Reduce Bandwidth Consumption Research Articles

Bandwidth-efficient multi-task AI inference with dynamic task importance for the Internet of Things in edge computing

Over the past years, artificial intelligence (AI) models have been utilized for the Internet of Things (IoT) in applications such as remote assistance based on augmented reality (AR) in smart factories, as well as powerline inspection and precision agriculture missions performed by unmanned aerial vehicles (UAVs). Due to the limited battery capacity and computing power of these devices (e.g., AR glasses and UAVs), edge computing is recognized as a means to empower the Internet of Things (IoT) with AI. Considering that multiple AI model inference tasks (e.g., point cloud classification and fault detection) are typically performed on the same stream of sensory data (e.g., UAV camera feed), we propose TORC ( T asks- Or iented Edge C omputing) to reduce the bandwidth requirement . By incorporating AI into data transmission, the lightweight framework of TORC preserves edge computing servers’ ability to reconstruct/restore data into the original form, ensuring the proper coexistence of AI inference tasks and traditional non-AI tasks like human inspection, as well as simultaneous localization and mapping. It encodes and decodes sensory data with neural networks , whose training is driven by the AI inference tasks, in order to reduce bandwidth consumption and latency without impairing the accuracy of the AI inference tasks. Additionally, taking into account the mobility of the IoT and changes in the environment, TORC can adapt to variation in the bandwidth budget, as well as the temporally dynamic importance of AI inference tasks, without the need to train multiple neural networks for each setting. As a demonstration, empirical results conducted on the Cityscapes dataset and tasks related to autonomous driving show that, at the same level of accuracy, TORC reduces the bandwidth consumption by up to 48% and latency by up to 26%.

Distributed hierarchical deep optimization for federated learning in mobile edge computing

Deep learning has recently attracted great attention in many application fields, especially for big data analysis in the field of edge computing. Federated learning, as a promising machine learning technology, applies training data on distributed edge nodes to design shared learning systems to protect data privacy. Due to the system update in federated learning is at the expense of parameter exchange between edge nodes, it is extremely bandwidth consuming. A novel distributed hierarchical tensor depth optimization algorithm is proposed, which compresses the model parameters from the high-dimensional tensor space to a union of low-dimensional subspaces to reduce bandwidth consumption and storage demands of federated learning. In addition, an update method based on hierarchical tensor back propagation is developed by directly calculating the gradient of low-dimensional parameters to reduce the memory requirement and improve the training efficiency caused by edge node training. Finally, a large number of simulation experiments were performed to evaluate performance based on classical data sets with different local data distributions. Experimental results show that the proposed algorithm reduces the burden of communication bandwidth and the energy consumption of edge nodes.

An Empirical Study on Interactive Flipped Classroom Model Based on Digital Micro-Video Course by Big Data Analysis and Models

This paper provides an in-depth analysis and study of the interactive flipped classroom model for a digital micro-video for a big data English course. To improve the learning efficiency of English courses and reduce the learning pressure of students, the thesis also uses certain techniques to apply audiovisual language to the production of specific micro-class videos, broadcast the successfully recorded micro-class courses to students, and then use the questionnaire to randomly distribute the designed audiovisual language use questionnaire. Micro-classes earnestly perform data statistics for students and finally conduct data analysis to summarize and verify the effects of micro-class audiovisual language use. The improved algorithm can effectively reduce the fluctuation of the consumption of various resources in the cluster and make the services in the cluster more stable. The new distributed interprocess communication based on protocol and serialization technology is more efficient than traditional communication based on protocol standards, reduces bandwidth consumption in the cluster, and improves the throughput of each node in the cluster. The content design and scripting of micro-video teaching resources are based on this. Then, the production process of micro-video teaching resources is explained, according to the selection of tools, the preparation, recording, editing, and generation of materials.

