A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication

The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners —This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.

Biobjective Task Scheduling for Distributed Green Data Centers

A Multi-Objective Deep Reinforcement Learning Framework

Cloud manufacturing promotes the transformation of intelligence for the traditional manufacturing mode. In a cloud manufacturing environment, the task scheduling plays an important role. However, as the number of problem instances increases, the solution quality and computation time always go against. Existing task scheduling algorithms can get local optimal solutions with the high computational cost, especially for large problem instances. To tackle this problem, a task scheduling algorithm based on a deep reinforcement learning architecture (RLTS) is proposed to dynamically schedule tasks with precedence relationship to cloud servers to minimize the task execution time. Meanwhile, the Deep‐Q‐Network, as a kind of deep reinforcement learning algorithms, is employed to consider the problem of complexity and high dimension. In the simulation, the performance of the proposed algorithm is compared with other four heuristic algorithms. The experimental results show that RLTS can be effective to solve the task scheduling in a cloud manufacturing environment.

Task scheduling based on deep reinforcement learning in a cloud manufacturing environment

A cyber-physical system (CPS) typically consists of the plant, sensors, actuators, the controller and a communication network. The communication network connects the individual components to achieve the computing and communication in the CPS. It also makes the CPS vulnerable to network attacks. How to deal with the network attacks in CPSs has become a research hotspot. This paper surveys the types of network attacks in CPSs, the intrusion detection methods and the attack defense strategies. The future research directions of CPSs network security are also presented.

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A Survey of Network Attacks on Cyber-Physical Systems

Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objective workflow scheduling approaches are still limited in many ways, e.g., encoding is restricted by prior experts' knowledge when handling a dynamic real-time problem, which strongly influences the performance of scheduling. In this paper, we apply a deep-Q-network model in a multi-agent reinforcement learning setting to guide the scheduling of multi-workflows over infrastructure-as-a-service clouds. To optimize multi-workflow completion time and user's cost, we consider a Markov game model, which takes the number of workflow applications and heterogeneous virtual machines as state input and the maximum completion time and cost as rewards. The game model is capable of seeking for correlated equilibrium between make-span and cost criteria without prior experts' knowledge and converges to the correlated equilibrium policy in a dynamic real-time environment. To validate our proposed approach, we conduct extensive case studies based on multiple well-known scientific workflow templates and Amazon EC2 cloud. The experimental results clearly suggest that our proposed approach outperforms traditional ones, e.g., non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, and game-theoretic-based greedy algorithms, in terms of optimality of scheduling plans generated.

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Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

GPS traces collected by IoT-enabled vehicle sensors are highly informative and exploitable in the area of human mobility analysis and traffic control. In this work, we focus on generating effective bus routes and networks by analyzing and exploiting the Taxi GPS trajectories. Specifically, we develop a novel framework for yielding potential bus stations by analyzing passenger boarding and getting spots collected by IoT-enabled vehicle sensors, a novel ant-colony-based heuristic for generating serving routes and further for yielding the bus network. For the model validation purpose, we perform extensive simulations upon a dataset of metropolitan traveling traces and illustrate that our approach definitely beats state-of-the-art ones in terms of the number of people served. 

Planning bus networks by exploiting GPS trajectories collected by IoT‐enabled vehicles

Abstract With the skyrocketing need for low-latency services on the Internet of Vehicles (IoV) and elastic cross-layer resource provisioning, multi-access edge computing (MEC) is considered a high-potent solution, which evolves from cloud and grid computing to meet the above needs in IoV scenarios. Instead of considering single-point and monolithic IoV tasks, in this paper, we consider the IoV applications to be with structural properties and the supporting environment to be with a hybrid cloud-edge architecture. We develop a scheduling method that offloads tasks to the eNode or cloud according to their estimations of latest starting time. Simulative results clearly demonstrate that our method beat existing solutions in terms of average completion time, average waiting time, and in-time completion rate. 

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A novel vehicular task deployment method in hybrid MEC

The Quality-of-Service (QoS) aspects of Web service has gained popularity in the field of service computing. QoS-oriented Web service composition is a distributed model to construct new web service on top of existing primitive or other composite web services with QoS guarantees. A major challenge in this field is that the QoS data of candidate services are with run-time fluctuations and thus difficult to predict. Traditional approaches in this direction tended to address the challenge by statistics, prediction and neural network-based models. A major limitation of these methods lies in that they ignore outliers data in the historical QoS data, in terms of inconsistencies, errors, shifts, corruptions, etc. In this work, instead, we consider outliers in QoS series to be non-neglectable, and propose an outlier-tolerable and predictive approach to service composition through leveraging a joint estimation-based outlier detection method and a niched genetic algorithm. To validate the effectiveness of our proposed method, we conduct extensive case studies based on different outlier conditions, and the experimental results show that our method is superior to existing ones. 

Hong Xie

Papers

Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

Planning bus networks by exploiting GPS trajectories collected by IoT‐enabled vehicles

A novel vehicular task deployment method in hybrid MEC

A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition