Chip Multiprocessors (CMPs) leverage multiple processing units to improve computational speed and efficiency. routing algorithms in NoC (Network-on-Chip) architectures ensure efficient data communication between these...
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Network-on-Chip (NoC) has become a cost-effective communication interconnect for Tiled Chip Multicore Processor systems. The communication between cores is done through packet exchange. As the computational intensity ...
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Network-on-Chip (NoC) has become a cost-effective communication interconnect for Tiled Chip Multicore Processor systems. The communication between cores is done through packet exchange. As the computational intensity of applications increases, the amount of packet exchange between cores will also increase. The improper routing of these packets will result in high congestion thereby degrading the system performance. This marks the need for congestion-aware routing in NoC. In the real world, the applications running in NoC create diverse traffic, which in turn creates challenges in routing. Such challenges have resulted in more researchers relying on machine learning algorithms to tackle them. However, the issues pertaining to storage overhead and packet latency prevail in such methodologies. This paper presents an adaptive routing algorithm DeepNR, which uses a deep reinforcement learning approach. The proposed approach considers network information for state representation, routing directions for actions, and queuing delay for reward function. Experiments carried out on synthetic as well as real-time traffics to demonstrate the effectiveness and efficiency of DeepNR using the Gem5 simulator. The results obtained for DeepNR indicate a reduction of up to 21.25% and 44% in overall packet latency under high traffic conditions on real and synthetic traffic respectively, as compared to the existing approaches. Also, DeepNR achieves a throughput of above 90% in both the traffic scenarios.
Communication networks are difficult to model and predict because they have become very sophisticated and dynamic. We develop a reinforcement learning routing algorithm (RL-routing) to solve a traffic engineering (TE)...
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Communication networks are difficult to model and predict because they have become very sophisticated and dynamic. We develop a reinforcement learning routing algorithm (RL-routing) to solve a traffic engineering (TE) problem of SDN in terms of throughput and delay. RL-routing solves the TE problem via experience, instead of building an accurate mathematical model. We consider comprehensive network information for state representation and use one-to-many network configuration for routing choices. Our reward function, which uses network throughput and delay, is adjustable for optimizing either upward or downward network throughput. After appropriate training, the agent learns a policy that predicts future behavior of the underlying network and suggests better routing paths between switches. The simulation results show that RL-routing obtains higher rewards and enables a host to transfer a large file faster than Open Shortest Path First (OSPF) and Least Loaded (LL) routing algorithms on various network topologies. For example, on the NSFNet topology, the sum of rewards obtained by RL-routing is 119.30, whereas those of OSPF and LL are 106.59 and 74.76, respectively. The average transmission time for a 40GB file using RL-routing is 25.2 s. Those of OSPF and LL are 63 s and 53.4 s, respectively.
Network on chip(NoC)is an infrastructure providing a communication platform to multiprocessor ***,the wormhole-switching method,which shares resources,was used to increase its efficiency;however,this can lead to ***,d...
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Network on chip(NoC)is an infrastructure providing a communication platform to multiprocessor ***,the wormhole-switching method,which shares resources,was used to increase its efficiency;however,this can lead to ***,dealing with this congestion consumes more energy and correspondingly leads to increase in power ***,consuming more power results in more heat and increases thermal fluctuations that lessen the life span of the infrastructures and,more importantly,the network’s *** these complications,providing a method that controls congestion is a significant design *** this paper,a fuzzy logic congestion control routing algorithm is presented to enhance the NoC’s performance when facing *** avoid congestion,the proposed algorithm employs the occupied input buffer and the total occupied buffers of the neighboring nodes along with the maximum possible path diversity with minimal path length from instant neighbors to the destination as the selection *** enhance the path selection function,the uncertainty of the fuzzy logic algorithm is *** a result,the average delay,power consumption,and maximum delay are reduced by 14.88%,7.98%,and 19.39%,***,the proposed method enhances the throughput and the total number of packets received by 14.9%and 11.59%,*** show the significance,the proposed algorithm is examined using transpose traffic patterns,and the average delay is improved by 15.3%.The average delay is reduced by 3.8%in TMPEG-4(treble MPEG-4),36.6%in QPIP(quadruplicate PIP),and 20.9%in TVOPD(treble VOPD).
Energy saving becomes a central issue in the design of wireless sensor network routing algorithms. In the wireless sensor networks (WSNs), when intra-network communication is ensured, the lifetime of node can be exten...
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Energy saving becomes a central issue in the design of wireless sensor network routing algorithms. In the wireless sensor networks (WSNs), when intra-network communication is ensured, the lifetime of node can be extended by reducing data transmission or data volume as much as possible. However, the problem is that energy of the nodes around the sink node becomes exhausted quickly due to excessive communication overhead. To handle this problem, in this study, we propose a routing algorithm based on the sink node path optimisation. The study uses the energy consumption model as a constraint, transforms the time optimisation problem into an optimisation model, optimises the sink node path with the aid of simulated annealing (SA) algorithm, and uses data fusion to reduce the intra-network redundant data in the time domain. The proposed algorithm innovatively self-adjusts the path of sink node that is optimised by SA using new fitness function. Comprehensive simulation results show that the proposed algorithm can reduce the node energy consumption of waiting of sink node at the address of sink node, balance the network load and improve survival time of WSNs by 30% in comparison with results produced with the state-of-the art algorithms REAC-IN and DALMDT.
