Real-time Streaming Applications Research Articles

Real-Time Video Streaming Applications in Military 5G Networks: A Technical Review

Abstract: The integration of advanced telecommunications technologies into military operations marks a pivotal evolution in modern warfare strategies. With the advent of military-grade 5G networks, there arises an unprecedented capacity for real-time data streaming and enhanced operational transparency on the battlefield. This literature review critically examines the deployment of real-time streaming applications within such networks, focusing on a technology stack comprising React Native, Node.js, MongoDB, Docker, and WebRTC. These technologies collectively form the backbone of a system designed to leverage the full capabilities of 5G’s high throughput and ultra-low latency, essential for the demands of contemporary military engagements.

Server Redundancy: Performa Jaringan Mengunakan DNS Failover MikroTik pada Kasus Private Server dan Public Server

The digitization of user services has increased, in line with the need for VPS and cloud computing services, which are rampant among application and platform developers. Quite a few companies that create applications or application users have servers to handle user needs. Testing is carried out using the ICMP Protocol to get real-time results and can be measured. From Scenario 1, carrying out 20 test requests, we get a packet loss of 5% with RTT Avarage of 195,838ms and Mdev 4,103ms. If you apply DNS failover in scenario 2, the client will likely access the web a little slower, as evidenced by the packet loss being 25% greater in value. Compared to scenario 1, having a high standard deviation (Mdev) of round-trip times is not desirable. This variation is also known as jitter. Increased jitter can cause a bad user experience, especially in real-time audio and video streaming applications. However, this is still understandable because it only has a 1-5 second effect on the service. Next, in scenario 3, we can see that private and public servers have relatively high gab with 0% packet loss, which has a small Mdev value of 0.309ms. Therefore, the DNS failover method is a solution for network administrators who have problems related to server migration between public servers and private servers so that services can run even if a server is maintaining or downlinking.

A Learning-based Methodology for Scenario-aware Mapping of Soft Real-time Applications onto Heterogeneous MPSoCs

Soft real-time streaming applications often process input data that evoke varying workloads for their tasks. This may lead to high energy consumption or deadline misses in case their mapping onto a heterogeneous MPSoC target architecture is not adapted, e.g., when tasks with high execution times for the current input are assigned to resources of low computational power. To handle the vast variety of different input data, we propose to cluster data with similar execution characteristics into so-called data scenarios for which we determine specialized mappings by performing a scenario-aware design space exploration (DSE). A runtime manager (RTM) uses these mappings to adapt the execution of the running applications to their upcoming input by first identifying their best-suited scenarios. Subsequently, the RTM selects mappings considering their identified scenarios, which minimize the total number of deadline misses and the consumed energy. We embed the RTM into hybrid application mapping (HAM); ergo, performing time-consuming optimizations offline. In this article, we propose a novel data-scenario-aware HAM methodology that can cope with multiple applications and comprises two novel scenario-based mapping selection algorithms: Inter-Application Resource Mediation Mapping introduces barely any runtime overhead. Adaptive multi-app mapping selection is highly adaptive to changes in the application workload but imposes a small runtime overhead. Our HAM approach is fully automated and uses machine-learning techniques to learn the selection of suitable mappings from training data sequences at design time. Experiments on three differently complex target architectures show that our proposed approach consistently outperforms existing state-of-the-art solutions regarding the number of deadline misses and consumed energy.

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.

Integrating Energy-Optimizing Scheduling of Moldable Streaming Tasks with Design Space Exploration for Multiple Core Types on Configurable Platforms

Design space exploration of a configurable, heterogeneous system for a given application with required throughput searches for a combination of cores or softcores with different architectures that can be accommodated within the given ASIC or FPGA area and that achieves the required throughput and optimizes power consumption. For a soft real-time streaming application, modeled as a task graph with internally parallelizable streaming tasks, this requires assigning a core type and quantity and DVFS frequency level to each task, which implies task runtime and energy consumption, and mapping and scheduling the tasks, such that the throughput requirement is met. We tightly integrate such static scheduling for stream processing applications with design space exploration of the best heterogeneous core combination, and solve the resulting combined optimization problem by an integer linear program (ILP). We evaluate our solution for different numbers of core types on synthetic and application-based task graphs, and demonstrate improvements of up to 34.8% for ARM big and LITTLE cores, and 70.5% for 3 different core types.

