Most of the current active queue management (AQM) designs have major issues including severe hardship of being tuned for highly fluctuated cellular access link bandwidths. Consequently, most of the cellular network providers either give up using AQMs or use conservative offline configurations for them. However, these choices will significantly impact the performance of the emerging interactive and highly delay sensitive applications such as virtual reality and vehicle-to-vehicle communications. Therefore, in this paper, we investigate the problems of existing AQM schemes and show that they are not suitable options to support ultra-low latency applications in a highly dynamic network such as current and future cellular networks. Moreover, we believe that achieving good performance does not necessarily come from complex drop rate calculation algorithms or complicated AQM techniques. Consequently, we propose BoDe an extremely simple and deployment friendly AQM scheme to bound the queuing delay of served packets and support ultra-low latency applications. We have evaluated BoDe in extensive trace-based evaluations using cellular traces from 3 different service providers in the US and compared its performance with state-of-the-art AQM designs including CoDel and PIE under a variety of streaming applications, video conferencing applications, and various recently proposed TCP protocols. Results show that despite BoDe's simple design, it outperforms other schemes and achieves significantly lower queuing delay in all tested scenarios.
Explicit congestion notification (ECN) is another technique used to do the buffer management indirectly. In other words, ECN-based approaches, use modified switches to tag certain packets and explicitly notify the senders about congestion in the network. Later, senders need to adjust their sending rates to reduce queue occupancy and congestion in the network. ABC @cite_19 is a recent example of ECN-based approach proposed for cellular networks. However, there are two key issues with all ECN-based approaches. First, to employ these approaches in practice, application (or network-stack) at the client server need to be modified to process the ECN bits set by the switches and change the sending rates accordingly. This means that for TCP-based applications, a Kernel patch at all sources destinations is required. The problem becomes worst for UDP-based applications (such as Skype, QUIC @cite_28 etc.) that manage the congestion in the application layer. This means that all these UDP-based applications need to be modified to use the ECN-bits which is not a deployment friendly solution. The second issue is the fact that the network service providers are usually not interested in exposing their network's states (queue occupancy, queue delay, etc.) required by ECN-based schemes to the end-users.
@cite_28 @cite_19
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Abstract Safety and efficiency applications in vehicular networks rely on the exchange of periodic messages between vehicles. These messages contain position, speed, heading, and other vital information that makes the vehicles aware of their surroundings. The drawback of exchanging periodic cooperative messages is that they generate significant channel load. Decentralized Congestion Control (DCC) algorithms have been proposed to minimize the channel load. However, while the rationale for periodic message exchange is to improve awareness , existing DCC algorithms do not use awareness as a metric for deciding when, at what power, and at what rate the periodic messages need to be sent in order to make sure all vehicles are informed. We propose an environment- and context-aware DCC algorithm combines power and rate control in order to improve cooperative awareness by adapting to both specific propagation environments ( e.g. , urban intersections, open highways, suburban roads) as well as application requirements ( e.g. , different target cooperative awareness range). Studying various operational conditions ( e.g. , speed, direction, and application requirement), ECPR adjusts the transmit power of the messages in order to reach the desired awareness ratio at the target distance while at the same time controlling the channel load using an adaptive rate control algorithm. By performing extensive simulations, including realistic propagation as well as environment modeling and realistic vehicle operational environments (varying demand on both awareness range and rate), we show that ECPR can increase awareness by 20 while keeping the channel load and interference at almost the same level. When permitted by the awareness requirements, ECPR can improve the average message rate by 18 compared to algorithms that perform rate adaptation only. Multi-tenant datacenters predominantly use equal-cost multipath (ECMP) routing to distribute traffic over multiple network paths. However, ECMP static hashing causes unequal load-balancing and collisions, leading to low throughput and high latencies. Recently proposed alternatives for load-balancing perform better, but are impractical as they require either changing the tenant VM network stacks (e.g., MPTCP) or replacing all the network switches (e.g., CONGA). In this paper, we argue that the end-host hypervisor provides a sweet spot for implementing a spectrum of load-balancing algorithms that are fine-grained, congestion-aware, and reactive to network dynamics at round-trip timescales. We propose CLOVE, a scalable hypervisor-based load-balancer that requires no changes to guest VMs or to physical network switches. CLOVE uses standard ECMP in the physical network, learns about equal-cost network paths using a traceroute mechanism, and learns about congestion state along these paths using standard switch features such as ECN. It then manipulates packet header fields in the hypervisor virtual switch to route traffic over less congested paths. We introduce different variants of CLOVE that differ in the way they learn about congestion in the physical network. Using extensive simulations, we show that CLOVE captures some 80 of the performance gain of best-of-breed hardware-based load-balancing algorithms without the need for expensive hardware replacement. Most datacenters still use Equal Cost Multi-Path (ECMP), which performs congestion-oblivious hashing of flows over multiple paths, leading to an uneven distribution of traffic. Alternatives to ECMP come with deployment challenges, as they require either changing the tenant VM network stacks (e.g., MPTCP) or replacing all of the switches (e.g., CONGA). We argue that the hypervisor provides a unique point for implementing load-balancing algorithms that are easy to deploy, while still reacting quickly to congestion. We propose Clove, a scalable load-balancer that (i) runs entirely in the hypervisor, requiring no modifications to tenant VM networking stacks or physical switches, and (ii) works on any topology and adapts quickly to topology changes and traffic shifts. Clove relies on standard ECMP in physical switches, discovers paths using a novel traceroute mechanism, uses software-based flowlet-switching, and continuously learns congestion (or path utilization) state using standard switch features. It then manipulates packet-header fields in the hypervisor switch to direct traffic over less congested paths. Clove achieves 1.5 to 7 times smaller flow-completion times at 70 network load than other load-balancing algorithms that work with existing hardware. Clove also captures some 80 of the performance gain of best-of-breed hardware-based load-balancing algorithms like CONGA that require new equipment. Cloud data centers host diverse applications, mixing workloads that require small predictable latency with others requiring large sustained throughput. In this environment, today's state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal impairments that lead to high application latencies, rooted in TCP's demands on the limited buffer space available in data center switches. For example, bandwidth hungry "background" flows build up queues at the switches, and thus impact the performance of latency sensitive "foreground" traffic. To address these problems, we propose DCTCP, a TCP-like protocol for data center networks. DCTCP leverages Explicit Congestion Notification (ECN) in the network to provide multi-bit feedback to the end hosts. We evaluate DCTCP at 1 and 10Gbps speeds using commodity, shallow buffered switches. We find DCTCP delivers the same or better throughput than TCP, while using 90 less buffer space. Unlike TCP, DCTCP also provides high burst tolerance and low latency for short flows. In handling workloads derived from operational measurements, we found DCTCP enables the applications to handle 10X the current background traffic, without impacting foreground traffic. Further, a 10X increase in foreground traffic does not cause any timeouts, thus largely eliminating incast problems.