Throughput Utilization Research Articles

Joint congestion control and resource allocation for energy-efficient transmission in 5G heterogeneous networks

The deployment of small cells with carrier aggregation (CA) is a significant feature of fifth generation (5G) mobile communication systems which could be characterized by the multi-dimensional heterogeneity on their diversified requirements upon different resources. Taking the heterogeneity into account, we consider here a joint optimization problem wherein multiple kinds of resources are concurrently allocated to optimize the system throughput utility while enhancing the network energy efficiency (EE) and maintaining the system stability. Especially, for the high-dimensional non-deterministic polynomial (NP)-hard allocation problem embedded, we conduct a mathematical programming model involving nonlinear integer constraints to seek the long-term stable utility on throughput and introduce an iterative optimal modulation and coding scheme-based (optimal MCS-based) heuristic algorithm as an effective solver. In addition, as data traffic and channel condition will be time-varying in the real world, an admission control based on the Lyapunov technique that requires no prior knowledge on channel information is proposed to reduce the system overhead. Finally, not only the performance bound is derived in theory, but also the numerical experiments are conduced to reveal its characteristics with respect to the system parameter V and the EE requirement.

Energy-efficient Delay-aware and Profit Maximization Caching enabled, Congestion Control in Stochastic Network

We are presenting a new unified structure for dynamic distributed forwarding and congestion-controlled network caching enabled. Improved use of data transfer capacity and storage resources in Stochastic networks in aspects of energy-efficient and profit-maximization. In the investigation of stochastic networks, a framework has been developed for combined implementation of caching, forwarding and traffic command called the Markov Decision Process in Stochastic Learning (MDPSL) strategy. The MDPSL structure uses a virtual plane that manages customer request prices, as well as a real plane that processes actual interest packets and data packets. It can accomplish dynamically structured transmission and caching. It can fulfill dynamically distributed forwarding and caching. Focus on MDPSL communication and queuing systems, including wireless networks with time-varying channels, mobility, and arrival of random traffic. Using this framework, estimates of the time are optimized such as throughput, utility throughput, energy, and distortion. Explicit performance-delay tradeoffs are provided to show the expense of attaining optimality. A congestion control algorithm is intended to improve client services subject to network stability when optimally coupled with forwarding and caching algorithms

Joint Vehicular and Static Users Multiplexing Transmission With Hierarchical Modulation for Throughput Maximization in Vehicular Networks

With the rapid development of intelligent transportation system (ITS), increasing the throughput of all kinds of users in the mobile communication network is a pressing problem. We consider there are two types of users, i.e. vehicular users (VUs) and static users (SUs), in a vehicular communication network. Since the channel characteristics of VUs and SUs are different, designing a multi-user multiplexing transmission strategy to provide an efficient and reliable transmission for two types of users is quite challenging. We propose a dynamic cross-layer transmission strategy with joint admission control, user association and resource allocation to increase the total throughput of VUs and SUs while satisfying the different quality of service (QoS) requirements. First, we formulate a throughput utility maximization problem subject to the QoS and maximum transmit power constraints. Second, with the help of Lyapunov optimization theory, the problem can be decomposed into three subproblems including traffic admission control, user association and resource allocation. Third, a novel robust resource allocation scheme with discrete-rate hierarchical modulation (HM) is designed for the reliable multiplexing transmission of two types of users. In addition, an optimal algorithm and a low complexity suboptimal algorithm are presented to solve the user association subproblem. For two types of users, comparing with uniform modulation schemes, the proposed transmission strategy with HM scheme can improve the throughput by more than 15%.

On signal processing scheme based on network coding in relay-assisted D2D systems

Network coding (NC) can greatly improve a system’s performance on throughput and channel utilization via corresponding signal processing on the relay side. Implementation of NC in relay-assisted device-to-device (RA-D2D) communications is a promising way to further explore the advantages of RA-D2D communications in cellular systems. This paper proposes a signal processing scheme for RA-D2D which jointly takes into account NC and optimal relay selection. First, the optimal relay user equipment (UE) is selected by jointly considering the end-to-end data rate, end-to-end transmission delay, and relay survival time. Then, in the procedure of signal processing, the transmitted useful signal is combined with interference signal for NC operation, and finally, the original useful signal is recovered at the destination node. Simulation results show that the proposed scheme is able to not only eliminate the interference effectively but also has a superior performance on end-to-end transmission delay, reachability, and amount of transmitted information than that without NC. In particular, signal processing via NC is equivalent to encrypting the signal, which further enhances the information security in D2D communications.