Energy-Aware Scheduling of Streaming Applications on Edge-Devices in IoT-Based Healthcare

The reliance on Network-on-Chip (NoC)-based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has shown to be an effective approach in significantly reducing energy consumption of the multiprocessor systems at the expense of additional delay. In this article we develop a novel energy-aware scheduler considering tasks with conditional constraints on Voltage Frequency Island (VFI)-based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. We propose a novel task level re-timing approach called R-CTG and integrate it with non linear programming-based scheduling and voltage scaling approach referred to as ALI-EBAD. The R-CTG approach aims to minimize the latency caused by re-timing without compromising on energy-efficiency. Compared to R-DAG, the state-of-the-art approach designed for traditional Directed Acyclic Graph (DAG)-based task graphs, R-CTG significantly reduces the re-timing latency because it only re-times tasks that free up the wasted slack. To validate our claims we performed experiments on using 12 real benchmarks, the results demonstrate that ALI-EBAD out performs CA-TMES-Search and CA-TMES-Quick task schedulers in terms of energy-efficiency.

A Comparative Analysis of Hadoop and Spark Frameworks using Word Count Algorithm

With the advent of the Big Data explosion due to the Information Technology (IT) revolution during the last few decades, the need for processing and analyzing the data at low cost in minimum time has become immensely challenging. The field of Big Data analytics is driven by the demand to process Machine Learning (ML) data, real-time streaming data, and graphics processing. The most efficient solutions to Big Data analysis in a distributed environment are Hadoop and Spark administered by Apache, both these solutions are open-source data management frameworks and they allow to distribute and compute the large datasets across multiple clusters of computing nodes. This paper provides a comprehensive comparison between Apache Hadoop & Apache Spark in terms of efficiency, scalability, security, cost-effectiveness, and other parameters. It describes primary components of Hadoop and Spark frameworks to compare their performance. The major conclusion is that Spark is better in terms of scalability and speed for real-time streaming applications; whereas, Hadoop is more viable for applications dealing with bigger datasets. This case study evaluates the performance of various components of Hadoop-such, MapReduce, and Hadoop Distributed File System (HDFS) by applying it to the well-known Word Count algorithm to ascertain its efficacy in terms of storage and computational time. Subsequently, it also provides an analysis of how Spark’s in-line memory processing could reduce the computational time of the Word Count Algorithm.

Game Theory Analysis and Modeling of Sophisticated Multi-Collusion Attack in MANETs

Mobile Adhoc Network (MANET) has been a core topic of research since the last decade. Currently, this form of networking paradigm is increasingly being construed as an integral part of upcoming urban applications of Internet-of-Things (IoT), consisting of massive connectivity of diverse types of nodes. There is a significant barrier to the applicability of existing routing approaches in conventional MANETs when integrated with IoT. This routing mismatch can lead to security risks for the MANET-based application tied with the IoT platform. This paper examines a pragmatic scenario as a test case wherein the mobile nodes must exchange multimedia signals for supporting real-time streaming applications. There exist two essential security requirements viz. i) securing the data packet and ii) understanding the unpredictable behavior of the attacker. The current study considers sophistication on the part of attacker nodes. They are aware of each other’s identity and thereby collude to conduct lethal attacks, which is rarely reflected in existing security modeling statistics. This research harnesses the potential modeling aspect of game theory to model the multiple-collusion attacker scenario. It contributes towards i) modeling strategies of regular/malicious nodes and ii) applying optimization principle using novel auxiliary information to formulate the optimal strategies. The model advances each regular node’s capability to carry out precise computation about the opponent player’s strategy prediction, i.e., malicious node. The simulation outcome of the proposed mathematical model in MATLAB ascertains that it outperforms the game theory’s baseline approach.

