In this paper, we revisit the idea to remove Slow Start from congestion control. To do so, we build upon the newly gained freedom of transport protocol extendability offered by QUIC to hint bandwidth estimates from a typical web client to a server. Using this bandwidth estimate, we bootstrap congestion windows of new connections to quickly utilize available bandwidth. This custom flow initialization removes the common early exit of Slow Start and thus fuels short flow fairness with long-running connections. Our results indicate that we can drastically reduce flow completion time accepting some losses and thereby an inflated transmission volume. For example, for a typical DSL client, loading a 2 MB YouTube video chunk is accelerated by nearly 2x. In the worst case, we find an inflation of the transfer volume by 12 due to losses.
While past connections can hint at receiver network conditions, we rather see them as a building block that can be used in combination with a receiver-side bandwidth hint. In our work, we highlighted possible angles in which to tackle bandwidth estimates but do not go into detail. @cite_7 shows how to utilize cellular (4G) PHY-layer information at the client for bandwidth estimations, they even implement it in the Google QUIC framework but do not show the effects on other flows, fairness and losses nor focus on the startup phase. Further bandwidth estimations @cite_1 @cite_24 utilize client context information ( location, signal quality, speed) in cellular networks to learn and predict channel throughput, which could be used in an implementation of our system.
@cite_24 @cite_1 @cite_7
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We investigate a wireless system of multiple cells, each having a downlink shared channel in support of high-speed packet data services. In practice, such a system consists of hierarchically organized entities including a central server, Base Stations (BSs), and Mobile Stations (MSs). Our goal is to improve global resource utilization and reduce regional congestion given asymmetric arrivals and departures of mobile users, a goal requiring load balancing among multiple cells. For this purpose, we propose a scalable cross-layer framework to coordinate packet-level scheduling, call-level cell-site selection and handoff, and system-level cell coverage based on load, throughput, and channel measurements. In this framework, an opportunistic scheduling algorithm--the weighted Alpha-Rule--exploits the gain of multiuser diversity in each cell independently, trading aggregate (mean) down-link throughput for fairness and minimum rate guarantees among MSs. Each MS adapts to its channel dynamics and the load fluctuations in neighboring cells, in accordance with MSs' mobility or their arrival and departure, by initiating load-aware handoff and cell-site selection. The central server adjusts schedulers of all cells to coordinate their coverage by prompting cell breathing or distributed MS handoffs. Across the whole system, BSs and MSs constantly monitor their load, throughput, or channel quality in order to facilitate the overall system coordination. Our specific contributions in such a framework are highlighted by the minimum-rate guaranteed weighted Alpha-Rule scheduling, the load-aware MS handoff cell-site selection, and the Media Access Control (MAC)-layer cell breathing. Our evaluations show that the proposed framework can improve global resource utilization and load balancing, resulting in a smaller blocking rate of MS arrivals without extra resources while the aggregate throughput remains roughly the same or improved at the hot-spots. Our simulation tests also show that the coordinated system is robust to dynamic load fluctuations and is scalable to both the system dimension and the size of MS population. How to keep the probability of hand-off drops within a prespecified limit is a very important quality-of-service (QoS) issue in cellular networks because mobile users should be able to maintain ongoing sessions even during their hand-off from one cell to another. We design and evaluate predictive and adaptive schemes for bandwidth reservation for the hand-offs of ongoing sessions and the admission control of new connections. We first develop a method to estimate user mobility based on an aggregate history of hand-offs observed in each cell. This method is then used to probabilistically predict mobiles' directions and hand-off times in a cell. For each cell, the bandwidth to be reserved for hand-offs is calculated by estimating the total sum of tractional bandwidths of the expected hand-offs within a mobility-estimation time window. Three different admission-control schemes for new connection requests using this bandwidth reservation are proposed. We also consider variations that utilize the path location information available from the car navigation system or global positioning system. Finally, we evaluate the performance of the proposed schemes extensively to show that they meet our design goal and outperform the static reservation scheme under various scenarios. Next-generation cellular networks will provide higher cell capacity by adopting advanced physical layer techniques and broader bandwidth. Even in such networks, boundary users would suffer from low throughput due to severe intercell interference and unbalanced user distributions among cells, unless additional schemes to mitigate this problem are employed. In this paper, we tackle this problem by jointly optimizing partial frequency reuse and load-balancing schemes in a multicell network. We formulate this problem as a network-wide utility maximization problem and propose optimal offline and practical online algorithms to solve this. Our online algorithm turns out to be a simple mixture of inter- and intra-cell handover mechanisms for existing users and user association control and cell-site selection mechanisms for newly arriving users. A remarkable feature of the proposed algorithm is that it uses a notion of expected throughput as the decision making metric, as opposed to signal strength in conventional systems. Extensive simulations demonstrate that our online algorithm can not only closely approximate network-wide proportional fairness but also provide two types of gain, interference avoidance gain and load balancing gain, which yield 20 100 throughput improvement of boundary users (depending on traffic load distribution), while not penalizing total system throughput.We also demonstrate that this improvement cannot be achieved by conventional systems using universal frequency reuse and signal strength as the decision making metric. In cellular networks, an important practical issue is how to limit the handoff dropping probability efficiently. One possible approach is to perform dynamic bandwidth reservation based on mobility predictions. With the rapid advances in mobile positioning technology, and the widespread availability of digital road maps previously designed for navigational devices, we propose a predictive bandwidth reservation scheme built upon these timely opportunities. In contrast to the common practice of utilizing only incoming handoff predictions at each cell to compute the reservations, our scheme is more efficient as it innovatively utilizes both incoming and outgoing handoff predictions; it can meet the same target handoff dropping probability by blocking fewer new calls. The individual base stations are responsible for the computations, which are shown to be simple enough to be performed in real-time. We evaluate the scheme via simulation, along with five other schemes for comparison. Simulation results show that those schemes that rely on positioning information are significantly more efficient than those that do not. Our scheme's additional use of the road topology information further improves upon this advantage, bringing the efficiency closer to the bound set by a benchmark scheme that assumes perfect knowledge about future handoffs.