Flying ad hoc networks (FANETs) that consist of multiple unmanned aerial vehicles (UAVs) have developed owing to the rapid technological evolution of electronics, sensors, and communication technologies. In this paper...
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Flying ad hoc networks (FANETs) that consist of multiple unmanned aerial vehicles (UAVs) have developed owing to the rapid technological evolution of electronics, sensors, and communication technologies. In this paper, we propose a multi-objective routing algorithm for FANETs. In addition to the basic transmission performance in the construction of the routing path, the network impact according to the mobility of the UAV nodes and the energy state of each node should be considered because of the characteristics of the FANET, and the overall efficiency and safety of the network should be satisfied. We therefore propose the use of Q-learning-based fuzzy logic for the FANET routing protocol. The proposed algorithm facilitates the selection of the routing paths to be processed in terms of link and overall path performances. The optimal routing path to the destination is determined by each UAV using a fuzzy system with link- and path-level parameters. The link-level parameters include the transmission rate, energy state, and flight status between neighbor UAVs, while the path-level parameters include the hop count and successful packet delivery time. The path-level parameters are dynamically updated by the reinforcement learning method. In the simulation results, we compared the proposal with the conventional fuzzy logic and Q-value-based ad hoc on-demand distance vector. The results show that the proposed method can maintain low hop count and energy consumption and prolong the network lifetime.
作者：
Kai, CuiNanjing Univ
Informat Technol Serv Ctr Nanjing 210093 Jiangsu Peoples R China
Under the influence of COVID-19, an efficient Ad-hoc network routing algorithm is required in the process of epidemic prevention and control. Artificial neural network has become an effective method to solve large-sca...
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Under the influence of COVID-19, an efficient Ad-hoc network routing algorithm is required in the process of epidemic prevention and control. Artificial neural network has become an effective method to solve large-scale optimization problems. It has been proved that the appropriate neural network can get the exact solution of the problem in real time. Based on the continuous Hopfield neural network (CHNN), this paper focuses on the study of the best algorithm path for QoS routing in Ad-hoc networks. In this paper, a new Hopfield neural network model is proposed to solve the minimum cost problem in Ad-hoc networks with time delay. In the improved version of the path algorithm, the relationship between the parameters of the energy function is provided, and it is proved that the feasible solution of the network belongs to the category of progressive stability by properly selecting the parameters. The calculation example shows that the solution is not affected by the initial value, and the global optimal solution can always be obtained. The algorithm is very effective in the prevention and control in COVID-19 epidemic.
The main requirement to make safer journey in VANET environment is minimum delay with high packet delivery rate. This ensures that all data packets are received with minimal delay to prevent any accident. This paper p...
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The main requirement to make safer journey in VANET environment is minimum delay with high packet delivery rate. This ensures that all data packets are received with minimal delay to prevent any accident. This paper presents a new algorithm for VANET class routing protocol that covers sparse and coarse region of vehicles. It takes the advantage of road layout to improve the performance of routing in VANETs. The proposed algorithm uses real-time GPS tracking system to obtain traffic information for creating road based paths from source node to destination node. The optimize forwarding is used to figure out the forwarding node along the road pattern that form the path to deliver the data packets. The results shows that proposed algorithm obtain better results considering the various simulation parameters.
In this paper, a CAODV-based routing approach is proposed which uses multi-channel and multi-path forwarding techniques to deal with the time-varying activities of PUs. We also benefited from a suitable channel select...
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In this paper, a CAODV-based routing approach is proposed which uses multi-channel and multi-path forwarding techniques to deal with the time-varying activities of PUs. We also benefited from a suitable channel selection strategy with the goal of increasing throughput. Our method allocates interference-free channels and, if data is entered or activated, each node will select a path. The proposed routing mechanism, in turn, considers the relay loading, and the interference of the common channel in the primary and secondary nodes. We use the Lyapunov optimization queuing model in a multi-channel network. What is clear from the simulation results is that the proposed protocol improves end to end delay, PDR and throughput performance significantly in comparison to another protocols such as SEARCH and CAODV.
Reconfigurable SRAM-based Field Programmable Gate Arrays (FPGAs) are everyday more attractive due to their high integration, performance, flexibility, and upgradability. Run-time reconfiguration improves the reconfigu...
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Reconfigurable SRAM-based Field Programmable Gate Arrays (FPGAs) are everyday more attractive due to their high integration, performance, flexibility, and upgradability. Run-time reconfiguration improves the reconfigurable computing paradigm allowing to rewrite just a portion of the FPGA configuration memory on-line. This enhances the flexibility and provides opportunities for new high-performing architectures able to adjust in-flight the hardware to the current payload. However, the performance of reconfigurable architectures is bounded by the efficiency of the reconfiguration procedure, which in turn is bounded by the amount of configuration frames to be rewritten in the memory. Furthermore, the lack of tools and design software to implement optimized reconfigurable architectures makes their performance less efficient than expectation. In this work, we propose an approach to enhance the performance of reconfigurable systems by reducing the reconfiguration time of reconfigurable resources. Our method is based on a frame-driven routing algorithm able to drastically reduce the number of configuration memory frames used in the design. We evaluate the optimization achieved with our algorithm on several benchmark circuits of different size and we investigate the performance and the routability for different placement solutions. Experimental results confirm that our approach reduces the reconfiguration time up to 40% with respect to traditional reconfiguration approaches for a wide range of circuits.
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