Optimizing OpenStack Nova for Scientific Workloads

The CERN OpenStack cloud provides over 300,000 CPU cores to run data processing analyses for the Large Hadron Collider (LHC) experiments. To deliver these services, with high performance and reliable service levels, while at the same time ensuring a continuous high resource utilization has been one of the major challenges for the CERN cloud engineering team. Several optimizations like NUMA-aware scheduling and huge pages, have been deployed to improve scientific workloads performance, but the CERN Cloud team continues to explore new possibilities like preemptible instances and containers on bare-metal. In this paper we will dive into the concept and implementation challenges of preemptible instances and containers on bare-metal for scientific workloads. We will also explore how they can improve scientific workloads throughput and infrastructure resource utilization. We will present the ongoing collaboration with the Square Kilometer Array (SKA) community to develop the necessary upstream enhancement to further improve OpenStack Nova to support large-scale scientific workloads.

Toward Delay-Based Utility Maximization: Modeling and Implementation in an SDWN Platform

Packet (link) scheduling algorithms in wireless networks should be able to maximize the networks' transmission capability while maintaining an acceptable delay and fairness performance. Maximum-weight (MW) policies based on the delay index are throughput-optimal in a wireless network. However, sampling the real-time head-of-line (HOL) packet statistics is still challenging. We model the delay as a function of the arrival rate and service rate, and this model enables the scheduling algorithms to obtain the packet delay without high-frequency sampling. Then, we model the delay-related performance problems in everyday life as a utility-maximization problem. By utilizing the Lyapunov optimization, we transform the initial problem into a queue stability problem and design an MW algorithm to solve it. The solution can ensure deterministic long-term average delay guarantees. Moreover, its throughput utility differs from the optimally fair value by an amount that is inversely proportional to the delay guarantee. We evaluate the theoretical performance via simulations. We also build a real-world software-defined wireless network platform and implement our algorithm on it to evaluate the algorithm's actual performance. The experimental results show that our method outperforms the state-of-the-art algorithm in terms of throughput by up to 13.83%, and it outperforms the default first-in, first-out (FIFO) method by up to 40.41%.

Workload scheduling toward worst‐case delay and optimal utility for single‐hop Fog‐IoT architecture

Fog computing is a distributed computing model that can utilise the storage, analysis and processing capabilities of fog nodes near edge devices. Although fog computing can support task processing for various Internet of Things (IoT) systems, Fog-IoT architecture faces several new challenges with the rapid development of IoT systems, especially delay-sensitive IoT systems, such as stochastic and dynamic data arrival, optimal utility and deadline of tasks. To address these challenges, workload scheduling toward worst-case delay and optimal utility for single-hop Fog-IoT architecture are studied and the workload dynamic scheduling algorithm (WDSA) is proposed. The proposed WDSA algorithm can maximise the average throughput utility while guarantees the worst-case delay of task processing. In addition, it is online and needs no prior information about future. The algorithm performance is analysed from the perspective of optimality and worst-case delay, demonstrating that the proposed WDSA algorithm can get an approximate optima and worst-case delay guarantees. Finally, simulation results demonstrate that the efficiency and efficacy of this kind of the algorithm can meet the requirement.

