As an emerging network abstraction, coflow greatly improves the communication performance of data-parallel computing jobs. Many existing studies have focused on the design of coflow scheduling to minimize the completi...
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As an emerging network abstraction, coflow greatly improves the communication performance of data-parallel computing jobs. Many existing studies have focused on the design of coflow scheduling to minimize the completion time of jobs. However, they treat the underlying network as a large non-blocking switch without considering the constraints of network resources, which may increase network bottlenecks, reduce link capacity utilization, and extend job completion time. In this paper, we take network resource constraints into account and study how to schedule coflows in multi-stage jobs with the objective of minimizing the total weighted job completion time. We first formalize this multi-stage job scheduling problem as nonlinear programming and prove its NP-hardness. By introducing a priority order of jobs using a linear programming relaxation based approach, we propose a polynomial-time algorithm with a performance guarantee, which can achieve a constant approximation ratio in many typical scenarios. Simulation results based on a Facebook trace show that, compared with a state-of-the-art approach, our algorithm can shorten the average weighted job completion time by up to 48.46% and run faster by up to 19.12x.
There is an increasing trend in the use of wireless communication along with new traffic signal control (TSC) algorithms to leverage and accommodate connected and autonomous vehicles. However, this development has inc...
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There is an increasing trend in the use of wireless communication along with new traffic signal control (TSC) algorithms to leverage and accommodate connected and autonomous vehicles. However, this development has increased the potential for cyber-attacks on TSC that can undermine the benefits of these new algorithms. An advanced persistent adversary can learn the behavior of TSC algorithms and launch attacks to preferentially get green time and/or to create traffic congestion in one intersection which can spread to the entire network. In this paper, we consider backpressure-based (BP-based) TSC algorithms and compare their performance under two misinformation attacks 1) time spoofing attack in which vehicles alter their arrival times at the intersection and 2) ghost vehicle attack in which vehicles disconnect the wireless communication and thereby hide from the TSC. We show that these misinformation can influence the signal phases determined by BP-based TSC algorithms. We consider an adversary that determines a set of arriving vehicles to be attack vehicles from many candidate sets (attack strategies) in order to maximize the number of disrupted signal phases. We show that by formulating the problem as a 0/1 Knapsack problem, the adversary can explore the space of attack strategies and determine the optimal strategy that maximally compromises the performance in terms of average delay and fairness. We propose two protection algorithms, namely, auction-based (APA) and hybrid-based (HPA) algorithms and show that they are able to mitigate the impacts of the misinformation attacks.
Mobile communication networks have entered a new age by introducing fifth-generation technologies (5G). The International Union of Telecommunications (IUT) proposes new core innovations and capabilities for 5G network...
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Mobile communication networks have entered a new age by introducing fifth-generation technologies (5G). The International Union of Telecommunications (IUT) proposes new core innovations and capabilities for 5G networks to meet the growing need for mobile broadband services. The requirements set by 5G for the enhanced Mobile BroadBand (eMBB) use case seem contradictory. It intends to increase the data rate, afford efficient spectrum usage, provide an excellent fairness level to all users, and reduce buffer size. Accordingly, these needs should be met to perform the expected quality of service. Besides, the scheduling algorithms existing in the field respond separately to the criteria mentioned earlier. For all these reasons, we opted for a multi-objective problem formulation to take all these constraints into account. This paper presents a multi-criteria scheduler for 5G eMBB communications transmitting in a dense urban environment. Our proposed solution combines the weighted sum multi-objective optimization and the perceptron's weights management deployed in neural networks. Moreover, a comparison study was carried out to assess the performance of the suggested algorithm. The comparative analysis proves that the algorithm developed in this paper provides the best performances for the enhanced mobile broadband use case and the scenario adopted.
We offer task scheduling algorithms that are economical in terms of energy consumption for edge computing networks that are supported by the Internet of Things (IoT). The challenges of spectrum utilization and energy-...
