HULA

Datacenter networks offer a large degree of multipath in order to provide large bisectional bandwidth. The end-to-end performance is determined by the load-balancing strategy which needs to be designed to effectively manage congestion. Consequently, congestion aware load-balancing strategies such as CONGA or HULA have been designed. Recently, more and more applications that are hosted on cloud servers use multipath transport protocols such as MPTCP. However, in the presence of MPTCP, existing load-balancing schemes including ECMP, HULA or CONGA may lead to suboptimal forwarding decisions where multiple MPTCP subflows of one connection are pinned on the same bottleneck link.

TCP's congestion control is deliberately cautious, avoiding network overloads by starting with a small initial window and then iteratively ramping up. As a result, it often takes flows several round-trip times to fully utilize the available bandwidth. In this paper we propose RC3, a technique to quickly take advantage of available capacity from the very first RTT. RC3 uses several levels of lower priority service and a modified TCP behavior to achieve near-optimal throughputs while preserving TCP-friendliness and fairness. We implement RC3 in the Linux kernel and in NS-3. In common wide-area scenarios, RC3 results in over 40% reduction in average flow completion times, with strongest improvements - more than 70% reduction in flow completion time - seen in medium to large sized (100KB - 3MB) flows.

Data center has become an important facility for hosting various applications. For data center networks, deadline missing rate and average flow completion time are two main metrics for the performance of applications. In this paper, we find deadline-aware methods can only reduce the percentage of flows missing deadline, while flowsize-aware and information-cumulative methods can only optimize the average flow completion time. However, traffic in data center is the mixture of various flows and focusing on the single goal is not enough. We advocate to incorporate deadline and flow duration time into flow rate control. Then we design DA&FD (Deadline-Aware and Flow Duration) based rate control mechanism and analyze its performance in theory. At last, we evaluate DA&FD under different topologies, real world traffic and load scenarios, both by simulation and in real testbed. Our results show that DA&FD performs close to D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TCP and about 15%, 25%, 30%, 35% better than Ameon, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCT, Karuna, DCTCP on deadline missing rate. For average FCT, the performance of DA&FD is similar to L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCT and compared with Ameon, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TCP, Karuna, DCTCP, DA&FD can reduce average FCT by 10%, 15%, 20%, 25%.

To meet the high performance requirement of real time and critical applications in industrial Internet of Things, modern lossless ethernet data center networks (DCNs) deployed with remote direct memory access and priority-based flow control (PFC) are dedicated to delivering low latency and high bandwidth. However, existing load balancing schemes either lack sub-round-trip-time congestion sensing or fail to accurately detect and reroute flows that cause congestion in PFC-enabled lossless DCNs. Therefore, we propose LBoDSN, an in-network load balancing for lossless DCNs using direct switch notification (DSN) for fast congestion flow detection, to address above challenges. LBoDSN tracks the evolution of ingress queue lengths at destination switches to anticipate the initiation of PFC pause, precisely identifies congested flows before PFC pause, and then sends DSNs to source switches to perform rerouting. After rerouting, the congestion notification packet associated with the previous path is selectively discarded to improve transmission performance. Experiments under realistic workloads reveal that, LBoDSN outperforms CONGA by 13%–65%, and 25%–80% in average and tail Flow Completion Times (FCTs), respectively. Compared to ConWeave, LBoDSN achieves approximately 9% improvement in both average and tail FCTs, while reducing switch queue consumption for reordering.

We propose a deadline-aware flow scheduling scheme, called Preemptive Efficient Queuing (PEQ), which takes both the deadlines and sizes of the flows into account for efficient flow scheduling of flows in a data-center network in this paper. PEQ benefits from evaluating the deadlines and flow sizes of flows to improve application throughput. Our results show that PEQ outperforms the state-of-the-art deadline-aware transport schemes in terms of the application throughput and average flow completion time, under traffic with different deadline distributions. Results also show that PEQ yields a near-optimal application throughput in cases where short flows have long deadlines and long flows have strict deadlines. In other words, PEQ attains higher performance than the counterpart schemes on traffic with broader distribution of deadlines and flow sizes.