Joint remote radio heads and baseband units pool resource scheduling for delay-aware traffic in cloud radio access networks

Due to the centralized signal processing and powerful computational ability, cloud radio access networks (C-RAN) is considered as a promising technique to meet the increasing demand of high-data-rate services of the fifth generation wireless communications. This paper investigates the stochastic optimization problem of C-RAN by joint remote radio heads and baseband units pool resource scheduling to achieve the throughput utility maximization. We formulate a problem to maximize the average throughput utility while stabilizing all processing and transmission queues under the power, subcarrier and computational resources constraints. By utilizing Lyapunov optimization technique, the primal problem can be decomposed into four subproblems. Throughput utility maximization, admission control and computational resource allocation subproblems can be solved easily. For the joint power and subcarrier allocation subproblem, we utilize time-sharing and alternating approaches to obtain a feasible solution. By utilizing the results of four subproblems, a joint admission control and resource allocation (JACRA) algorithm is proposed. The simulation results are provided to show that the total average throughput of JACRA algorithm is increased by 4.02% compared with joint Hungarian and iterative waterfilling (JHIW) algorithm, but the total average backlogs of JACRA algorithm are decreased by 7.04% when the control parameter grows from 1 to 10.

Downlink Dual Connectivity Approach in mmWave-Aided HetNets With Minimum Rate Requirements

The integration of millimeter-wave (mmWave) communication with conventional microwave transmission in the heterogeneous networks (HetNets) has recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, this integration combined with dual-connectivity (DC) technology is expected to further guarantee reliable transmission of as many users as possible. In this letter, from the point of user association, a downlink DC approach in the integrated HetNets is proposed. The objective is to improve the access-fairness among users, while guaranteeing the maximization of total throughput utility. First, the DC-enabled user association problem in mmWave-aided HetNets is formulated as a nonlinear integer programming problem. Then, a two-level iterative algorithm is proposed to achieve the sub-optimal user association. Finally, the numerical results show the effectiveness of our proposed algorithm.

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.

Secure Inter-Frame Space Communications for Wireless LANs

The internet of things (IoTs) offers a wide range of consumer benefits, from personal portable devices to internet-connected infrastructure. The wireless local area network (WLAN) is one of the potential candidates for IoTs technology to connect billions of smart devices. Long-range WLAN is widely deployed in dense networks as an alternative to cellular networks or satellite internet access because of its low cost, high performance, and existing ecosystem. However, due to the nature of unregulated communications in industrial, scientific, and medical (ISM) bands, WLANs experience interferences from other radios such as radars and frequency hopping devices. Once interference is detected at a WLAN device, the channel is avoided and other channels become crowded. Thus, it degrades network throughput and channel utilization. In this paper, a secure inter-frame space communication system design is proposed for WLANs to share the ISM bands with other radio systems that generate periodic radio signals. The proposed secure inter-frame communication scheme achieves the goal by designing accurate radar detection and secure inter-frame space communication. The simulation results demonstrate that the proposed scheme significantly increases the receiver sensitivity and user datagram protocol throughput.

Joint Mobility Management and Multicast Rate Adaptation in Software–Defined Enterprise WLANs

The ever-increasing demand for mobile content delivery and multimedia services is bringing renewed interest in multicast communications in Wi-Fi based WLANs. Nevertheless, multicast over Wi-Fi raises several challenges including low data rates and coexistence issues with other unicast streams. Some amendments to the Wi-Fi standard, such as 802.11aa, have introduced new delivery schemes for multicast traffic as well as finer control on the low-level aspects of the 802.11 medium access scheme. However, the logic for using such features is left to the implementer of the standard. In this paper, we present SDN@Play Mobile , a novel SDN-based solution for joint mobility management and multicast rate-adaptation in Wi-Fi networks. The solution builds upon a new abstraction, named Transmission Policy , which allows the SDN controller to reconfigure a multicast transmission policy when its optimal operating conditions are not met. An experimental evaluation carried out over a real-world testbed shows that our approach can deliver significant improvements in terms of both throughput and channel utilization compared to the legacy 802.11 multicast scheme. Finally, we release the entire software implementation under a permissive APACHE 2.0 license for academic use.

An FPGA-based Network Firewall with Expandable Rule Description

With the rapid growth of communications via the Internet, the need for an effective firewall system which has not badly affect the overall network performances has been increased. In this paper, a Field Programmable Gate Array (FPGA) -based firewall system with high performance has been implemented using Network FPGA (NetFPGA) with Xilinx Kintex-7 XC7K325T FPGA. Based on NetFPGA reference router project, a NetFPGA-based firewall system was implemented. The hardware module performs rule matching operation using content addressable memory (CAM) for higher speed data processing. To evaluate system performance, throughput, latency, and memory utilization were measured for different cases using different tools, also the number of rules that an incoming packet is subjected to was varied to get more readings using both software and hardware features. The results showed that the designed firewall system provides better performance than traditional firewalls. System throughput was doubled times of the one with Linux-Iptables firewalls.