STRENGTHNING THE PRODUCTIVITY OF STORAGE FOR BIG DATA STORAGE SYSTEMS USING DISTRIBUTED DEDUPLICATION

Cloud storage is one of the key features of cloud computing, which helps cloud users outsource large numbers of data without upgrading their devices. However, Cloud Service Providers (CSPs) data storage faces problems with data redundancy. The data deduplication technique aims at eliminating redundant information segments and maintains one single instance of the data set, even if any number of users own similar data set. Since blocks of data are spread on many servers, each block of the file has to be downloaded before restoring the file to decrease system output. We suggest a cloud storage server data recovery module to improve file access efficiency and reduce time spent on network bandwidth. Device coding is used in the suggested method to store blocks in distributed cloud storage, and for data integrity, MD5 (Message Digest 5) is used. Running recovery algorithm helps the user to retrieve a file directly from the cloud servers without downloading every block. The scheme proposed improves system time efficiency and the ability to access the stored data quickly. This reduces bandwidth consumption and reduces overhead user processing while downloading the data file.

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

Performance analysis of local exit for distributed deep neural networks over cloud and edge computing

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

Vehicular Edge Computing and Networking: A Survey

As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

Enhancing energy efficiency of RISC-V processor-based embedded graphics systems through frame buffer compression

In embedded graphics systems, the graphics processing unit (GPU) also consumes significant amount of frame buffer memory bandwidth. Frame buffer compression is widely adopted to alleviate both memory bandwidth and power consumption issues for the display controller, but rarely has it been applied to addressing GPU’s consumption. This paper proposes a real-time fixed-ratio frame buffer compression technique for RISC-V processor-based embedded graphics systems. The advantage of the proposed method is that the fixed-ratio compressed frame buffer can be directly adopted as an input texture by GPU. The proposed architecture (called an FBC coprocessor) is a hardware extension to the RISC-V microprocessor that supports frame buffer memory bandwidth reduction. The results show that the coprocessor consumes only 1% additional silicon space of the whole system, while reducing bandwidth consumption by 72.64%. A prototype system-on-a-chip indicates that the proposed FBC coprocessor can reduce GPU power consumption by up to 12.7% for an example automotive application.

Prediction-based secured handover authentication for mobile cloud computing

Mobile cloud computing (MCC) is a new technology that brings cloud computing and mobile networks together. It enhances the quality of service delivered to mobile clients, network operators, and cloud providers. Security in MCC technology, particularly authentication during the handover process, is a big challenge. Current vertical handover authentication protocols encounter different problems such as undesirable delays in real-time applications, the man in the middle attack, and replay attack. In this paper, a new authentication protocol for heterogeneous IEEE 802.11/LTE-A mobile cloud networks are proposed. The proposed protocol is mainly based on the view of the 3GPP access network discovery and selection function, which uses the capacities given by the IEEE 802.11 and the 3GPP long term evolution-advanced (LTE-A) standards interconnection. A prediction scheme, with no additional load over the network, or the user is utilized to handle cloud computing issues arising during authentication in the handover process. The proposed handover authentication protocol outperformed existing protocols in terms of key confidentiality, powerful security, and efficiency which was used to reduce bandwidth consumption.

Bandwidth-Aware Dynamic Prefetch Configuration for IBM POWER8

Advanced hardware prefetch engines are being integrated in current high-performance processors. Prefetching can boost the performance of most applications, however, the induced bandwidth consumption can lead the system to a high contention for main memory bandwidth, which is a scarce resource in current multicores. In such a case, the system performance can be severely damaged. This article characterizes the applications’ behavior in an IBM POWER8 machine, which presents many prefetch settings, varying the bandwidth contention. The study reveals that the best prefetch setting for each application depends on the main memory bandwidth availability, that is, it depends on the co-running applications. Based on this study, we propose Bandwidth-Aware Prefetch Configuration (BAPC) a scalable adaptive prefetching algorithm that improves the performance of multi-program workloads. BAPC increases the performance of the applications in a 12, 15, and 16 percent of 6-, 8-, and 10-application workloads over the IBM POWER8 default configuration. In addition, BAPC reduces bandwidth consumption in 39, 42, and 45 percent, respectively.

