Nowoczesne Sieci Komputerowe
Spis treści
Zasady zaliczenia przedmiotu
Zasady zaliczenia przedmiotu zostały opisane w dokumencie dostępnym tutaj.
Praca dodatkowa - streszczenie artykułów
Streszczenie dowolnego artykułu, zaakceptowanego przez prowadzącego. Warto spojrzeć na konferencje: INFOCOM, MOBICOM, GLOBECOM, PerCom, SIGCOMM, SIGMETRICS. Poniżej kilka wybranych artykułów:-
Machine learning paradigms for next-generation wireless networks.Abstrakt: Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-to-device communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.
-
Response Time and Availability Study of RAFT Consensus in Distributed SDN Control PlaneAbstrakt: Software defined networking (SDN) promises unprecedented flexibility and ease of network operations. While flexibility is an important factor when leveraging advantages of a new technology, critical infrastructure networks also have stringent requirements on network robustness and control plane delays. Robustness in the SDN control plane is realized by deploying multiple distributed controllers, formed into clusters for durability and fast-failover purposes. However, the effect of the controller clustering on the total system response time is not well investigated in current literature. Hence, in this work we provide a detailed analytical study of the distributed consensus algorithm RAFT, implemented in OpenDaylight and ONOS SDN controller platforms. In those controllers, RAFT implements the data-store replication, leader election after controller failures and controller state recovery on successful repairs. To evaluate its performance, we introduce a framework for numerical analysis of various SDN cluster organizations w.r.t. their response time and availability metrics. We use Stochastic Activity Networks for modeling the RAFT operations, failure injection and cluster recovery processes, and using real-world experiments, we collect the rate parameters to provide realistic inputs for a representative cluster recovery model. We also show how a fast rejuvenation mechanism for the treatment of failures induced by software errors can minimize the total response time experienced by the controller clients, while guaranteeing a higher system availability in the long-term.
-
Deep Learning for Massive MIMO CSI FeedbackAbstrakt: In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.
-
Directional Spatial Channel Estimation for Massive FD-MIMO in Next Generation 5G NetworksAbstrakt: Full-dimensional (FD) channel state information at transmitter (CSIT) has always been a major limitation of the spectral efficiency of cellular multi-input multi-output (MIMO) networks. This letter proposes an FD-directional spatial channel estimation algorithm for frequency division duplex massive FD-MIMO systems. The proposed algorithm uses the statistical spatial correlation between the uplink (UL) and downlink (DL) channels of each user equipment. It spatially decomposes the UL channel into azimuthal and elevation dimensions to estimate the array principal receive responses. An FD spatial rotation matrix is constructed to estimate the corresponding transmit responses of the DL channel, in terms of the frequency band gap between the UL and DL channels. The proposed algorithm shows significantly promising performance, approaching the ideal perfect-CSIT case without UL feedback overhead.
-
End-to-End Deep Learning of Optical Fiber CommunicationsAbstrakt: In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow-without reconfiguration-reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42 Gb/s below the HD-FEC threshold at distances beyond 40 km. We find that our results outperform conventional IM/DD solutions based on two- and four-level pulse amplitude modulation with feedforward equalization at the receiver. Our study is the first step toward end-to-end deep learning based optimization of optical fiber communication systems.
-
Double-matching resource allocation strategy in fog computing networks based on cost efficiencyAbstrakt: Fog computing is an advanced technique to decrease latency and network congestion, and provide economical gains for Internet of Things (IoT) networks. In this paper, we investigate the computing resource allocation problem in three-layer fog computing networks. We first formulated the resource allocation problem as a double two-sided matching optimization problem. Then, we propose a double-matching strategy for the resource allocation problem in fog computing networks based on cost efficiency, which is derived by analysing the utility and cost in fog computing networks. The proposed double-matching strategy is an extension of the deferred acceptance algorithm from two-side matching to three-side matching. Numerical results show that high cost efficiency performance can be achieved by adopting the proposed strategy. Furthermore, by using the proposed strategy, the three participants in the fog computing networks could achieve stable results that each participant cannot change its paired partner unilaterally for more cost efficiency.
