture, coupled with service oriented architectures (SOA) publication and subscription model. It has the ability to scale to utilize available computational resources and automatically balances computational resources among multiple processors. Compared with current AM simulation systems, AMS improves performance of AM simulation considerably and can run on not only HPC systems, but also low-cost desktop computers.