Resource Service Chain Research Articles

Composition of Resource-Service Chain Based on Evolutionary Algorithm in Distributed Cloud Manufacturing Systems

In distributed cloud manufacturing (CMfg) systems, multi-resource service can complete more complex manufacturing tasks than single resource service. Especially in business process, all the resource services are invoked in a certain sequence, which is called the Resource-Service Chain (RSC). The RSC, as a sequential composition of resource services, expresses the scheduling and the flow of servicing to a distributed business process. A perfect composition can improve utilization ratio and efficient matching availability of resource services greatly. However, most of the existing methods for resource service composition paid no attention to the temporal relationship between resource services. Moreover, the methods strongly depend on relevant element to be considered. Inspired by biological evolution, a Resource-Service Chain Composition Evolutionary (RSCCE) algorithm is proposed. Specifically, RSCCE tries to find multiple optimal solutions, namely all RSCs in a workflow with given constraints. To begin, initial sets of composite resource service are resolved by calculating the degree of dependency between resource services, so as to obtain initial RSCs by workflow. Then, RSCCE algorithm applies genetic algorithm to search for the extended of each initial RSC, a longer chain composing of it, to improve the reuse of RSC. Under this approach, gene and chromosome represent resource service and the entire RSC respectively. If the propagated chromosomes violate the sequence of resource service, as constraint in RSCCE algorithm, they will be repaired to obtain a valid solution. Finally, we take a multi-enterprise collaborative business process as an example to simulate our approach. Experimental results confirm the effectiveness of the approach.

Selection of resource service chain with conflict-free dependencies in cloud manufacturing systems

In cloud manufacturing (CMfg), to improve the efficiency of a whole business process, all distributed manufacturing resources services should be selected as 'service flows', called resource-service chain (RSC). However, selecting an RSC with conflict-free dependency is more difficult in collaborative manufacturing than in a centralised one, as resource services are usually selected independently by different organisations. To overcome this shortcoming, an algorithm based evolutionary algorithm is proposed. First, five common types of dependencies are defined after analysing the relationship among manufacturing resources. Then, genetic algorithm (GA) is applied to find the optimal set of RSCs, in which the dependencies among resource services are considered as constraint rules. Finally, a collaborative business process is taken as an instance to verify the selection of RSCs with conflict-free dependency. The results show that it can improve the efficiency of resource service selection.

Selection of resource service chain with conflict-free dependencies in cloud manufacturing systems

Selection of resource service chain with conflict-free dependencies in cloud manufacturing systems

Business Anomaly Detection Based on QoS Benchmark of Resource-service Chain in Collaborative Task

To detect business anomaly caused by resource services in collaborative task, an approach based on QoS benchmark of resource-service chain is presented. Firstly, taking QoS of resource services as feature, the QoS benchmarks are obtained by clustering the relationships between the features of resource services in resource service chains. Then, thresholds of QoS benchmarks are calculated. A dynamic calibration strategy to the threshold is used to perform online detection of business anomaly in global and local views of a collaborative task. Finally, experiments and case studies show that the proposed approach has a high accuracy rate for business anomaly detection.

Detecting a Business Anomaly Based on QoS Benchmarks of Resource-service Chains for Collaborative Tasks in the IoT

In the Internet of Things (IoT), the online performance of many online services is determined by their distribution resources, which are connected to many different devices. The expected performance of a resource service primarily depends on the optimal use of the service in satisfying end-to-end quality requirements to support its successful execution. Therefore, the performance of a resource service is dynamic and should be discovered as a benchmark to detect a performance anomaly online. A performance anomaly is referred to as a business anomaly because it depends on its usage. The performance is measured by the quality of service (QoS) that is possessed by a resource service. In this paper, an approach based on the resource service QoS is proposed to detect a business anomaly via mining business process data in collaborative tasks in the IoT. First, a resource-service chain (RSC) is considered to be an analysis object because resource services are employed as a “service flow” by a business process. The similarity between any two RSCs is measured according to the QoS indicator values of resource services. Based on the similarity, a clustering algorithm is presented to resolve clustering centers that are considered to be QoS benchmarks. Second, according to the QoS benchmarks of RSCs, the thresholds of QoS indicators of a business anomaly are determined. Third, an algorithm is presented to detect anomalies of the business process. Finally, the proposed approach is illustrated by a simulation experiment. The experimental results show that the approach can be used to effectively detect a business anomaly online.

Modelling of cross organizational manufacturing resource service chain based on service supply-demand dynamic matching network

In the cloud manufacturing systems, both manufacturing tasks and manufacturing services are in a dynamic environment. How could cloud manufacturing platform optimizes manufacturing cloud services based on QoS, matching an optimal service composition for manufacturing tasks has become an urgent problem at present. In view of this problem, we study the matching of manufacturing tasks and manufacturing services from the perspective of complex network theory. On the basis of manufacturing task network and manufacturing service network, a dynamic matching network theory model of manufacturing task-service is constructed. And then, we take a dynamic assessment of QoS. Finally, we use load and dynamic QoS as the optimization objectivities, transform the optimal manufacturing service composition problem into the shortest path problem, and the dynamic scheduling of manufacturing services is realized.