Energy-efficient scheduling of streaming applications in VFI-NoC-HMPSoC based edge devices

Energy-aware high-performance computing is becoming a challenging facet for streaming applications at edge devices in Internet-of-Things (IoT) due to the high computational complexity involved. Therefore, the number of processors has increased significantly on the multiprocessor system subsequently, Voltage Frequency Island (VFI) recently adopted for an effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, energy-aware scheduling of real-time streaming applications on edge-devices is investigated. First, an innovative re-timing based technique is developed to transform the dependent workload into an independent task model to avail resources and the wasted slack in the processors with a possible minimal prologue. Moreover, unlike the existing population-based optimization algorithms, a novel population-based algorithm, ARSH-FATI is proposed that can dynamically switch between explorative and exploitative search modes at run-time for performance trade-off. Finally, a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm is presented. Our static scheduler ARHS-FATI collectively performs task mapping and ordering. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based MPSoCs.

Fuzzy-logic threat classification for multi-level selective encryption over real-time video streams

This paper proposes a Fuzzy-logic Threat Classification (FTC) model as the basis of a method to auto-detect three different confidentiality levels for videos streamed from heterogeneous, mobile devices via web edge servers, possibly part of a Content Distribution Network (CDN). The FTC consists of three parallel Fuzzy Inference Systems (FIS) corresponding to device, network, and type of video application, for the real-time, intelligent selection of an appropriate confidentiality level for a specific end-user. After selection of the level, an encryption module implements the corresponding form of encryption. In tests to demonstrate the concept, there were three increasing confidentiality levels, namely (1) low-level with no encryption, (2) Medium level with an in-house cipher [variant of eXclusive OR (XOR)], named P-XOR (XOR with additional rounds of permutation) applied to Selective Encryption (SE) and (3) high level with the Advanced Encryption Standard again for SE of compressed video syntax components. Results were obtained by considering realistic specifications of multiple digital devices, networks, and differing real-time streaming applications. Visual analysis of encrypted test video clips established that the FTC outputs an appropriate privacy level by reason of the implemented FISs. Absolute encryption times across the privacy levels were distinguished by their real-time response level, which is proportionate to the required degree of confidentiality.

MAC-aware Loss Discrimination for Real-time Streaming Services in Wireless Networks

This paper addresses the rate control problem for real-time applications streamed over wireless networks. In wired networks, an equation-based rate control such as TCP-friendly rate control (TFRC) can be used to control the rates of a source under the assumption that the loss is primarily due to congestion. But in wireless networks, packet loss may be due either to congestion or to channel errors. Thus, it is necessary to differentiate between packet loss due to wireless channel errors and that due to congestion. The MAC-aware rate control scheme for real-time streaming applications discriminates packet losses due to channel errors using the event generation in the MAC layer. The simulation results show that the MAC-aware rate control scheme has higher throughput than the rate control scheme without loss classification.

Exploring the Fragmentation of the Representation of Data-Driven Journalism in the Twittersphere: A Network Analytics Approach

As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing existing power structures via an interpretive community, where homophily-evoked social interactions—even in the social media context—create echo chambers and discussion fragmentation. Is the representation of data-driven journalism in the electronic public sphere breaking boundaries among people from different domains or does it resemble the existing power structure? This study adopts a network analytics approach and constructs a representational network among actors who joined the public discussion of data-driven journalism in the Twittersphere—the co-retweeted network—such that two accounts are connected if their tweets are retweeted by the same user. Public tweets containing search queries related to data-driven journalism published from February 2017 to February 2018 were collected with Twitter real-time streaming application programming interface (API). A co-retweeted network with 1,148 accounts was derived from verified accounts’ retweeting posts. Results found that several communities emerged, and news organizations, nongovernmental and nonprofit professional organizations, and academic institutions were in the crucial positions of the network. The exponential random graph models (ERGMs) based on this network revealed the extent to which gender, geographical location, and institutional type of the users were associated with the tie-formation. This study documents the major actors who are discussing the subject of data-driven journalism and raises critical reflections toward the interdisciplinary collaboration in the production of public knowledge.