GPU Performance vs. Thread-Level Parallelism

Graphics Processing Units (GPUs) leverage massive thread-level parallelism (TLP) to achieve high computation throughput and hide long memory latency. However, recent studies have shown that the GPU performance does not scale with the GPU occupancy or the degrees of TLP that a GPU supports, especially for memory-intensive workloads. The current understanding points to L1 D-cache contention or off-chip memory bandwidth. In this article, we perform a novel scalability analysis from the perspective of throughput utilization of various GPU components, including off-chip DRAM, multiple levels of caches, and the interconnect between L1 D-caches and L2 partitions. We show that the interconnect bandwidth is a critical bound for GPU performance scalability. For the applications that do not have saturated throughput utilization on a particular resource, their performance scales well with increased TLP. To improve TLP for such applications efficiently, we propose a fast context switching approach. When a warp/thread block (TB) is stalled by a long latency operation, the context of the warp/TB is spilled to spare on-chip resource so that a new warp/TB can be launched. The switched-out warp/TB is switched back when another warp/TB is completed or switched out. With this fine-grain fast context switching, higher TLP can be supported without increasing the sizes of critical resources like the register file. Our experiment shows that the performance can be improved by up to 47% and a geometric mean of 22% for a set of applications with unsaturated throughput utilization. Compared to the state-of-the-art TLP improvement scheme, our proposed scheme achieves 12% higher performance on average and 16% for unsaturated benchmarks.

Improved NSGA-II optimizing coding-link cost trade-offs for multicast routing in WDM networks

Network coding can greatly improve throughput and bandwidth utilization of optical networks, but it may bring additional coding cost. Besides, excessive transmission links for network coding may increase transmission distances and routing costs. In order to achieve the maximum multicast rate as much as possible for finding an efficient trade-off between the routing cost and coding cost, an improved Minimizing Coding-Link Cost Non-dominated Sorting Genetic Algorithm NSGA-II (MCLC-NSGA-II) is proposed in this paper. To reduce the complexity of coding cost optimization and link cost optimization, a modified non-dominated classification method is designed in the MCLC-NSGA-II. In the MCLC-NSGA-II, for speeding up the convergence and finding more Pareto-optimal solutions, a modified crowded-sorting method based on crowding distance and Hamming distance is put forward. And a crossover operator based on mutual learning is introduced to improve the evolutionary process. To increase the diversity of the population, a deleting–reserving strategy is applied to those individuals having the same coding cost schemes and routing cost schemes. Simulation results show that the proposed MCLC-NSGA-II can obtain more trade-offs than other multi-objective optimization algorithms with faster speed.

Latency-Sensitive Data Allocation and Workload Consolidation for Cloud Storage

Customers often suffer from the variability of data access time in (edge) cloud storage service, caused by network congestion, load dynamics, and so on. One efficient solution to guarantee a reliable latency-sensitive service (e.g., for industrial Internet of Things application) is to issue requests with multiple download/upload sessions which access the required data (replicas) stored in one or more servers, and use the earliest response from those sessions. In order to minimize the total storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to deal with. In this paper, we study the latency-sensitive data allocation problem, the latency-sensitive data reallocation problem and the latency-sensitive workload consolidation problem for cloud storage. We model the data access time as a given distribution whose cumulative density function is known, and prove that these three problems are NP-hard. To solve them, we propose an exact integer nonlinear program (INLP) and a Tabu Search-based heuristic. The simulation results reveal that the INLP can always achieve the best performance in terms of lower number of used nodes and higher storage and throughput utilization, but this comes at the expense of much higher running time. The Tabu Search-based heuristic, on the other hand, can obtain close-to-optimal performance, but in a much lower running time.

