Reduce Access Latency Research Articles

LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

OFDM-Based Massive Connectivity for LEO Satellite Internet of Things

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bay esian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.

CDAR-DRAM: Enabling Runtime DRAM Performance and Energy Optimization via In-Situ Charge Detection and Adaptive Data Restoration

With the increasing of DRAM’s capacity, the refresh operation rapidly becomes a major concern to the performance of the current computational system. Moreover, conservative timing parameters adopted for access operations make an increasing amount of negative impact on system performance and energy efficiency. In this paper, we propose an in-situ charge detection and adaptive data restoration DRAM (CDAR-DRAM) architecture, which can dynamically adjust the refresh rate and relax the constraints on access timing by removing pessimistic timing margins for PVT variations. CDAR-DRAM employs a lowcost skewed-inverter-based detector to monitor the bitline voltage in runtime and estimate real-time timing parameters of cells. Based on the detector, an adaptive refresh and restore scheme (CDAR-ref) is presented, which progressively reduces the refresh rate and partially restores cells’ voltage just enough for cells with sufficient charge, thereby optimizing both refresh and restoration operations. Moreover, a supplementary adaptive access scheme (CDAR-acc) is presented, which detects the runtime charge level of recently-accessed rows and reduces access latency aggressively, benefitting workloads in a single-core system and memory nonintensive workloads in a multi-core system. CDAR’s flexibility allows the two schemes to be combined. The evaluation shows that in an 8-core system, the combined scheme improves performance and energy efficiency by 15.2% and 22.6%, respectively.

A Low-Latency and High-Endurance MLC STT-MRAM-Based Cache System

Spin-transfer torque magnetic random access memory (STT-MRAM) is a promising cache memory candidate due to its high density, low leakage power, and nonvolatility. Multilevel cell (MLC) STT-MRAM can further increase density by storing 2 bits in one cell’s hard and soft domain, respectively. However, MLC STT-MRAM suffers two-step write, leading to high write energy, long latency, and severe lifetime degradation. Current encoding techniques propose to encode the new data to reduce the two-step data writes. However, they have two weaknesses: 1) high area overhead, e.g., recent work TSE (Hsieh et al., 2020) needs extra 37.5% MLCs and 2) prolong the write latency due to an extra read. Therefore, we propose enhanced one-step write (EOSwrite) to write data in one step. EOSwrite includes line bypassing and four intraline encoding techniques. Line bypassing schemes can bypass the writes to zero or clean lines, leading to low write/read latency. As for the intraline techniques, we propose four write modes. They utilize the data patterns and the clean data in cache lines to write data in one step, therefore reducing the data write latency. The key idea of one-step write is to write as much data as possible in the soft domain of MLC STT-MRAM. EOSwrite can greatly relieve the weaknesses of the current encoding schemes. Evaluation results show that EOSwrite can improve the lifetime of MLC STT-MRAM by 56.96%, reduce dynamic energy by 33.95%, reduce access latency by 36.95%, and improve system performance of MLC STT-MRAM by 4.30%, respectively. While the area overhead of EOSwrite is only 7.27%.

Exploring the Non-Overlapping Visibility Regions in XL-MIMO Random Access and Scheduling

The recent extra-large scale massive multiple-input multiple-output (XL-MIMO) systems are seen as a promising technology for providing very high data rates in high user-density scenarios. Spatial non-stationarities and visibility regions (VRs) appear across the XL-MIMO array, since its large dimension is of the same order of the distances to the user-equipments (UEs). Due to the increased density of UEs in typical applications of XL-MIMO systems and the scarcity of pilots, the design of random access (RA) protocols and scheduling algorithms become challenging. In this paper, we propose a joint RA and scheduling protocol, namely non-overlapping VR XL-MIMO (NOVR-XL) RA protocol, which takes advantage of the different VRs of the UEs for improving RA performance, besides of seeking UEs with non-overlapping VRs to be scheduled in the same payload data pilot (PDP) resource. Our results reveal that the proposed scheme achieves significant gains in terms of sum-rate compared with traditional RA schemes, as well as reducing access latency and improving connectivity performance as a whole.

Joint Optimization of IRS and UAV-Trajectory: For Supporting Statistical Delay and Error-Rate Bounded QoS Over mURLLC-Driven 6G Mobile Wireless Networks Using FBC

Massive ultra-reliable and low-latency communications (mURLLC) has been developed as a new and dominating 6G-standard services to support statistical quality of service (QoS) provisioning while raising several major design issues, including massive connectivity, ultra-low latency, and super-reliability. Correspondingly, a number of emerging 6G candidate enablers, including statistical delay and error-rate bounded QoS provisioning, finite blocklength coding (FBC), intelligent reflecting surfaces (IRS), unmanned aerial vehicle (UAV), etc., have been developed to support mURLLC. Specifically, due to the potential improvements in coverage capability as well as the spectral efficiency, both IRS and UAV have been widely proposed to reconfigure wireless propagation environments to compensate for blocked line-of-sight (LOS) communication links and create controllable and smart radio environments. In addition, to solve the massive connectivity issues imposed by mURLLC, integrating UAV with IRS provides a promising means to significantly enhance LOS coverage due to the relatively high altitude and 3D mobility of the UAVs. However, although small-packet communications enabled by FBC are usually employed for massive access to reduce access latency and decoding complexity, how to upper-bound both delay and error rate while efficiently supporting mURLLC in IRS-UAV-integrated systems still remains a challenging problem.