Modern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipments widely exist in production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make full use of available paths, they are prone to experience packet reordering problem under asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called AG, which adaptively adjusts switching granularity according to the asymmetric degree of multiple paths. AG increases switching granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing switching granularity to obtain high link utilization under small degrees of topology asymmetry. AG is deployed on the switches with negligible overhead, while making no modification on end-hosts. We evaluate AG through both Mininet testbed and large-scale NS2 simulations. The experimental results show that AG reduces the average and 99th flow completion time by up to 51% and 56% over the state-of-the-art load balancing schemes, respectively.

Due to the overload of IP semantics, the traditional TCP/IP network has a number of problems in scalability, mobility, and security. In this context, information-centric networking (ICN) is proposed to solve these problems. To reduce the cost of deployment and smoothly evolve, the ICN architecture needs to be compatible with existing IP infrastructure. However, the rigid underlying IP routing regulation limits the data transmission efficiency of ICN. In this paper, we propose a novel multipath transmission scheme by utilizing the characteristics and functions of ICN to enhance data transmission. The process of multipath transmission can be regarded as a service, and a multipath transmission service ID (MPSID) is assigned. By using the ICN routers bound to the MPSID as relay nodes, multiple parallel paths between the data source and the receiver are constructed. Moreover, we design a path management mechanism, including path selection and path switching. It can determine the initial path based on historical transmission information and switch to other optimal paths according to the congestion degree during transmission. The experimental results show that our proposed method can improve the average throughput and reduce the average flow completion time and the average chunk completion time.

RaceCC: A rapidly converging explicit congestion control for datacenter networks

Proactive transport has been attractive in modern high-speed and shallow buffered datacenter networks. At its heart, the link capacity is proactively allocated as credit, and then following scheduled packets are triggered by active senders according to credits, providing (near) zero loss rate and extremely low latency. Despite being promising, when waiting for credits in the first RTT, called the “pre-credit phase a substantial amount of spare bandwidth is being underutilized. To bridge this gap, current practices send a Bandwidth-Delay-Product (BDP) worth of unscheduled packets with line rate in the pre-credit phase, however, degrading throughput or tail latency. One key insight is that they consider the spare bandwidth in the pre-credit phase as a fixed value but actually changes across time and space. In this paper, we present Schef, a novel switch-based bandwidth calculation mechanism to address the pre-credit phase challenge without introducing network congestion or throughput degradation. The main idea is to calculate the spare bandwidth and apply efficient control of unscheduled packets at switches. Schef is compatible with existing proactive transports. We integrate it into a representative proposal NDP and evaluate its performance through large-scale simulations. Compared with the state-of-art, Schef reduces the average and 99th flow completion time (FCT) of short flows by up to 23% and 31%, and achieves 13% higher goodput simultaneously.

Modern datacenters provide a wide variety of application services, which generate a mix of delay-sensitive short flows and throughput-oriented long flows, transmitting in the multi-path datacenter network. Though the existing load balancing designs successfully make full use of available parallel paths and attain high bisection network bandwidth, they reroute flows regardless of their dissimilar performance requirements. The short flows suffer from the problems of large queuing delay and packet reordering, while the long flows fail to obtain high throughput due to low link utilization and packet reordering. To address these inefficiency, we design a fine-grained load balancing scheme, namely TR (Traffic-aware Rerouting), which identifies flow types and executes flexible and traffic-aware rerouting to balance the performances of both short and long flows. Besides, to avoid packet reordering, TR leverages the reverse ACKs to estimate the switch-to-switch delay, thus excluding paths that potentially cause packet reordering. Moreover, TR is only deployed on the switch without any modification on end-hosts. The experimental results of large-scale NS2 simulations show that TR reduces the average and tail flow completion time for short flows by up to 60% and 80%, as well as provides up to 3.02x gain in throughput of long flows compared to the state-of-the-art load balancing schemes.

Data center technology is growing in last few years because it can accommodate variety of applications. Some data-intensive applications are deadline sensitive and require minimum flow completion time. In literature, many proposals exist for either reducing average flow completion time or the long tail of flow completion. The state of art proposal is pFabric [3] with shortest remaining size first scheduling. Data centers are generally built on layer 3 switching to reduce broadcast domain. Hosts are assigned IP address hierarchically to reduce the size of forwarding table across all data center switches. With the help of destination IP address, a sender can find the distance to the receiver. In the proposed protocol, Topology Aware pFabric (TAP), the distance information with remaining size of flow is used for flow prioritization. TAP outperforms existing protocol pFabric. The simulation results show that prioritizing long distance flows1 further reduces average flow completion time and minimizes the total number of timeout events.