-
Quality of Experience-based Routing of Video Traffic for Overlay and ISP NetworksAbstrakt: The surge of video traffic is a challenge for service providers that need to maximize Quality of Experience (QoE) while optimizing the cost of their infrastructure. In this paper, we address the problem of routing multiple HTTP-based Adaptive Streaming (HAS) sessions to maximize QoE. We first design a QoS-QoE model incorporating different QoE metrics which is able to learn online network variations and predict their impact on representative classes of adaptation logic, video motion and client resolution. Different QoE metrics are then combined into a QoE score based on ITU-T Rec. P.1202.2. This rich score is used to formulate the routing problem. We show that, even with a piece-wise linear QoE function in the objective, the routing problem without controlled rate allocation is non-linear. We therefore express a routing-plus-rate allocation problem and make it scalable with a dual subgradient approach based on Lagrangian relaxation where subproblems select a single path for each request with a trivial search, thereby connecting explicitly QoE, QoE and HAS bitrate. We show with ns-3 simulations that our algorithm provides values for HAS QoE metrics (quality, rebufferings, variation) equivalent to MILP and better than QoS-based approaches.
-
One-Hop Out-of-Band Control Planes for Low-Power Multi-Hop Wireless NetworksAbstrakt: Separation of control and data planes (SCDP) is a desirable paradigm for low-power multi-hop wireless networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. To advance the network architecture design, we propose to leverage on the long-range communication capability of the increasingly available low-power wide-area network (LPWAN) radios to form one-hop out-of-band control planes. We choose LoRaWAN, an open, inexpensive, and ISM band based LPWAN radio to prototype our out-of-band control plane called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control (MAC) present challenges to achieving reliability and efficiency. To address these challenges, we design a TDMA-based multi-channel MAC featuring an urgent channel and negative acknowledgment. On a testbed of 16 nodes, we demonstrate applying LoRaCP to physically separate the control-plane network of the Collection Tree Protocol (CTP) from its ZigBee-based data-plane network. Extensive experiments show that LoRaCP increases CTP's packet delivery ratio from 65 % to 80 % in the presence of external interference, while consuming a per-node average radio power of 2.97mW only.
-
Mode-Suppression: A Simple and Provably Stable Chunk-Sharing Algorithm for P2P NetworksAbstrakt: The ability of a P2P network to scale its throughput up in proportion to the arrival rate of peers has recently been shown to be crucially dependent on the chunk sharing policy employed. Some policies can result in low frequencies of a particular chunk, known as the missing chunk syndrome, which can dramatically reduce throughput and lead to instability of the system. For instance, commonly used policies that nominally “boost” the sharing of infrequent chunks such as the well-known rarest-first algorithm have been shown to be unstable. Recent efforts have largely focused on the careful design of boosting policies to mitigate this issue. We take a complementary viewpoint, and instead consider a policy that simply prevents the sharing of the most frequent chunk(s). Following terminology from statistics wherein the most frequent value in a data set is called the mode, we refer to this policy as mode suppression. We prove the stability of this algorithm using Lyapunov techniques. We also design a distributed version that suppresses the mode via an estimate obtained by sampling three randomly selected peers. We show numerically that both algorithms perform well at minimizing total download times, with distributed mode suppression outperforming all others that we tested against.
-
Using machine learning in communication networks [Invited]Abstrakt: In this paper, we first review how the main machine learning concepts can apply to communication networks. Then we present results from a concrete application using unsupervised machine learning in a real network. We show how the application can detect anomalies at multiple network layers, including the optical layer, how it can be trained to anticipate anomalies before they become a problem, and how it can be trained to identify the root cause of each anomaly. Finally, we elaborate on the importance of this work and speculate about the future of intelligent adaptive networks.
