Serving at the Edge: An Edge Computing Service Architecture Based on ICN

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

Rescheduling of Distributed Manufacturing System with Machine Breakdowns

This study attempts to explore the dynamic scheduling problem from the perspective of operational research optimization. The goal is to propose a rescheduling framework for solving distributed manufacturing systems that consider random machine breakdowns as the production disruption. We establish a mathematical model that can better describe the scheduling of the distributed blocking flowshop. To realize the dynamic scheduling, we adopt an “event-driven” policy and propose a two-stage “predictive-reactive” method consisting of two steps: initial solution pre-generation and rescheduling. In the first stage, a global initial schedule is generated and considers only the deterministic problem, i.e., optimizing the maximum completion time of static distributed blocking flowshop scheduling problems. In the second stage, that is, after the breakdown occurs, the rescheduling mechanism is triggered to seek a new schedule so that both maximum completion time and the stability measure of the system can be optimized. At the breakdown node, the operations of each job are classified and a hybrid rescheduling strategy consisting of “right-shift repair + local reorder” is performed. For local reorder, we designed a discrete memetic algorithm, which embeds the differential evolution concept in its search framework. To test the effectiveness of DMA, comparisons with mainstream algorithms are conducted on instances with different scales. The statistical results show that the ARPDs obtained from DMA are improved by 88%.

Trade-Off between Task Accuracy, Task Completion Time and Naturalness for Direct Object Manipulation in Virtual Reality

Virtual reality devices are used for several application domains, such as medicine, entertainment, marketing and training. A handheld controller is the common interaction method for direct object manipulation in virtual reality environments. Using hands would be a straightforward way to directly manipulate objects in the virtual environment if hand-tracking technology were reliable enough. In recent comparison studies, hand-based systems compared unfavorably against the handheld controllers in task completion times and accuracy. In our controlled study, we compare these two interaction techniques with a new hybrid interaction technique which combines the controller tracking with hand gestures for a rigid object manipulation task. The results demonstrate that the hybrid interaction technique is the most preferred because it is intuitive, easy to use, fast, reliable and it provides haptic feedback resembling the real-world object grab. This suggests that there is a trade-off between naturalness, task accuracy and task completion time when using these direct manipulation interaction techniques, and participants prefer to use interaction techniques that provide a balance between these three factors.

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

An improved algorithm to minimize the total completion time in a two-machine no-wait flow-shop with uncertain setup times

Since scheduling literature has a wide range of uncertainties, it is crucial to take these into account when solving performance measure problems. Otherwise, the performance may severely be affected in a negative way. In this paper, an algorithm is proposed to minimize the total completion time (TCT) of a two-machine no-wait flowshop with uncertain setup times within lower and upper bounds. The results are compared to the best existing algorithm in scheduling literature: the programming language Python is used to generate random samples with respect to various distributions, and the TCT of the proposed algorithm is compared to that of the best existing one. Results reveal that the proposed one significantly outperforms the best one given in literature for all considered distributions. Specifically, the average percentage improvement of the proposed algorithm over the best existing one is over 90%. A test of hypothesis is conducted to further confirm the results.

An algorithm for a no-wait flowshop scheduling problem for minimizing total tardiness with a constraint on total completion time

We consider a no-wait m-machine flowshop scheduling problem which is common in different manufacturing industries such as steel, pharmaceutical, and chemical. The objective is to minimize total tardiness since it minimizes penalty costs and loss of customer goodwill. We also consider the performance measure of total completion time which is significant in environments where reducing holding cost is important. We consider both performance measures with the objective of minimizing total tardiness subject to the constraint that total completion time is bounded. Given that the problem is NP-hard, we propose an algorithm. We conduct extensive computational experiments to compare the performance of the proposed algorithm with those of three well performing benchmark algorithms in the literature. Computational results indicate that the proposed algorithm reduces the error of the best existing benchmark algorithm by 88% under the same CPU times. The results are confirmed by extensive statistical analysis. Specifically, ANOVA analysis is conducted to justify the difference between the performances of the algorithms, and a test of hypothesis is performed to justify that the proposed algorithm is significantly better than the best existing benchmark algorithm with a significance level of 0.01.