Joint Activity and Blind Information Detection for UAV-Assisted Massive IoT Access

Grant-free non-coherent index-modulation (NC-IM) has been recently considered as an efficient massive access scheme for enabling cost- and energy-limited Internet-of-Things (IoT) devices that transmit small data packets. This paper investigates the grant-free NC-IM scheme combined with orthogonal frequency division multiplexing for applicant to unmanned aerial vehicle (UAV)-based massive IoT access. Specifically, each device is assigned a unique non-orthogonal signature sequence codebook. Each active device transmits one of its signature sequences in the given time-frequency resources, by modulating the information in the index of the transmitted signature sequence. For small-scale multiple-input multiple-output (MIMO) deployed at the UAV-based aerial base station (BS), by jointly exploiting the space-time-frequency domain device activity, we propose a computationally efficient space-time-frequency joint activity and blind information detection (JABID) algorithm with significantly improved detection performance. Furthermore, for large-scale MIMO deployed at the aerial BS, by leveraging the sparsity of the virtual angular-domain channels, we propose an angular-domain based JABID algorithm for improving the system performance with reduced access latency. In addition, for the case of high mobility IoT devices and/or UAVs, we introduce a time-frequency spread transmission (TFST) strategy for the proposed JABID algorithms to combat doubly-selective fading channels. Finally, extensive simulation results are illustrated to verify the superiority of the proposed algorithms and the TFST strategy over known state-of-the-art algorithms.

Reconfigurable intelligent surface assisted grant-free massive access

Massive machine-type communications (mMTC) is envisioned to be one of the pivotal scenarios in the fifth-generation (5G) wireless communication, where the explosively emerging Internet-of-Things (IoT) applications have triggered the demand for services with low-latency and high-reliability. To this end, grant-free random access paradigm has been proposed as a promising enabler in simplifying the connection procedure and significantly reducing access latency. In this paper, we propose to leverage the burgeoning reconfigurable intelligent surface (RIS) for grant-free massive access working at millimeter-wave (mmWave) frequency to further boost access reliability. By attaching independently controllable phase shifts, reconfiguring, and refracting the propagation of incident electromagnetic waves, the deployed RISs could provide additional diversity gain and enhance the access channel conditions. On this basis, to address the challenging active device detection (ADD) and channel estimation (CE) problem, we develop a joint-ADDCE (JADDCE) method by resorting to the existing approximate message passing (AMP) algorithm with expectation maximization (EM) to extract the structured common sparsity in traffic behaviors and cascaded channel matrices. Finally, simulations are carried out to demonstrate the superiority of our proposed scheme.

Cost-effective data replication mechanism modelling for cloud storage

Replication increases data availability and reduces access latency in cloud storage. Solutions prioritise system performance over replica production and storage costs. Under a pay-as-you-go model, cloud users incur rising fees for replica storage and consistency management. Before expanding node copies, we outline a dynamic replication technique. The suggested technique concentrates resources for effective distribution across all nodes. The cost-effectiveness of the proposed model is determined using threshold values; the proposed system reduced cloud replicas, reducing costs. Azure's $6 static solution has three nodes. The adaptive data replication management system reduces copies from three to two, saving memory and reducing price from $6 to $4. MATLAB and other programming languages are used. Azure is compared to Date Replication and Reproducer (DRR). The proposed solution is cheaper than Azure with three replicates.

Multiagent Reinforcement Learning Meets Random Access in Massive Cellular Internet of Things

Internet of Things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this article, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Toward this end, we introduce two novel frameworks, namely, local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multiagent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor–critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.

Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Statistical Delay and Error-Rate Bounded QoS Provisioning for SWIPT Over CF M-MIMO 6G Mobile Wireless Networks Using FBC

As a new and dominating type of time-sensitive traffic over 6G wireless networks, massive ultra-reliable and low latency communications (mURLLC) has attracted considerable research attention, while raising several new challenges, including massive connectivity, ultra-low latency, super-reliability, and high energy efficiency. Several promising 6G enablers, including statistical delay and error-rate bounded quality-of-service (QoS) provisioning, cell-free (CF) massive multi-input multi-output (m-MIMO), simultaneous wireless information and power transfer (SWIPT), etc., have been developed to support mURLLC. Specifically, CF m-MIMO can significantly enhance QoS performance of SWIPT by boosting data rate and energy efficiency. On the other hand, finite blocklength coding (FBC) has been proposed to support various massive access techniques while reducing access latency and guaranteeing stringent QoS. However, how to efficiently integrate SWIPT with CF m-MIMO using FBC for statistical delay and error-rate bounded QoS to support mURLLC has posed many new challenges not encountered before. To overcome these difficulties, in this paper we develop FBC based statistical delay and error-rate bounded QoS provisioning schemes over SWIPT-enabled CF m-MIMO 6G wireless networks. First, we establish SWIPT-enabled CF m-MIMO system models using FBC. Then, we optimize the tradeoffs between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -effective capacity and harvested energy for our proposed statistical QoS provisioning. Finally, our simulated results validate and evaluate our developed schemes.

