An improved genetic algorithm for cost-effective data-intensive service composition

The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for data-intensive service provision is proposed, 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services, 3) Finally, a modified genetic algorithm-based approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm

Aggregate node placement for maximizing network lifetime in sensor networks

Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, given K aggregate nodes and a dense sensor network consisting of n sensors with K ≪ n, the problem is to place the K aggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP-hard. In this paper, we first give a non-linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema - equal distance placement schema significantly

A Scalable and Adaptive Distributed Service Discovery Mechanism in SOC Environments

Abstract. Current researches on service discovery mainly pursue fast response and high recall, but little work focuses on scalability and adaptability of largescale distributed service registries in SOC. This paper proposes a solution using an agent based distributed service discovery mechanism. Firstly an unstructured P2P based registry system is proposed in which each peer is an autonomous registry center and services are organized and managed according to domain ontology within these registry centers. Secondly, an ant-like multi-agent service discovery method is proposed. Search agents and guide agents cooperate to discover services. Search agents simulate the behaviors of ants to travel the network and discover services. Guide agents are responsible to manage a service routing table consisting of pheromone and hop count, instructing search agents' routing. Experimental results show that the suggested mechanism is scalable and adaptive in a large-scale dynamic SOC environment

Feasibility study of temporary permanent pacemaker in patients with conduction block after TAVR

BackgroundLimited data exist on the use of temporary permanent pacemaker (TPPM) to reduce unnecessary PPM in patients with high-degree atrioventricular block (HAVB) after transcatheter aortic valve replacement (TAVR).ObjectivesThis study aims to determine the feasibility of TPPM in patients with HAVB after TAVR to provide prolonged pacing as a bridge.Materials and methodsOne hundred and eleven consecutive patients undergoing TAVR were screened from August 2021 to June 2022. Patients with HAVB eligible for PPM were included. TPPM were used in these patients instead of conventional temporary pacing or early PPM. Patients were followed up for 1 month. Holter and pacemaker interrogation were used to determine whether to implant PPM.ResultsTwenty one patients met the inclusion criteria for TPPM, of which 14 patients were third-degree AVB, 1 patient was second-degree AVB, 6 patients were first degree AVB with PR interval > 240 ms and LBBB with QRS duration > 150 ms. TPPM were placed on the 21 patients for 35 ± 7 days. Among 15 patients with HAVB, 26.7% of them (n = 4) recovered to sinus rhythm; 46.7% (n = 7) recovered to sinus rhythm with bundle branch block. The remains of 26.7% patients (n = 4) still had third-degree AVB and received PPM. For patients with first-degree AVB and LBBB, PR interval shortened to < 200 ms in all 6 patients and LBBB recovered in 2 patients. TPPM were successfully removed from all patients and no procedure-related adverse events occurred.ConclusionTPPM is reliable and safe in the small sample of patients with conduction block after TAVR to provide certain buffer time to distinguish whether a PPM is necessary. Future studies with larger sample are needed for further validation of the current results

Evaluations of heuristic algorithms for teamwork-enhanced task allocation in mobile cloud-based learning

Enhancing teamwork performance is a significant issue in mobile cloud-based learning. We introduce a service oriented system, Teamwork as a Service (TaaS), to realize a new approach for enhancing teamwork performance in the mobile cloud environment. To coordinate most learners' talents and give them more motivation, an appropriate task allocation is necessary. Utilizing the Kolb's learning style (KLS) to refine learner's capabilities, and combining their preferences and tasks' difficulties, we formally describe this problem as a constraint optimization model. Two heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA), are employed to tackle the teamwork-enhanced task allocation, and their performances are compared respectively. Having faster running speed, the SA is recommended to be adopted in the real implementation of TaaS and future development

Bio-inspired cost-aware optimization for data-intensive service provision

The use of Big Data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss single-objective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics

Facilitating an ant colony algorithm for multi-objective data-intensive service provisions

The explosion of enormous sources of digital data has led to greater dependence on data-intensive services. Applications based on data-intensive services have become one of the most challenging applications in cloud computing. The service provision, and in particular service composition, will face new challenges as the services and data grow. In this paper, we will evaluate an ant colony system to resolve the multi-objective data-intensive service composition problem. The algorithm for a multi-objective context will get a set of Pareto-optimal solutions considering two objectives at the same time: the total cost and the total execution time of a composite service

Enhancing the privacy of e-learning systems with alias and anonymity

The privacy of e-learning systems has become a big concern. This paper proposes a new approach, which can effectively protect the privacy of e-learning users. We propose to use the alias and anonymity to implement the privacy preservation for e-learning systems. Through the approach, a unique alias represents the real e-learning user to communicate with each other or e-learning users use the anonymity to hide their information. This approach can be very simple and efficient to be implemented in the e-learning system by well designed meta-formats of digital identities of four types of e-learning users. The anonymity can be adopted by all e-learning users to keep their privacy