Selecting Key Feature Sequence of Resource Services in Industrial Internet of Things

The Industrial Internet of Things (IIoT) achieves reliable operation of physical resources through real-time perceiving. In a smart factory, it provides a new way to share manufacturing resources as services or to use resource services of other enterprises. However, selecting a sequence of resource services, called the resource service chain (RSC), for a collaborative business process is difficult because the correlated resource services are usually selected independently by different organizations. The features of resource services, as an important intrinsic factor, should be used to identify all the candidate RSCs. To serve the purpose, the key feature sequences are extracted from an RSC to identify the bottleneck that have a great influence on the business process. This problem is called the key feature sequence of IIoT resource service chain (KFSR-IIoT). The proposed approach, composed of several algorithms, is called the Algorithms for KFSR-IIoT (AKFSR-IIoT). First, resource service modeling is introduced to obtain all the RSCs associated with a workflow. Next, the degree of influence (DI), which is the extent to which a feature of an upstream resource service influences a feature of a downstream one in an RSC, is analyzed and defined. After normalization, the average DI of any two adjoining features is calculated; thus, the key feature sequences are resolved. In the proposed algorithms, the average DIs have the characteristics of a high value, low variation, and long duration. Then, based on the DIs of key feature sequences, an algorithm is presented to measure the synthetic DI of an RSC. Finally, the AKFSR-IIoT is tested with a nearly practical data set, and the results show that it is very promising.

Mining QoS benchmark of resource-service chain for collaborative tasks

To improve the efficiency and quality of resource services selection in collaborative task systems, the temporal relationship between the resource services should be taken into account. At the same time, the changing market requires a dynamic selection benchmark. Quality of service (QoS) as the main basis for the selection of resource services, mining the business data set to develop the QoS benchmark has a good objectivity and dynamic. Taking manufacturing as a typical collaborative process, based on the resource service chain (RSC) formed by the scheduling order of resource services, an approach called the mining method for QoS benchmark of RSC (QoSB-RSC) is proposed to obtain the QoS benchmark of RSC. By comparing the QoS similarity between scheduling instances of RSC, mining the cluster centres from business data set as the QoS benchmark. The performance of the QoSB-RSC is verified by experiment and case analysis, indicating that it has positive significance to improve the efficiency and quality of resource services selection.

Mining QoS benchmark of resource-service chain for collaborative tasks

Mining QoS benchmark of resource-service chain for collaborative tasks

Optimizing the Composition of a Resource Service Chain With Interorganizational Collaboration

In collaborative tasks, the composition of distributed resource services can improve the resource utilization. From the perspective of a business process, resource services should be composed as service flow so as to serve better a business process. However, most of the existing methods neglect an important factor, the characteristic of interorganization, which should be taken into consideration. An interorganizational resource service sequence is not necessarily available to all participating organizations in a collaborative task, as each organization seeks and selects resource services independently. Therefore, resource services in sequential order are called the resource service chain (RSC). This problem is called resource service chain composition with inter-organizational collaboration (RSCCOrg). The collaborative manufacturing is taken as an instance because it is a typical collaborative task. The proposed approach here is composed of several algorithms, called the algorithms for RSCCOrg (ARSCCOrg) and can better cope with interorganizational collaboration. To begin, a model based on a weighted directed graph is presented for the resource service temporal dependences. This is convenient for describing the temporal dependences among resource services and obtaining frequent RSCs. By calculating the frequencies between every two resource services from business data, the degrees of temporal dependence between them are resolved. A set of frequent RSCs can be obtained by choosing the higher weights. Next, an algorithm is presented to extend the frequent RSCs to increase the reusability and to improve the efficiency of selection. Then, a similarity formula is presented to compare the similarities between the frequent RSCs and the extended RSCs. In particular, the characteristic of interorganization is considered in the similarity comparison. The ARSCCOrg has been tested with a practical dataset, and the results show that it is very promising.

Composition of Resource-Service Chain for Cloud Manufacturing

In distributed manufacturing systems, manufacturing resource composition is one of the most important problems. This is because efficiency of resource selection and resource utilization can all be improved if it is tackled well. However, most of the existing methods neglect temporal relationship between resources. This leads to an inefficient use of resources, because all resources have to be kept available before a business process is started. A temporal composition of resources is more suitable, as it expresses the scheduling and the flow of servicing to a business process. Therefore, resource services invoked in sequential order are called the resource-service chain (RSC), in view that distributed resources are encapsulated into cloud services in a cloud manufacturing (CMfg) environment. We propose an approach, called RSC composition algorithm (RSCCA) that can better cope with the temporal relationship between the resource services in a business process. Specifically, a two-stage composition method based on the degrees of dependency between resource services in workflow is proposed. To begin, in the build-time stage algorithm, RSCCA resolves initial compositions based on task relatedness and temporal dependencies between resource services, and then calculates the usage frequencies of ICs by mining workflow log at workflow runtime stage. Based on this, RSCCA can compose individual resource services as more than sets, especially as chains, allowing flow directions and dynamics to be considered. RSCCA has been tested with different data sets and the results show that it can be very promising.

Semantic-supported and agent-based decentralized grid resource discovery

One of the open issues in grid computing is efficient resource discovery. In this paper, we propose a novel semantic-supported and agent-based decentralized grid resource discovery mechanism. Without overhead of negotiation, the algorithm allows individual resource agents to semantically interact with neighbour agents based on local knowledge and to dynamically form a resource service chain to complete a task. The algorithm ensures the resource agent’s ability to cooperate and coordinate on neighbour knowledge requisition for flexible problem solving. The developed algorithm is evaluated by investigating the relationship between the success probability of resource discovery and semantic similarity under different factors. The experiments show the algorithm could flexibly and dynamically discover resources and therefore provide a valuable addition to the field.