Pache: A Packet Management Scheme of Cache in Data Center Networks

The communication traffic among servers is a principal bottleneck for data center networks (DCNs), in which redundant traffic has a significant impact on the performance of DCNs. In this paper, we propose Pache , a distributed and efficient cache scheme to achieve traffic redundancy elimination in DCNs. In Pache, nodes cache packets to reduce bandwidth consumption and gain performance revenue. Each cache node manages the local cache and improves cache efficiency with counting bloom filter, hash table and Trie. Each cache node broadcasts the local cache information by a bloom filter to other servers, and servers have cache information tables (cache-table) to record them, which achieves a cache sharing mechanism. Moreover, Pache employs false positive information table (fp-table) to reduce the false positive rate caused by bloom filters. A server determines an original packet or the corresponding fingerprint packet to send by querying cache-table and fp-table. Cache node placement mechanism is also designed to select which node to cache a packet. For evaluating the performance of Pache, we execute extensive simulations and conduct a case study on Amazon EC2 platform from different aspects. The results show that Pache can eliminate about 40 percent redundant traffic on average, and only increase 10 percent runtime, and it is also a scalable and feasible cache scheme in DCNs.

Tuoris: A middleware for visualizing dynamic graphics in scalable resolution display environments

In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images.

Optimal Guaranteed Cost Event-triggered Control of Smart Grid Against Time Delay Switch Attack

This paper mainly studies the optimal robust guaranteed cost load frequency control (LFC) problem for a class of uncertain power system under time delay switch (TDS) attack. The closed-loop power system is modelled as time delay system when an event-triggered communication scheme is adopted to reduce bandwidth consumption. In order to obtain less conservative stability criteria of the system with additive time delays, a novel Lyapunov-Krasovskii (L-K) functional is proposed and some latest integral inequalities are applied as well. Then, an optimal guaranteed cost controller is designed to eliminate the system uncertainty and frequency fluctuation, and the minimum upper bound of the performance index can be obtained by solving a convex optimization problem.

Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network.

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to establish a Flying Ad-hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapidly deployable systems. In this context, Software-Defined Networking FANET (SDN-FANET ) separates the control and data plane and provides network programmability, which considers a centralized controller to perform all FANET control functions based on global UAV context information, such as UAV positions, movement trajectories, residual energy, and others. However, control message dissemination in an SDN-FANET with low overhead and high performance is not a trivial task due to FANET particular characteristics, i.e., high mobility, failures in UAV to UAV communication, and short communication range. With this in mind, it is essential to predict UAV information for control message dissemination as well as consider hierarchical network architecture, reducing bandwidth consumption and signaling overhead. In this article, we present a Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NEtwork, called CAPONE. Based on UAV contextual information, the controller can predict UAV information without control message transmission. In addition, CAPONE divides the FANET into groups by computing the number of clusters using the Gap statistics method, which is input for a Fuzzy C-means method to determine the group leader and members. In this way, CAPONE reduces the bandwidth consumption and signaling overhead, while guaranteeing the control message delivering in FANET scenarios. Extensive simulations are used to show the gains of the CAPONE in terms of Packet Delivery Ratio, overhead, and energy compared to existing SDN-FANET architectures.