A Multi-Path-Based Adaptive Scheme for Multi-View Streaming Over HTTP

A majority of the existing research on HTTP focuses on improving the quality of experience for single-source videos. Recently, there has been a focus on multi-view streaming applications, where multiple videos are streamed simultaneously. The challenge is to improve the viewing experience of concurrently streamed videos over the bandwidth-constrained network. In this paper, we propose an adaptive scheme for multi-view streaming over HTTP that requests segments of multiple videos concurrently. First, we propose a unified throughput estimation method to calculate the throughput, solving unfair and inefficient throughput utilization problems of existing estimation methods. Second, we design a unified segment scheduling method that guarantees synchronicity among segments of concurrently downloaded video streams. Third, we present a multi-path streaming method that leverages path diversity to improve the user experience of the video streams. Finally, we present a rate adaptation algorithm that dynamically downloads high-quality segments of multiple video streams, while minimizing video quality changes and the risk of playback interruption in single- and multi-path environments.

The effect of Weibull fading channel on cooperative spectrum sensing network using an improved energy detector

This paper analyses the performance of proposed cooperative spectrum sensing (CSS) network in Weibull fading environment. First, we have derived the novel analytic expressions for probabilities of missed detection and false alarm in Weibull fading channel, assuming an improved energy detector (IED), selection combining diversity scheme and multiple antennas at each cognitive radio (CRs). Next, performance is analyzed using complementary receiver operating characteristics curves, total error rate, average channel throughput, and network utility function curves for the proposed CSS network. The optimal performance of CSS network is achieved by optimizing the CSS network parameters. The closed form of expressions for the optimum value of number of CRs, arbitrary power of received signal, and detection threshold at each CR are derived using OR-Rule and AND-Rule at fusion center (FC). The average channel throughput and network utility function analysis are evaluated using $$k=1+n$$ and $$k=N-n$$ fusion rules at FC. Finally, the impact of several network parameters such as, multiple antennas at each CR (M), number of CRs (N) in CSS network, Weibull fading parameter (V), arbitrary power of received signal (p), and sensing channel SNR ( $${\bar{\gamma }})$$ on the performance of proposed CSS network are investigated using the simulation results. The performance comparison between conventional energy detector and an IED has been highlighted with the simulations.

Distributed Interference and Delay Aware Design for D2D Communication in Large Wireless Networks With Adaptive Interference Estimation

We investigate distributed flow control and power allocation strategies for delay-aware device-to-device (D2D) communication underlaying large wireless networks, where D2D pairs reuse the resource blocks of interior cellular users (CUEs). We consider a distributed D2D power allocation framework, where the D2D pairs individually attempt to maximize their own time-average throughput utility, while collectively guaranteeing the time-average coverage probability of CUEs in multiple cells. We design a novel method to compute the individual budget of interference from each D2D pair to CUEs based on stochastic geometry tools. Then, accounting for time-varying channel fading and dynamic D2D traffic arrival, we design a distributed interference-and-delay-aware (DIDA) flow control and power allocation strategy based on Lyapunov optimization and several interference estimation methods. We also analytically derive the performance bounds of D2D pairs, and prove that the coverage probability of CUEs can be guaranteed regardless of the interference estimation error at D2D receivers. Finally, simulation results suggest that adaptive interference estimation methods are preferred and demonstrate that the DIDA strategy achieves substantial performance improvement against alternative strategies.

Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?

Recently we have suggested a universal superstatistical model of user access patterns and aggregated network traffic. The model takes into account the irregular character of end user access patterns on the web via the non-exponential distributions of the local access rates, but neglects the long-term correlations between these rates. While the model is accurate for quasi-stationary traffic records, its performance under highly variable and especially non-stationary access dynamics remains questionable. In this paper, using an example of the traffic patterns from a highly loaded network cluster hosting the website of the 1998 FIFA World Cup, we suggest a generalization of the previously suggested superstatistical model by introducing long-term correlations between access rates. Using queueing system simulations, we show explicitly that this generalization is essential for modeling network nodes with highly non-stationary access patterns, where neglecting long-term correlations leads to the underestimation of the empirical average sojourn time by several decades under high throughput utilization.