570 research outputs found
Collision-Free Sequential Task Offloading for Mobile Edge Computing
In this letter, a collision-free sequential task offloading scheme to multiple mobile-edge computing servers is proposed. The problem is formulated as a multi-objective optimization of latency and offloading failure probability. An exact solution technique is developed to obtain a benchmark optimal solution for the problem. A more computationally efficient heuristic algorithm is additionally developed, whose sub-optimal solution yields a performance close to optimal. Simulation results illustrate that the proposed offloading scheme can effectively reduce both latency and offloading failure probability.This work has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), as well as the UC3M Chair of Excellence and the Spanish National Project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE)
Task scheduling for mobile edge computing using genetic algorithm and conflict graphs
In this paper, we consider parallel and sequential
task offloading to multiple mobile edge computing servers. The
task consists of a set of inter-dependent sub-tasks, which are
scheduled to servers to minimize both offloading latency and
failure probability. Two algorithms are proposed to solve the
scheduling problem, which are based on genetic algorithm and
conflict graph models, respectively. Simulation results show that
these algorithms provide performance close to the optimal solution,
which is obtained through exhaustive search. Furthermore,
although parallel offloading uses orthogonal channels, results
demonstrate that the sequential offloading yields a reduced
offloading failure probability when compared to the parallel
offloading. On the other hand, parallel offloading provides less
latency. However, as the dependency among sub-tasks increases,
the latency gap between parallel and sequential schemes decreases.This work was supported in part by the Memorial University Chair, in part by
the Natural Sciences and Engineering Research Council of Canada (NSERC)
through its Discovery program, in part by the Chair of Excellence at UC3M, and
in part by the Spanish National Project TERESA-ADA (TEC2017-90093-C3-
2-R) (MINECO/AEI/FEDER, UE).Publicad
GPU Computing to Improve Game Engine Performance
Although the graphics processing unit (GPU) was originally designed to accelerate the image creation for output to display, today's general purpose GPU (GPGPU) computing offers unprecedented performance by offloading computing-intensive portions of the application to the GPGPU, while running the remainder of the code on the central processing unit (CPU). The highly parallel structure of a many core GPGPU can process large blocks of data faster using multithreaded concurrent processing. A game engine has many "components" and multithreading can be used to implement their parallelism. However, effective implementation of multithreading in a multicore processor has challenges, such as data and task parallelism. In this paper, we investigate the impact of using a GPGPU with a CPU to design high-performance game engines. First, we implement a separable convolution filter (heavily used in image processing) with the GPGPU. Then, we implement a multiobject interactive game console in an eight-core workstation using a multithreaded asynchronous model (MAM), a multithreaded synchronous model (MSM), and an MSM with data parallelism (MSMDP). According to the experimental results, speedup of about 61x and 5x is achieved due to GPGPU and MSMDP implementation, respectively. Therefore, GPGPU-assisted parallel computing has the potential to improve multithreaded game engine performance
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has
significantly advanced UAV-based applications for military, civil, and
commercial domains. Nevertheless, the challenges of establishing high-speed
communication links, flexible control strategies, and developing efficient
collaborative decision-making algorithms for a swarm of UAVs limit their
autonomy, robustness, and reliability. Thus, a growing focus has been witnessed
on collaborative communication to allow a swarm of UAVs to coordinate and
communicate autonomously for the cooperative completion of tasks in a short
time with improved efficiency and reliability. This work presents a
comprehensive review of collaborative communication in a multi-UAV system. We
thoroughly discuss the characteristics of intelligent UAVs and their
communication and control requirements for autonomous collaboration and
coordination. Moreover, we review various UAV collaboration tasks, summarize
the applications of UAV swarm networks for dense urban environments and present
the use case scenarios to highlight the current developments of UAV-based
applications in various domains. Finally, we identify several exciting future
research direction that needs attention for advancing the research in
collaborative UAVs
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
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