Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing （PQ）, first-in-first-out （FIFO）, and weighted fair queuing （WFQ）.

Multi-Agent Deep Reinforcement Learning for Cross-Layer Scheduling in Mobile Ad-Hoc Networks

Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.

CRF:A Scheduling of Multi-Granularity Locks in Object-Oriented Database Systems

This paper introduces a multi-granularity locking model (MGL) for concurrency control in object-oriented database system briefiy, and presents a MGL model formally. Four lockingscheduling algorithms for MGL are proposed in the paper. The ideas of single queue scheduling(SQS) and dual queue scheduling (DQS) are proposed and the algorithm and the performance evaluation for these two scheduling are presented in some paper. This paper describes a new idea of thescheduling for MGL, compatible requests first (CRF). Combining the new idea with SQS and DQS,we propose two new scheduling algorithms called CRFS and CRFD. After describing the simulationmodel, this paper illustrates the comparisons of the performance among these four algorithms. Asshown in the experiments, DQS has better performance than SQS, CRFD is better than DQS, CRFSperforms better than SQS, and CRFS is the best one of these four scheduling algorithms.

Q-greedyUCB: a New Exploration Policy to Learn Resource-Efficient Scheduling

This paper proposes a Reinforcement learning(RL)algorithm to find an optimal scheduling policy to minimize the delay for a given energy constraint in communication system where the environments such as traffic arrival rates are not known in advance and can change over time.For this purpose,this problem is formulated as an infinite-horizon Constrained Markov Decision Process(CMDP).To handle the constrained optimization problem,we first adopt the Lagrangian relaxation technique to solve it.Then,we propose a variant of Q-learning,Q-greedyUCB that combinesε-greedy and Upper Confidence Bound(UCB)algorithms to solve this constrained MDP problem.We mathematically prove that the Q-greedyUCB algorithm converges to an optimal solution.Simulation results also show that Q-greedyUCB finds an optimal scheduling strategy,and is more efficient than Q-learning withε-greedy,R-learning and the Averagepayoff RL(ARL)algorithm in terms of the cumulative regret.We also show that our algorithm can learn and adapt to the changes of the environment,so as to obtain an optimal scheduling strategy under a given power constraint for the new environment.

Energy Efficient and QoS Aware Framework for Video Transmission over Wireless Sensor Networks

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms of memory, computing, bandwidth, and battery lifetime. Multimedia transmission over WSN requires certain QoS guarantees such as huge amount of bandwidth, strict delay and lower loss ratio that makes transmitting multimedia is a complicated task. However, adopting cross-layer approach in WMSNs improves quality of service of WSN under different environmental conditions. In this work, an energy efficient and QoS aware framework for transmitting multimedia content over WSN (EQWSN) is presented, where packet, queue and path scheduling were introduced. It adapts the application layer parameter of video encoder to current wireless channel state, and drops less important packets in case of network congestion according to packet type. Finally, the path scheduling differentiates packets types/priority and route them through different paths with different QoS considering network lifetime. Simulation results show that the new scheme EQWSN transmits video quality with QoS guarantees in addition to prolonging network lifetime.

Fuzzy Scheduling of Coupled Customers toa Queueing Network with Parallel Servers

In this paper we consider a queueing network consisting of two parallel servers and threearrival streams generated by independent Poisson sources. Each server has its own queue and receivescustomers from its own arrival stream. A third arrival stream consists of customers which place resourcedemands on both servers, which are handled separately by each server once the request is made. Eachservice time is independent and exponentially distributed. Each customer in the system pays a holdingcost per unit time. The objective is to dynamically determine the optimal scheduling policy to the thirdstream of conupled customers. based on the state of the system, so as to minimize the average cost. Thismodel is new, and has Policy implications for computer or communication networks. A fuzzy approachis presented to solve this problem. Simulation shows that the approach is efficient and promising.