The increasing quantity of both multifunctional ground TT&C resources and satellites with diverse requirements impose great challenges to TT&C resources scheduling. For this situation, the problem of how to sc...
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ISBN:
(数字)9781728190457
ISBN:
(纸本)9781728190464
The increasing quantity of both multifunctional ground TT&C resources and satellites with diverse requirements impose great challenges to TT&C resources scheduling. For this situation, the problem of how to schedule the use of TT&C resources from the prospective of satellite mission requirement is studied. Firstly, the characteristic of the satellite mission requirement is analyzed and a unified ability vector for TT&C and data transmission TT&C resources is proposed. Then three heuristic factors including visible windows crowing degree, matching degree and satellite preference deviation are introduced. Finally, in the view of satellite mission decomposition rather than independent and fragmented satellite visible windows, the characteristic-based TT&C resources scheduling algorithm is established. Simulation results show that this method is feasible in meeting satellite on-orbit mission requirements.
In cellular networks, the Shortest Remaining Processing Time first (SRPT) principle, which is known to be optimal for sharing jobs on a single-server system, can deliver significant advantages over conventional schedu...
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ISBN:
(纸本)9781479958641
In cellular networks, the Shortest Remaining Processing Time first (SRPT) principle, which is known to be optimal for sharing jobs on a single-server system, can deliver significant advantages over conventional scheduling algorithms. Transmissions are finished earlier, which improves user experience and enables operators to allow more users in their networks. However, this comes at the cost of reduced cell throughput and a penalty for large objects in peak traffic situations. We therefore propose an extension to SRPT that combines it with opportunistic scheduling and allows to tradeoff the advantages of both sides. We evaluate and compare the performance of SRPT and conventional schedulers by simulation with a realistic traffic scenario. An important metric is the transmission duration of application layer objects as it is central for the users' Quality of Experience (QoE). The results show that the proposed scheduler provides the superior QoE of SRPT for short, interactive transmissions while keeping the rate reduction for larger traffic objects at an acceptable level.
For a set of periodic real-time tasks running on a multi-processor system, some tasks need access to shared resources, while the remaining tasks do not. This article aims to solve the problem of priority inversion cau...
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ISBN:
(纸本)9781665442084
For a set of periodic real-time tasks running on a multi-processor system, some tasks need access to shared resources, while the remaining tasks do not. This article aims to solve the problem of priority inversion caused by simultaneous access to shared resources by tasks in a multi-processor real-time system. We propose a task allocation model and partition scheduling algorithm based on the MSRP protocol, which is called SASR-MSRP. Firstly, the algorithm divides the task set into two categories based on whether the task accesses shared resources or not. Secondly, calculate the system utilization rate U of the task that accesses the shared resource and determine the execution priority of the task according to its non-increasing order and assign it to the corresponding processor. Finally, we use the EDF scheduling algorithm to sequentially allocate the remaining independent tasks to the idle time period of the application processor. This algorithm not only reduces the problem of priority inversion, but also improves the overall scheduling efficiency of the system.
For multi-user satellite Internet of Things (IoT) systems operating at lower signal-to-noise ratio, spread spectrum techniques are usually used to combat narrowband interference. In addition, the communication perform...
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For multi-user satellite Internet of Things (IoT) systems operating at lower signal-to-noise ratio, spread spectrum techniques are usually used to combat narrowband interference. In addition, the communication performance in the spread spectrum system depends on the anti-jamming ability of the spreading codes (SCs). Therefore, how to design the SCs scheduling strategies under users' requirements and resource constraints has become a crucial problem for satellite IoT systems. In this paper, communication rewards and scheduling delays are introduced as gauges to measure the scheduling performance of the satellite gateway station control center (SGSCC). Specifically, SGSCC must efficiently and effectively allocate limited available SCs over terminal gateways under request at each transmission time slot. The SCs scheduling problem is formulated as a Markov Decision Process (MDP) along with the observed environments composed of resource status and user request status. Then a deep reinforcement learning scheduling algorithm is devised by embedding the idea of Long Short-Term Memory (LSTM) in the standard Double Deep Q-learning (DDQN). Simulation results show that the proposed algorithm can achieve much better performance than traditional algorithms in terms of communication rewards and scheduling delays. Finally, we draw some conclusions.
When ground base stations are not available in the aftermath of a disaster, unmanned aerial vehicle (UAV) acting as flying relay is a promising option. The UAVs with limited energy as flying relays allow for wider dat...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
When ground base stations are not available in the aftermath of a disaster, unmanned aerial vehicle (UAV) acting as flying relay is a promising option. The UAVs with limited energy as flying relays allow for wider data coverage and more stable data transmission. However, with the changes of ground devices topology and channel, it is challenging to consider quality of service (QoS) and the age of information (AoI) in UAV communication under the energy constraint. In this paper, we propose a dynamic priority packet scheduling for UAV assisted AoI-aware network whose utility is maximized subject to QoS to get the best tradeoff of the energy consumption and the weighted AoI. Specifically, the dynamics of devices are characterized by Gauss-Markov mobility model. Dynamic priority is affected by devices' movement, channel changes and others. We optimize the trajectory of the UAV and the scheduling scheme of the packets by the Dueling Double Deep Q Network (D3QN) algorithm. Simulations show that the scheme significantly improves the utility of the system compared to the benchmarks.
In this paper, we investigate the forward packet scheduling of a broadband GEO multibeam satellite system that aggressively reuses the user link frequency resources, and thus considers the implementation of interferen...
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In this paper, we investigate the forward packet scheduling of a broadband GEO multibeam satellite system that aggressively reuses the user link frequency resources, and thus considers the implementation of interference mitigation techniques at the transmitter side to exploit the multi-antenna multiplexing via precoding. The scheduling and the precoding design are closely coupled with each other, making it very challenging to provide a joint optimal solution that can be implemented in practical systems. On the other hand, future broadband satellite systems have to be capable of accommodating heterogeneous services and guarantee their corresponding uneven Quality of Service (QoS) requirements. As a consequence, we propose a novel cross-layer scheduling algorithm which takes into account the physical layer framing together with the modulator and precoding functionality combined with system constraints imposed by QoS requirements in upper layers. The proposed design is compared and validated using numerical results considering a realistic multibeam satellite system.
In the current world of big data analytics and huge processing request from the client server system the service requirement from the computing cores has been growing exponentially. In this paper we proposed a novel i...
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In the current world of big data analytics and huge processing request from the client server system the service requirement from the computing cores has been growing exponentially. In this paper we proposed a novel idea wherein the agent will check the ready queue length dynamically and based on the threshold value comparison further scheduling has been done. There are three different variants of scheduling and load balancing are identified with respect to the queue length. Always the queue length is directly proportional to the number of tasks given to a particular node in the distributed system. Compared to the traditional scheduling algorithms, our approach increases the cores' throughput by 2.5%. In addition, we calculated that as compared to the preceding approaches, the typical hold time for work in the ready state has decreased to 1.25%. The primary benefit of this endeavor is when multiple jobs are submitted to the server then the proposed algorithm speed up the process of getting the jobs executed at a faster rate.
We present an adaptation of the dual priority scheduling algorithm to schedule both hard real-time periodic tasks and soft-aperiodic tasks in shared memory multiprocessor systems. The goal is to achieve low mean aperi...
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In this paper, a speedup event scheduling algorithm is proposed to benefit CPU caching for large scale networks simulations. A digest queue over a ring-typed array is introduced to classify discrete event list. The an...
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With the development of the internet, the number of malicious programs rises observably, which has become one of the main threats to national basic network, information system and so on. Besides, the analysis of malic...
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