A Simple Congestion Avoidance mechanism for OpenDaylight (ODL) - Multipath TCP (MPTCP) Network structure in Software Defined Data Center (SDDC)

Centros de dados existentes hoje não estão prontos para apoiar as organizações de TI para atender às demandas de negócios em constante mudança. Assim, a próxima geração de centro de dados como Software Defined Data Center (SDDC) é explorada e deverá vir para ajudar. No entanto, o SDDC é relativamente novo desde a sua criação, em 2012, em que existem diferentes interpretações iniciais sobre a sua definição, os critérios, a arquitetura e os valores que SDDC traz referência. Há também limitada literatura e compartilhamento sobre como o SDDC funciona. O objetivo deste estudo é esclarecer a respeito da definição operacional do SDDC, critérios, arquitetura de referência, representação em obras como SDDC em três cenários, bem como padronizou os valores que ela traz. Além disso, alguns fatores para guiar as organizações de TI como adotar SDDC também são discutidos. Este estudo teve uma abordagem qualitativa em que a literatura SDDC é revista e alguns profissionais de TI SDDC são entrevistados. Por fim, as limitações do estudo, futuraspesquisas e conclusões também são fornecidos.

Today, there is a generalized standard usage of internet for all.The devices via multiple technologies that facilitates to provide few communication methods to scholars to work with. By forming multiple paths in the data center network, latest generation data centers offer maximum bandwidth with robustness. To utilize this bandwidth, it is necessary that different data flows take separate paths. In brief, a single-path transport seems inappropriate for such networks. By using Multipath TCP, we must reconsider data center networks, with a diverse approach as to the association between topology, transport protocols, routing. Multipath TCP allows certain topologies that single path TCP cannot use. In newer generation data centers, Multipath TCP is already deployable using extensively deployed technologies such as Equal-cost multipath routing. But, major benefits will come when data centers are specifically designed for multipath transports. Due to manifold of technologies like Cloud computing, social networking, and information networks there is a need to deploy the number of large data centers. While Transport Control Protocol is the leading Layer-3 transport protocol in data center networks, the operating conditions like high bandwidth, small-buffered switches, and traffic patterns causes poor performance of TCP. Data Center TCP algorithm has newly been anticipated as a TCP option for data centers which address these limitations. It is worth noting that traditional TCP protocol.

Recent data centers provide dense inter-connectivity between each pair of servers through multiple paths. These data centers offer high aggregate bandwidth and robustness by using multiple paths simultaneously. Multipath TCP (MPTCP) protocol is developed for improving throughput, fairly sharing network link capacity and providing robustness during path failure by utilizing multiple paths over multi-homed data center networks. Running MPTCP protocol for latency-sensitive rack-local short flows with many-to-one communication pattern at the access layer of multi-homed data center networks creates MPTCP incast problem. In this paper, Balanced Multipath TCP (BMPTCP) protocol is proposed to mitigate MPTCP incast problem in multi-homed data center networks. BMPTCP is a window-based congestion control protocol that prevents constant growth of each worker’s subflow congestion window size. BMPTCP computes identical congestion window size for all concurrent subflows by considering bottleneck Top of Rack (ToR) switch buffer size and increasing count of concurrently transmitting workers. This helps BMPTCP to avoid timeout events due to full window loss at ToR switch. Based on current congestion situation at ToR switches, BMPTCP adjust transmission rates of each worker’s subflow so that total amount of data transmitted by all concurrent subflows does not overflow bottleneck ToR switch buffer. Simulation results show that BMPTCP effectively alleviates MPTCP incast. It improves goodput, reduces flow completion time as compared to existing MPTCP and EW-MPTCP protocols.

Space-ground integrated networks (SGINs) have been recently regarded as a promising way to provide resilient, dependable as well as efficient data transmission in the high-speed railway (HSR) scenario. Applying multipath transmission control protocol (MPTCP) to SGIN can realize data transmission simultaneously via terrestrial and satellite networks. However, since the existing congestion control (CC) mechanisms of MPTCP fail to distinguish between adverse influences (such as packet loss and/or round-trip time increase) caused by congestion and those caused by handovers, it suffers severe performance degradation in the HSR scenario where handover frequently occurs. In this article, we first present the SGIN oriented HSR (SGIN-HSR) with MPTCP. Then leveraging cross-layer information (i.e., reference signal received power), we design a novel cross-layer aided MPTCP CC mechanism targeted at SGIN-HSR based on deep reinforcement learning, which is referred to as HSR-CC, to alleviate performance degradation problems induced by handover. The experimental results show that HSR-CC significantly enhances the goodput and outperforms state-of-the-art MPTCP CC algorithms in SGIN-HSR environments where handover frequently occurs.

One of the main goals of multipath TCP (MPTCP) is to achieve higher throughput than regular TCP by utilizing multiple paths simultaneously. When these paths share a common bottleneck, MPTCP tries not to be more aggressive than a regular TCP flow. This is achieved by MPTCP's coupled congestion control mechanism that couples the increase factor of MPTCP's subflows in congestion avoidance. However, slow-start remains unchanged and behaves uncoupled for each subflow, affecting MPTCP and concurrent traffic at the bottleneck. We propose LISA, a simple algorithm for coupling MPTCP subflows in slow-start, and investigate the trade-off that this coupling entails. Our evaluations show that coupling in slow-start not only provides gains for MPTCP but also for a concurrent TCP at the bottleneck.

Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.