-
RASCAR: Recovery-Aware Switch-Controller Assignment and Routing in SDNAbstrakt: Decoupling control and data planes in a software-defined network (SDN) has its advantages along with its challenges. Especially, resilient communication between elements in the data plane (switches) and in the control plane (controllers) is key to SDN's success as disruption of this communication after a failure can severely affect data-plane functions. After a failure, simultaneous recovery of all switch-controller communication paths (control paths) may not be possible, and multiple recovery stages may be required. Since restoration of disrupted data paths depends on the recovery of disrupted control paths feeding control information to switches, the performance of control-path recovery seriously affects data-path recovery performance. The assignment of controller to switches and the routing of controller-switch control paths are what determines the control-plane recovery performance, and hence should be performed in conjunction with a recovery plan after failures. This study proposes an algorithm for recovery-aware switch-controller assignment and routing (RASCAR), which enables fast data-path recovery after a set of failures (e.g., single point of failures and disasters). We formulate the problem as an integer linear program and propose an efficient heuristic algorithm to solve large problem instances. Our illustrative numerical studies show that RASCAR significantly reduces the data-path restoration times after any failure with a minor increase in resource consumption of control paths.
-
Priority-Based Flow Control for Dynamic and Reliable Flow Management in SDNAbstrakt: Software-defined networking (SDN) is a promising paradigm of computer networks, offering a programmable and centralized network architecture. However, although such a technology supports the ability to dynamically handle network traffic based on real-time and flexible traffic control, SDN-based networks can be vulnerable to dynamic change of flow control rules, which causes transmission disruption and packet loss in SDN hardware switches. This problem can be critical because the interruption and packet loss in SDN switches can bring additional performance degradation for SDN-controlled traffic flows in the data plane. In this paper, we propose a novel robust flow control mechanism referred to as priority-based flow control (PFC) for dynamic but disruption-free flow management when it is necessary to change flow control rules on the fly. PFC minimizes the complexity of flow modification process in SDN switches by temporarily adapting the priority of flow rules in order to substantially reduce the time spent on control-plane processing during run-time. Measurement results show that PFC is able to successfully prevent transmission disruption and packet loss events caused by traffic path changes, thus offering dynamic and lossless traffic control for SDN switches.
-
Wireless Toward the Era of Intelligent VehiclesAbstrakt: The current age is witnessing speedy revolution of vehicles from the hundred-year old moving metal box on four wheels into a new species with dazzling intelligence. To enable such intelligence, the nervous system heavily hinges upon the connectivity among vehicles as well as between vehicles and the transportation infrastructure. With such intelligence, humans would be relieved from the driving duties and naturally convert the vehicle into moving offices or entertainment rooms, thus imposing unprecedented burden to the connectivity to the world beyond the vehicle. Due to the mobile nature of vehicles, wireless naturally becomes the rescue. However, though wireless has been, to some extent, deployed on vehicles for more than half a century, the current wireless-vehicle interactions are, to the best, a mere combination, in which the wireless systems are designed accounting for the mobile environment, but do not have much to do with the vehicle core functions. In this paper, we will discuss the challenges, progresses and perspectives of the present-to-the-near-future vehicular wireless channels, wireless-vehicle combination, as well as the more demanding wireless-vehicle integration.
-
Cooperative control feedback: On backoff misbehavior of CSMA/CA MAC in channel-hopping cognitive radio networksAbstrakt: Due to dynamic channel availability in cognitive radio networks (CRNs), rendezvous problem is known as the most challenging issue in the design of media access control (MAC) protocol, which is a key step for secondary users (SUs) to start communication. With the concept of blind rendezvous, numerous channel-hopping sequence (CHS) based rendezvous schemes have been proposed to solve this problem in these years. Currently, little attention is paid to the design of a carrier sensing multiple access/collision avoidance (CSMA/CA) MAC based on these rendezvous schemes and also the rendezvous de-synchronization problem brought by multiple rendezvous networking paradigm into MAC design. To this end, we propose a cooperative channel-hopping based CSMA/CA MAC (named CoCH-CSMA/CA MAC) which works on the top of existing CHS based rendezvous schemes. Resulting from the rendezvous de-synchronization problem, a new type of collision, named false collision, is identified. As a SU cannot discern the false collision by itself, we design a cooperative control feedback scheme which employs correlation-based signal detection to reduce the coordination overhead of cooperation and helps SUs to avoid backoff misbehavior. Moreover, we analyze the behavior of our MAC protocol and its advantage. Extensive simulations prove that the cooperative control feedback scheme can effectively alleviate the impact of rendezvous de-synchronization problem on backoff misbehavior and improve network performance.