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

Efficient cooperative cache management for latency-aware data intelligent processing in edge environment

Recently, with the rapid development of the fifth-generation (5G) communication technology, various new services such as virtual reality (VR), augmented reality (AR), and video conferencing, etc. have emerged. To achieve optimized quality of service (QoS), high load and low latency, we propose an effective mobile edge caching strategy. Specifically: first, a cache prefetching algorithm is proposed to improve cache hit rate. second, the load balancing algorithm based on the maximum distribution weighted entropy is designed to improve bandwidth utilization and avoid mobile network congestion; third, the optimized cache replacement policy is built based on the content future popularity to reduce access latency and backhaul link pressure. With these proposed cache prefetching and replacement algorithm, the access latency can be reduced significantly, the load balance can be maintained and cache hit ratio can be improved obviously in 5G campus networks. This mobile edge caching policy is an effective improvement of the existing strategy, offering a strong support for the full realization of the 5G network potential.

Petri Net based modeling and analysis for improved resource utilization in cloud computing.

The cloud is a shared pool of systems that provides multiple resources through the Internet, users can access a lot of computing power using their computer. However, with the strong migration rate of multiple applications towards the cloud, more disks and servers are required to store huge data. Most of the cloud storage service providers are replicating full copies of data over multiple data centers to ensure data availability. Further, the replication is not only a costly process but also a wastage of energy resources. Furthermore, erasure codes reduce the storage cost by splitting data in n chunks and storing these chunks into n + k different data centers, to tolerate k failures. Moreover, it also needs extra computation cost to regenerate the data object. Cache-A Replica On Modification (CAROM) is a hybrid file system that gets combined benefits from both the replication and erasure codes to reduce access latency and bandwidth consumption. However, in the literature, no formal analysis of CAROM is available which can validate its performance. To address this issue, this research firstly presents a colored Petri net based formal model of CAROM. The research proceeds by presenting a formal analysis and simulation to validate the performance of the proposed system. This paper contributes towards the utilization of resources in clouds by presenting a comprehensive formal analysis of CAROM.

Hyperparameter-Free Receiver for Grant-Free NOMA Systems With MIMO-OFDM

We introduce a new grant-free non-orthogonal multiple access (GF-NOMA) scheme, which unlike conventional approaches, spreads over the frequency domain instead of the time domain to reduce access latency. Most conventional receivers for GF-NOMA require the pre-tuning of hyperparameters to achieve superior performance. To avoid such pre-tuning, we propose a hyperparameter-free receiver for the proposed architecture, exploiting the sparsity of channels in the delay domain. Simulation results demonstrate the superior performance of the proposed receiver in terms of the normalized mean-squared error and the activity error rate.

Space Habitat Data Centers—For Future Computing

Data from sensor-bearing satellites requires processing aboard terrestrial data centres that use water for cooling at the expense of high data-transfer latency. The reliance of terrestrial data centres on water increases their water footprint and limits the availability of water for other applications. Therefore, data centres with low data-transfer latency and reduced reliance on Earth’s water resources are required. This paper proposes space habitat data centres (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by 11.9–33.6% and 46.7–77% on average, respectively.

Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms

This article studies massive access in cell-free massive multi-input multi-output (MIMO)-based Internet of Things and solves the challenging active user detection (AUD) and channel estimation (CE) problems. For the uplink transmission, we propose an advanced frame structure design to reduce the access latency. Moreover, by considering the cooperation of all access points (APs), we investigate two processing paradigms at the receiver for massive access: cloud computing and edge computing. For cloud computing, all APs are connected to a centralized processing unit (CPU), and the signals received at all APs are centrally processed at the CPU. While for edge computing, the central processing is offloaded to part of APs equipped with distributed processing units, so that the AUD and CE can be performed in a distributed processing strategy. Furthermore, by leveraging the structured sparsity of the channel matrix, we develop a structured sparsity-based generalized approximated message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the quantization accuracy of the processed signals is taken into account. Based on the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE scheme is further developed under two paradigms for reduced access latency. Simulation results validate the superiority of the proposed approach over the state-of-the-art baseline schemes. Besides, the results reveal that the edge computing can achieve the similar massive access performance as the cloud computing, and the edge computing is capable of alleviating the burden on CPU, having a faster access response, and supporting more flexible AP cooperation.

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

SummaryIn Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

DeepCP: Deep Learning Driven Cascade Prediction-Based Autonomous Content Placement in Closed Social Network

Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user's personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user's privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users' personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes.