Yuning Chen

Bio: Yuning Chen is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Heuristic (computer science) & Satellite constellation. The author has an hindex of 2, co-authored 3 publications receiving 46 citations.

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

Abstract: Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.

Agile Earth Observation Satellite Scheduling Over 20 Years: Formulations, Methods, and Future Directions

Abstract: Agile satellites with advanced attitude maneuvering capability are the new generation of earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this article are, thus, to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multiobjective function and autonomous model. A detailed literature review from 1997–2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic, and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

The Time-dependent Electric Vehicle Routing Problem: Model and solution

Abstract: We study a new problem named the Time-dependent Electric Vehicle Routing Problem (TDEVRP) which involves routing a fleet of electric vehicles to serve a set of customers and determining the vehicle’s speed and departure time at each arc of the routes with the purpose of minimizing a cost function. We propose an integer linear programming (ILP) model to formulate the TDEVRP and show that the state-of-the-art commercial optimizer (CPLEX) can only solve instances of very limited sizes (with no more than 15 customers). We thus propose an iterated variable neighbourhood search (IVNS) algorithm to find near-optimal solutions for larger instances. The key ingredients of IVNS include a fast evaluation method that allows local search moves to be evaluated in constant time O ( 1 ) , a variable neighbourhood descent (VND) procedure to optimize the node sequences, and a departure time and speed optimization procedure(DSOP) to optimize the speed and departure time on each arc of the routes. The proposed algorithm demonstrates excellent performances on a set of newly created instances. In particular, it can achieve optimal or near-optimal solutions for all small-size instances (with no more than 15 customers) and is robust for large-size instances where the gap between the average and the best solution value is consistently lower than 2.38%. Additional experimental results on 40 benchmark instances of the closely related Time-Dependent Pollution Routing Problem indicate that the proposed IVNS algorithm also performs very well and even discovers 39 new best-known solutions (improved upper bounds).

Machine learning in telemetry data mining of space mission: basics, challenging and future directions

Abstract: The development of an intelligent artificial satellite health monitoring system is a key issue in aerospace engineering that determines satellite health status and failure using telemetry data. The modern design of data mining and machine learning technologies allows the use of satellite telemetry data and the mining of integrated information to produce an advanced health monitoring system. This paper reviews the current status and presents a framework of necessary processes on data mining to solving various problems in telemetry data such as error detection, prediction, summarization, and visualization of large quantities, and help them understand the health status of the satellite and detect the symptoms of anomalies. Machine learning technologies that include neural networks, fuzzy sets, rough sets, support vector machines, Naive Bayesian, swarm optimization, and deep learning are also presented. Also, this paper reviews a wide range of existing satellite health monitoring solutions and discusses them in the framework of remote data mining techniques. In addition, we are discussing the analysis of space debris flow analysis and the prediction of low earth orbit collision based on our orbital Petri nets model. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.