Privacy-Aware Caching in Information-Centric Networking

Information-Centric Networking (ICN) is an emerging networking paradigm where named and routable data (content) is the focal point. Users send explicit requests (interests) which specify content by name, and the network handles routing these interests to some entity capable of satisfying them with the appropriate data response (producer). One key feature of ICN is opportunistic in-network content caching. This property facilitates efficient content distribution by reducing bandwidth consumption, lessening network congestion, and improving the content retrieval latency by users (consumers). Unfortunately, the same feature is also detrimental to privacy of content consumers and producers. Simple to implement, and difficult to detect, timing attacks can exploit ICN routers as “oracles” and allow an adversary to learn whether a nearby consumer recently requested certain content. The attack leverages a timing side channel that relies on router caches and is implemented by requesting a few packets from each piece of content being probed. Similarly, probing attacks that target content producers can be used to discover whether certain content has been recently distributed. After analyzing the scope and feasibility of such attacks, we propose and evaluate some efficient countermeasures that offer quantifiable privacy guarantees while retaining the benefits of ICN.

Write-Aware Replica Placement for Cloud Computing

Cloud applications such as virtualized network functions usually rely on static or dynamic replication mechanisms to achieve high service reliability and satisfy the requirements of service level agreement. Because replicas are allocated in geographically distributed datacenters, their updates increase the difficulty of maintaining consistency among replicas in different datacenters. The consistency overheads among replicas increase bandwidth consumption and transmission latencies to degrade the quality of service (QoS) performance and service reliability. Although previous mechanisms attempt to shorten response time, reduce bandwidth consumption, or maintain high service reliability with minimized storage cost, they do not consider the overhead of data consistency. In this paper, we study the problem of replica placement with consistency overheads by analyzing the latency and reliability for different types of operations. A replication algorithm, write-aware replica placement (WARP), is then presented based on the analysis. The simulation results show that WARP can shorten response time and improve QoS without degrading reliability.

Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities

Fog computing is emerging an attractive paradigm for both academics and industry alike. Fog computing holds potential for new breeds of services and user experience. However, Fog computing is still nascent and requires strong groundwork to adopt as practically feasible, cost-effective, efficient and easily deployable alternate to currently ubiquitous cloud. Fog computing promises to introduce cloud-like services on local network while reducing the cost. In this paper, we present a novel resource efficient framework for distributed video summarization over a multi-region fog computing paradigm. The nodes of the Fog network is based on resource constrained device Raspberry Pi. Surveillance videos are distributed on different nodes and a summary is generated over the Fog network, which is periodically pushed to the cloud to reduce bandwidth consumption. Different realistic workload in the form of a surveillance videos are used to evaluate the proposed system. Experimental results suggest that even by using an extremely limited resource, single board computer, the proposed framework has very little overhead with good scalability over off-the-shelf costly cloud solutions, validating its effectiveness for IoT-assisted smart cities.

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

SummaryOne of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

Exploiting the Spatio-Temporal Attributes of HD Videos: A Bandwidth Efficient Approach

This paper proposes a novel method named exploiting spatio-temporal attributes of videos, which focuses on reducing bandwidth consumption in high-definition video streaming services. The method sequentially embeds every ninth frame of a video sequence in the spatial domain of its preceding eight consecutive frames. In this way, every ninth video frame does not require transmission, hence saving substantial amount of bandwidth. To avoid any visible degradation in videos, the embedding is done in an intelligent fashion by considering the variations in temporal information between consecutive video frames. The results show that up to 19% of bandwidth can be saved, while maintaining high video quality.

Pairing-based authentication protocol for V2G networks in smart grid

Vehicle to Grid (V2G) network is a very important component for Smart Grid (SG), as it offers new services that help the optimization of both supply and demand of energy in the SG network and provide mobile distributed capacity of battery storage for minimizing the dependency of non-renewable energy sources. However, the privacy and anonymity of users’ identity, confidentiality of the transmitted data and location of the Electric Vehicle (EV) must be guaranteed. This article proposes a pairing-based authentication protocol that guarantees confidentiality of communications, protects the identities of EV users and prevents attackers from tracking the vehicle. Results from computing and communications performance analyses were better in comparison to other protocols, thus overcoming signaling congestion and reducing bandwidth consumption. The protocol protects EVs from various known attacks and its formal security analysis revealed it achieves the security goals.