Quanjun Yin

Scheduling parallel jobs with tentative runs and consolidation in the cloud

We introduce a priority-based consolidation method for parallel jobs.We introduce an easy-to-implementation technique for tentative runs of jobs.We devise a scheduling algorithm using the above two techniques.We employ extensive experiments to evaluate the proposed algorithm. Since the success of cloud computing, more and more high performance computing parallel applications run in the cloud. Carefully scheduling parallel jobs is essential for cloud providers to maintain their quality of service. Existing parallel job scheduling mechanisms do not take the parallel workload consolidation into account to improve the scheduling performance. In this paper, after introducing a prioritized two-tier virtual machines architecture for parallel workload consolidation, we propose a consolidation-based parallel job scheduling algorithm. The algorithm employs tentative run and workload consolidation under such a two-tier virtual machines architecture to enhance the popular FCFS algorithm. Extensive experiments on well-known traces show that our algorithm significantly outperforms FCFS, and it can even produce comparable performance to the runtime-estimation-based EASY algorithm, though it does not require users to provide runtime estimation of the job. Moreover, our algorithm allows inaccurate CPU usage estimation and only requires trivial modification on FCFS. It is effective and robust for scheduling parallel workload in the cloud.

Dynamic obstacle-avoiding path planning for robots based on modified potential field method

The potential field method is widely used for autonomous robots due to its simplicity and high efficiency in dynamic motion planning. However, there is still drawback of unnecessary obstacle avoidance of former methods in dynamic obstacle avoidance planning. This paper proposes a new potential field method to solve the problem, whose new virtual force is deduced through introducing the restriction of collision angle with exponential form and both the information of angle and magnitude of relative velocity. The simulation results prove that the robot can not only avoid their obstacles and move to the target safely and quickly in dynamic environments, but remove largely the unnecessary obstacle avoidance by using the proposed method.

Formation Control of Robotic Swarm Using Bounded Artificial Forces

Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions.

A Semi-Markov Decision Model for Recognizing the Destination of a Maneuvering Agent in Real Time Strategy Games

Recognizing destinations of a maneuvering agent is important in real time strategy games. Because finding path in an uncertain environment is essentially a sequential decision problem, we can model the maneuvering process by the Markov decision process (MDP). However, the MDP does not define an action duration. In this paper, we propose a novel semi-Markov decision model (SMDM). In the SMDM, the destination is regarded as a hidden state, which affects selection of an action; the action is affiliated with a duration variable, which indicates whether the action is completed. We also exploit a Rao-Blackwellised particle filter (RBPF) for inference under the dynamic Bayesian network structure of the SMDM. In experiments, we simulate agents’ maneuvering in a combat field and employ agents’ traces to evaluate the performance of our method. The results show that the SMDM outperforms another extension of the MDP in terms of precision, recall, and -measure. Destinations are recognized efficiently by our method no matter whether they are changed or not. Additionally, the RBPF infer destinations with smaller variance and less time than the SPF. The average failure rates of the RBPF are lower when the number of particles is not enough.

A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition

Intention recognition is significant in many applications. In this paper, we focus on team intention recognition, which identifies the intention of each team member and the team working mode. To model the team intention as well as the world state and observation, we propose a Logical Hierarchical Hidden Semi-Markov Model (LHHSMM), which has advantages of conducting statistical relational learning and can present a complex mission hierarchically. Additionally, the LHHSMM explicitly models the duration of team working mode, the intention termination, and relations between the world state and observation. A Logical Particle Filter (LPF) algorithm is also designed to infer team intentions modeled by the LHHSMM. In experiments, we simulate agents’ movements in a combat field and employ agents’ traces to evaluate performances of the LHHSMM and LPF. The results indicate that the team working mode and the target of each agent can be effectively recognized by our methods. When intentions are interrupted within a high probability, the LHHSMM outperforms a modified logical hierarchical hidden Markov model in terms of precision, recall, and -measure. By comparing performances of LHHSMMs with different duration distributions, we prove that the explicit duration modeling of the working mode is effective in team intention recognition.

