In task-oriented service computing, a user's computing goal is modeled and represented as a task, which is composed of activities that are performed by accessing service instances in a local environment. The abstract service requirements specified in an activity of a task are resolved and bound to service instances dynamically in runtime. When there are many candidate services that provide similar capabilities for a task, it is essential to consider quality of service (QoS) such as response time, latency, and availability to determine which service instances to use. Finding a service composition that meets the optimal level of quality is a well-known NP-hard problem-the time complexity for task-level (global) optimization increases exponentially as the number of services and the number of quality attributes increase. Although it is possible to use a heuristic approach that shows a reasonable response time with a certain level of service quality, this strategy often fails when there are hard QoS constraints that need to be considered in the task level. In this paper, to overcome this limitation, we propose an adaptive method of selecting services based on the hardness of QoS constraints. The basic idea is to sample services that represent a specific quality-value range. The quality-value range of candidate services is divided into smaller sub-ranges in which representative services are sampled and evaluated. At this time, the size of the QoS sub-ranges is determined adaptably based on the hardness of the QoS constraints. In a QoS sub-range, candidate services may have a similar QoS value for a quality attribute. We calculate the utility of candidate services in a QoS sub-range and sample the highest utility service. This process of sampling services and evaluating their utility value is repeated until it makes a composite service that has the highest level of global utility for a task. Our experiment results show that the proposed approach effectively improves the success rate of service composition while achieving a certain level of global optimality and maintaining a reasonable level of performance. Our approach shows up to 80 percent improvement in success rate in comparison to the existing heuristic approaches.

As emerging paradigms such as service-oriented computing and ubiquitous computing become combined, end-users are now being provided with a myriad of services to utilize smart objects to achieve their goals. The most promising standard to utilize these services is WS-BPEL, which employs centralized coordination for simpler management of interaction and synchronization. However, centralized approaches suffer from scalability and heterogeneity issues as well as inefficiency, especially when the system is managed across different entities. Moreover, it is quite unrealistic to assume that one client device has permissions to access all available operations of smart objects and can also support different types of required communication interfaces. Hence, in this paper, we propose a novel distributed coordination scheme that helps end-users collaborate more efficiently to achieve their common goals. Unlike traditional distributed coordination methods, which are limited to static environments, the proposed scheme incorporates dynamic ubiquitous computing environments where requirements of tasks and available resources can be altered throughout task execution. Under the proposed scheme, mobile client devices are able to self-collaborate without a dedicated central server by spontaneously electing a task coordinator among them. The proposed scheme also deals with dynamic events such as the joining and leaving of users, clients and tasks, and supporting dynamic reallocation while keeping them transparent for end-users. Finally, the proposed scheme is evaluated through simulations with different numbers of services and client devices, showing improved results in performance optimality, assignment efficiency, and dependency coverage of composition.

This paper describes a practical equation for estimating the fractal dimensions (FD) of images and discusses the recognition model for which it is applicable. The FD is applied to pre-estimate quantities of the information that can be used to recognize images.