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We offer task scheduling algorithms that are economical in terms of energy consumption for edge computing networks that are supported by the Internet of Things (IoT). The challenges of spectrum utilization and energy-efficient work scheduling that lead to novel design are not addressed in this study, despite the fact that it provides encouraging results for task offloading. There is a possibility that the larger homogeneous fog computing architecture will include all homogeneous nodes, in addition to additional spectrum for node-to-node and device-to-device communications and work scheduling. We create a fog computing architecture that is efficient in terms of energy consumption for edge computing networks that are supported by the Internet of Things. By utilizing this approach, userdevice nodes are able to collaborate while simultaneously reaping the benefits of diverse computing and network resources. In addition to this, we provide a solution to the problem of task scheduling that maximizes energy efficiency across all of the help nodes.
5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service pr...
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5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service.
One of the goals of 5G is to provide enhanced mobile broadband and enable low latency in some use cases. To achieve this aim, the Internet Engineering Task Force has proposed the Multipath TCP by utilizing the feature...
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One of the goals of 5G is to provide enhanced mobile broadband and enable low latency in some use cases. To achieve this aim, the Internet Engineering Task Force has proposed the Multipath TCP by utilizing the feature of dual connectivity in 5G, where a 5G device can be served by two different base stations. However, the path heterogeneity between the 5G device and the server may cause a packet out-of-order problem. The researchers proposed a number of scheduling algorithms to tackle this issue. This paper introduces the existing algorithms, with the aim to make a thorough comparison between the existing scheduling algorithms and provide the guidelines for designing new scheduling algorithms in 5G, we have conducted an extensive set of emulation studies based on the real Linux experimental platform. The evaluation covers a wide range of network scenarios to investigate the impact of different network metrics, namely, RTT, buffer size, and file size on the performance of existing widely deployed scheduling algorithms.
The problem of searching for hidden or missing objects (called targets) by autonomous intelligent robots in an unknown environment arises in many applications, e.g., searching for and rescuing lost people after disast...
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The problem of searching for hidden or missing objects (called targets) by autonomous intelligent robots in an unknown environment arises in many applications, e.g., searching for and rescuing lost people after disasters in high-rise buildings, searching for fire sources and hazardous materials, etc. Until the target is found, it may cause loss or damage whose extent depends on the location of the target and the search duration. The problem is to efficiently schedule the robot's moves so as to detect the target as soon as possible. The autonomous mobile robot has no operator on board, as it is guided and totally controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the robot which is used before an emergency evacuation of people after a disaster.
5G and Industrial Internet are bringing a variety of applications with on-time and reliable demands. Cyclic queuing and forwarding (CQF), a well-known mechanism defined by IEEE 802.1 Qch in Time Sensitive Network (TSN...
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ISBN:
(纸本)9781538674628
5G and Industrial Internet are bringing a variety of applications with on-time and reliable demands. Cyclic queuing and forwarding (CQF), a well-known mechanism defined by IEEE 802.1 Qch in Time Sensitive Network (TSN), achieves deterministic end-to-end latency and jitter without complex gating calculations. However, most of the current work ignores the prevalence of hybrid networks with different link rates, resulting in low bandwidth utilization and high latency for single-cycle CQF. In this paper, we propose a multi-cycle CQF to address the transmission in multi-link-rate networking, reducing deterministic end-to-end latency and improving link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi-link-rate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single-cycle CQF online scheduling algorithm in hierarchical multi-link-rate networking scenarios, and the evaluation shows that our algorithm achieves better end-to-end ultra-low latency (38.9% reduction) with a smaller schedulability gap compared with single-cycle CQF.
Time-sensitive networking (TSN) is a promising real-time network technology that has deterministic delay and jitter guarantee capabilities. However, TSN flow scheduling faces challenges in the multi-level topology due...
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Time-sensitive networking (TSN) is a promising real-time network technology that has deterministic delay and jitter guarantee capabilities. However, TSN flow scheduling faces challenges in the multi-level topology due to the cooperative scheduling requirements of multiple data flows, which are abstracted as TSN chain flows. In this paper, we present an efficient scheduling approach, namely TSN Chained Flow scheduling (TCFS), which is utilized to globally schedule chain flows in the multi-level topology. To obtain optimal TCFS scheduling results, we design the scheduling constraints and propose an offline algorithm to efficiently schedule chain flows in the multi-level topology. Based on offline scheduling results, we further propose a heuristic method that includes time-tabling and sequencing algorithms to improve the dynamic scheduling performance. Experimental results show that the TCFS approach efficiently improves the schedulability for chain flows, as compared with benchmarks.
Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting s...
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Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.
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