Multi-agent intention recognition using logical hidden semi-Markov models

Intention recognition (IR) is significant for creating humanlike and intellectual agents in simulation systems. Previous widely used probabilistic graphical methods such as hidden Markov models (HMMs) cannot handle unstructural data, so logical hidden Markov models (LHMMs) are proposed by combining HMMs and first order logic. Logical hidden semi-Markov models (LHSMMs) further extend LHMMs by modeling duration of hidden states explicitly and relax the Markov assumption. In this paper, LHSMMs are used in multi-agent intention recognition (MAIR), which identifies not only intentions of every agent but also working modes of the team considering cooperation. Logical predicates and connectives are used to present the working mode; conditional transition probabilities and changeable instances alphabet depending on available observations are introduced; and inference process based on the logical forward algorithm with duration is given. A simple game “Killing monsters” is also designed to evaluate the performance of LHSMMs with its graphical representation depicted to describe activities in the game. The simulation results show that, LHSMMs can get reliable results of recognizing working modes and smoother probability curves than LHMMs. Our models can even recognize destinations of the agent in advance by making use of the cooperation information.

Activity recognition using logical hidden semi-Markov models

Activity recognition is challenging and valuable in both real and virtual world. As important directed graphical models, hidden Markov models and their extensions are widely used to solve probabilistic activity recognition problems. In this paper, logical hidden semi-Markov models (LHSMMs) which combine logical hidden Markov models (LHMMs), a statistical relational learning method, and hidden semi-Markov models are proposed, and the lognormal distribution is used to model the duration explicitly. The formal description of LHSMMs and the exact inference process using a logical forward algorithm with duration are presented; the directed graphical representation of unmanned aerial vehicle activities is also given. Experiments are also designed to compare the performances of LHSMMs and LHMMs. The results prove that, the recognition result of abstract states using LHSMMs is more smoothing, and the probability of the real instantiated activity is larger than that of LHMMs in most time because of modeling duration explicitly.

A method for UAVs detection task planning of multiple starting points

UAV detection task planning has been significantly applied in many domains. As a famous optimizing algorithm, the ant colony algorithm (ACA) has a good performance to solve problem of this kind because of its positive feedback characteristic. The problem of the standard ACA is that only one starting point is considered when ACA is used in UAV detection task planning. In allusion to the UAV detection task planning problem of multiple starting points, a multi-start ant colony algorithm (MS-ACA) is proposed, deferent ant colonies are allocated to every UAV starting point. To lead the searching direction, the target is marked by a shared tabu table and a shared decision table. The results of experiment prove that the MS-ACA is able to find an optimized deploying and scheduling policy of UAVs, which conforms to the scenario requirement, and is with preferable adaptation.

A Scalable GVT Estimation Algorithm for PDES: Using Lower Bound of Event-Bulk-Time

Global Virtual Time computation of Parallel Discrete Event Simulation is crucial for conducting fossil collection and detecting the termination of simulation. The triggering condition of GVT computation in typical approaches is generally based on the wall-clock time or logical time intervals. However, the GVT value depends on the timestamps of events rather than the wall-clock time or logical time intervals. Therefore, it is difficult for the existing approaches to select appropriate time intervals to compute the GVT value. In this study, we propose a scalable GVT estimation algorithm based on Lower Bound of Event-Bulk-Time, which triggers the computation of the GVT value according to the number of processed events. In order to calculate the number of transient messages, our algorithm employs Event-Bulk to record the messages sent and received by Logical Processes. To eliminate the performance bottleneck, we adopt an overlapping computation approach to distribute the workload of GVT computation to all worker-threads. We compare our algorithm with the fast asynchronous GVT algorithm using PHOLD benchmark on the shared memory machine. Experimental results indicate that our algorithm has a light overhead and shows higher speedup and accuracy of GVT computation than the fast asynchronous GVT algorithm.

Incremental Construction of Generalized Voronoi Diagrams on Pointerless Quadtrees

In robotics, Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent the spatial topologies of their surrounding area. In this paper we consider the problem of constructing GVDs on discrete environments. Several algorithms that solve this problem exist in the literature, notably the Brushfire algorithm and its improved versions which possess local repair mechanism. However, when the area to be processed is very large or is of high resolution, the size of the metric matrices used by these algorithms to compute GVDs can be prohibitive. To address this issue, we propose an improvement on the current algorithms, using pointerless quadtrees in place of metric matrices to compute and maintain GVDs. Beyond the construction and reconstruction of a GVD, our algorithm further provides a method to approximate roadmaps in multiple granularities from the quadtree based GVD. Simulation tests in representative scenarios demonstrate that, compared with the current algorithms, our algorithm generally makes an order of magnitude improvement regarding memory cost when the area is larger than . We also demonstrate the usefulness of the approximated roadmaps for coarse-to-fine pathfinding tasks.