Edge computing can provide high bandwidth and low-latency service for big data tasks by leveraging the edge side's computing, storage, and network resources. With the development of microservice and docker technology, service providers can flexibly and dynamically cache microservice at the edge side to respond efficiently with limited resources. Automatically caching needed services on the nearest edge nodes and dynamically scheduling users' requests can realize that computing power and software services flow with the users to provide continuous services. However, achieving the goal needs to overcome many challenges, such as the significant fluctuation of user devices' requests at the edge side and the lack of collaboration among edge nodes. In this article, dynamic computing power scheduling and collaborative task scheduling among edge nodes are comprehensively developed. The problem is considered a multiobjective optimization problem, including sequentially minimizing the deadline missing rate of requests and the average task completion time. We propose an adaptive mechanism for dynamically collaborative computing power and task scheduling (ADCS) in the edge environment to solve this problem. It adopts the greedy decision method to schedule computing tasks to meet their deadline requirements. At the same time, it uses the best-fit method to adjust the computing resources according to the changes of users' requests. The simulation results show that ADCS can decrease the deadline missing rate and reduce the average completion time. Compared with DSR and CoDSR, the deadline missing rate is reduced by 59.91% and 19.95%, respectively. The average completion time is decreased by 37.87% and 6.71%.

The modern network is equipped with many service functions to acquire high-quality service. The emergence of network function virtualization (NFV) provides a convenient way to accomplish the network services in the form of virtual network function (VNF) and also makes the scheduling solution of VNFs flexible. The VNFs can be deployed on commodity servers as software processes. Besides, multiple VNFs are chained in a specified order as a service function chain (SFC) to serve a given flow, increasing the scheduling difficulty to minimize the average flow completion time. In this paper, we study the problem of scheduling multiple SFCs with the constraint of different resource limitations in various commodity servers. This problem is typically formulated as an Integer Linear Programming (ILP) problem, which is NP-hard. To well tackle this problem, we propose a deep reinforcement learning (DRL) approach. It involves multi-step decision making, which can be naturally transformed into a DRL problem. We design specific reward and state representations for such a multi-resource scheduling problem. We also consider how to use DRL to handle online requests of SFCs. The experiment results demonstrate that the DRL approach can significantly reduce the average completion time of a set of SFC and achieves a cost saving of 39.94% against the benchmark method.

The proliferation of cloud computing has forced data centers to support a large number of dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. The emergence of cloud applications that exchange large volumes of information have created large persistent flows that need to coexist with other types of traffic. As a result, a number of data center topologies have been proposed to support cloud applications. One of the topologies that has gained prominence is the Clos network. On one hand, Clos networks provide many nice properties, such as equidistant hops, bandwidth, and latency. On the other hand the routing of diverse traffic flows still remains an issue. Current routing methods such as equal-cost multi-path (ECMP) routing that rely on flow hashing over equal cost paths do not take into consideration traffic volume nor network state. In this paper, we investigate routing in Clos topologies. We formulate optimal flow routing in Clos networks as a binary multicommodity flow problem and present optimized flow re-routing, a modified version of the binary multicommodity flow problem that uses a heuristics approach to minimize hash collisions. We also propose FlowFit, a practical optimization method that uses network state to optimally re-assign flows to links. Our experimental and simulation results show a significant improvement in bisection bandwidth and flow completion time over ECMP.

Modern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipment widely exist in the production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make the best use of available paths, However, they are prone to experiencing packet reordering problem under the asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called PDLB, which adaptively adjusts flowcell granularity according to path diversity. PDLB increases flowcell granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing flowcell granularity to obtain high link utilization under small degrees of topology asymmetry. PDLB is only deployed on the sender without any modification on switch. We evaluate PDLB through large-scale NS2 simulations. The experimental results show that PDLB reduces the average flow completion time by up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}8-53% over the state-of-the-art load balancing schemes.