A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches
Multipath TCP (MPTCP) has been widely used as an efficient way for communication in many applications. Data centers, smartphones, and network operators use MPTCP to balance the traffic in a network efficiently. MPTCP is an extension of TCP (Transmission Control Protocol), which provides multiple paths, leading to higher throughput and low latency. Although MPTCP has shown better performance than TCP in many applications, it has its own challenges. The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly. Moreover, communication latency can occur if the packets are not scheduled correctly between the subflows. This paper reviews techniques to solve the above-mentioned problems based on two main approaches; non data-driven (classical) and data-driven (Machine Learning) approaches. This paper compares these two approaches and highlights their strengths and weaknesses with a view to motivating future researchers in this exciting area of machine learning for communications. This paper also provides details on the simulation of MPTCP and its implementations in real environments.
Code (0)
등록된 구현이 없습니다.
Tasks
SchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deep Adaptive Rate Allocation in Volatile Heterogeneous Wireless Networks
Modern multi-access 5G+ networks provide mobile terminals with additional capacity, improving network stability and performance. However, in highly mobile environments such as vehicular networks, supporting multi-access …
Reinforcement LearningLearning-Assisted Congestion-Aware Route Scheduling for Semiconductor Fab Material Control Systems
Automated material handling systems in semiconductor fabs are operated by a material control system (MCS) that must schedule a relay route for every transport command online, before execution. This is a data-driven sched…
Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory
Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of transportation mul…
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery
Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel …
ManagementReinforcement Learning (RL)SchedulingOn the SIR Meta Distribution in Massive MTCNetworks with Scheduling and Data Aggregation
Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope wit…
Scheduling