Congestion avoidance mechanisms are used to prevent the saturation of the gateway which represents a bottleneck of the system. Recently, a new method for the congestion avoidance has been proposed: the smart access point with limited advertised window (SAP-LAW). The main idea is to hijack at the gateway the acknowledge packets in the transmission control protocol (TCP) connections in order to artificially reduce the advertised destination window according to some bandwidth allocation policy. Therefore, the flux control mechanism is artificially exploited to control the congestion at the bottleneck. The advantage of this approach is that it does not drop any packet and does not require any modification in the TCP implementations at the clients. While the most popular congestion avoidance mechanisms are the explicit congestion notification (ECN) and the random early detection (RED). In this paper, we propose stochastic models for the ECN/RED and SAP-LAW mechanisms in order to compare their performances under different scenarios. The models are studied in mean field regime, i.e. under a great number of TCP connections and UDP-based transmissions. Different from previous work for the ECN/RED, in this paper, we consider the presence of UDP traffic with bursts and the case of not greedy TCP connections. The models for SAP-LAW are totally new. The comparison is performed in terms of different performance indices including average queue length, system throughput, and expected waiting time.

Multipath TCP (MPTCP) is a promising technique for boosting application throughput while using well-known and versatile network socket interfaces. Recently, many interesting applications of MPTCP in various environments such as wireless networks and data centers have been proposed, but little work has been done to investigate the impact of this protocol on conventional network devices. For example, MPTCP throughput advantage can be better achieved if all MPTCP subflows are routed on disjoint paths, but this is currently not feasible since routers are not designed to recognize the membership of MPTCP subflows. In this paper, we take a first step to address this issue by proposing SAMPO, an online algorithm to detect and associate MPTCP subflows in network. The main challenge is that sampling techniques and network dynamics may cause a network device to only obtain partial flow records. SAMPO takes advantage of both protocol information and statistical characteristics of MPTCP data sequence number to overcome the challenge in network. Through analysis and experimentation, we show that SAMPO is able to detect and associate MPTCP subflows with high accuracy even when a small portion of the entire flow records are available.

As of 2010 data centers use 1.5% of global electricity production and this is expected to keep growing [1]. There is a need for a near real-time power consumption modeling/monitoring system that could be used at scale within a Software Defined Data Center (SDDC). The power consumption models and information they provide can then be used to make better decisions for data center orchestration, e.g., whether to migrate virtual machines to reduce power consumption. We propose a scalable system that would 1) create initial power consumption models, as needed, for data center components, and 2) could be continually refined while the components are in use. The models will be used for the near real-time monitoring of power consumption, as well as predicting power consumption before and after potential orchestration decisions. The first step towards this goal of whole data center power modeling and prediction is to be able to predict the power consumption of one server effectively, based on high level utilization statistics from that server. In this paper we present a novel method for modeling whole system power consumption for a server, under varying random levels of CPU utilization, with a scalable random forest based model, that utilizes statistics available at the data center management level.

Active queue management (AQM) algorithm is the key technology of congestion control for routers recommended by IETF. AQM provides a main way to avoid Internet congestion combined with TCP. Random Early Detection (RED), recommended by AQM as the default algorithm for routers of NGN, has become the research hotspot nowadays. In this paper, we proposes an improved RED algorithm named ERED (Enhanced RED) based on the study of existing RED, congestion detection and congestion avoidance. A serial of simulation had been done based on NS2 simulator. The experimental results indicate that the packet loss and stability are better than RED algorithm. The comparison of packet numbers and packet loss numbers under different congestion condition shows that the packet loss and link utilization of ERED are better than RED, which enhances the adaptability of RED algorithm.

Random early detection (RED) is widely deployed as a congestion control mechanism in the routers. However, RED is often limited by the difficulty of tuning its parameters under different congestion levels. Adaptive RED (ARED) addresses this problem by adaptively adjusting the aggressiveness of RED settings to keep the average queue size between minimum threshold (minth) and maximum threshold (maxth ). We have observed that the additive increase multiplicative decrease (AIMD) approach used in ARED can always maintain high link utilization but not effective enough in preventing high packet loss rates under heavy congestion. In this paper, we propose an adaptive version of RED which applies non-linear power function instead of linear AIMD approach to achieve better link utilization as well as minimizing the packet loss rates under various traffic load conditions

A path characteristics based adaptive subflow switching algorithm for dynamic heterogeneous MPTCP network

Cloud Data Centers are becoming a more and more important reality nowadays, as more and more services are being offered in the form of Cloud resources. In order to accommodate as many requests as possible, Data Centers typically deploy oversubscription, which requires a thoughtfully calibrated usage of the computing and network resources of the system. We especially need to re-think the usage of network resources, as the kinds of demands from applications we see today have more stringent network requirements than in the past.The Software-Defined Network paradigm offers new tools to manage the network in agile, flexible ways, according to specific needs. It enables to build new customized functions in order, for example, to monitor the instantaneous load of traffic, slice the network, route each flow according to specific policies that can be modified accordingly with the needs of the system.In this paper we present an orchestrator for Software-Defined Data Center, and the strategies used to allocate Virtual Machines, basing our decisions on the current state of the system.

Age of Information minimization in UAV-aided data collection for WSN and IoT applications: A systematic review

Data centre networks are intended to meet the data transmission needs of the data centre’s highly networked hosts. The network architecture and routing technique can have a major impact on performance parameters such as latency. The fat-tree network is now one of the most popular data centre network architectures. In recent times, data centre network traffic has been steadily expanding. Because of the high volume of traffic, it is nearly impossible for a single server to satisfy all the client’s requests. In this paper, a bio-inspired Territorial Predator Scent Marking Algorithm (TPSMA) is proposed for load balancing in a fat-tree software-defined data centre. The bio-inspired algorithm was developed to increase network Quality of Service (QoS). The proposed technique is validated using the network emulator Mininet, with the Open Network Operating System ONOS controller. The performance study conducted showed that the throughput and network utilization were improved, hence improving the QoS.