Quality/Latency-Aware Real-time Scheduling of Distributed Streaming IoT Applications

Embedded systems are increasingly networked and distributed, often, such as in the Internet of Things (IoT), over open networks with potentially unbounded delays. A key challenge is the need for real-time guarantees over such inherently unreliable and unpredictable networks. Generally, timeouts are used to provide timing guarantees while trading off data losses and quality. The schedule of distributed task executions and network timeouts thereby determines a fundamental latency-quality trade-off that is, however, not taken into account by existing scheduling algorithms. In this paper, we propose an approach for scheduling of distributed, real-time streaming applications under quality-latency goals. We formulate this as a problem of analytically deriving a static worst-case schedule of a given distributed dataflow graph that minimizes quality loss while meeting guaranteed latency constraints. Towards this end, we first develop a quality model that estimates SNR of distributed streaming applications under given network characteristics and an overall linearity assumption. Using this quality model, we then formulate and solve the scheduling of distributed dataflow graphs as a numerical optimization problem. Simulation results with random graphs show that quality/latency-aware scheduling improves SNR over a baseline schedule by 50% on average. When applied to a distributed neural network application for handwritten digit recognition, our scheduling methodology can improve classification accuracy by 10% over a naive distribution under tight latency constraints.

Designing a dynamic protocol for real-time Industrial Internet of Things-based applications by efficient management of system resources

Wireless sensor networks have gained the attention of researchers from various fields due to increased applicability. This has thus led to rapid development in the field. However, these networks still suffer from various challenges and limitations. These range from computation and processing power and available energy to mention but a few. These problems are even much more pronounced in some areas of the field such as real-time Internet of Things-based applications. In this study, a dynamic protocol that efficiently utilizes the available resources is proposed. The protocol employs five developed algorithms that aid the data transmission, neighbor, and optimal path finding processes. The protocol can be utilized in, but not limited to, real-time large-data streaming applications. The protocol is implemented on sensor nodes that are custom made by our research team. In this article, a structure that enables the sensor devices to communicate with each other over their local network or Internet as required in order to preserve the available resources is defined. Both theoretical and experimental result analyses of the entire protocol in general and individual algorithms are also performed.

Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud.

The cloud-based media streaming service is a promising paradigm for multimedia applications. It is attractive to media streaming service providers, who wish to deploy their media server clusters in a media cloud at reduced cost. Since the real-time live streaming service is both a bandwidth-intensive and quality-sensitive application, how to optimize the internal bandwidth utilization of a data center network (DCN) as well as guarantee the external bandwidth of the real-time live streaming application, is a key issue of deploying virtual machine (VM)-hosted media server cluster in a media cloud. Therefore, in this study, we propose an external-bandwidth-guaranteed media server cluster deployment scheme in media cloud. The approach simultaneously considers the outside bandwidth requirement of a tree-based media server cluster for live streaming and the intra-bandwidth consumption of a DCN. The proposed scheme models the optimal problem as a new terminal-Steiner-tree-like problem and provides an approximate algorithm for placing the media servers. Our evaluation results show that the proposed scheme guarantees the external bandwidth requirement of a real-time live streaming application, at the same time, greatly reduces the intra-bandwidth utilization of a media cloud with different DCN structures.

Resource Optimization for Real-Time Streaming Applications Using Task Replication

In this paper, we study the problem of exploiting parallelism in a hard real-time streaming application modeled as an acyclic synchronous data flow (SDF) graph and scheduled on a heterogeneous multiprocessor system-on-chip platform to alleviate the capacity fragmentation due to partitioned scheduling algorithms and reduce the number of required processors when a throughput requirement is satisfied. As the main contribution in this paper, we propose a method to determine a replication factor for each task in an acyclic SDF graph such that by distributing the workloads among more parallel tasks with lower utilization in the obtained transformed graph, the left capacity on the processors can be efficiently exploited, hence reducing the number of required processors. The experimental results, on a set of real-life streaming applications, demonstrate that our approach can reduce the minimum number of processors required to schedule an application and considerably improve the memory requirements and application latency compared to related approaches while meeting the same throughput constraint.

Task-FIFO Co-Scheduling of Streaming Applications on MPSoCs with Predictable Memory Hierarchy

This article studies the scheduling of real-time streaming applications on multiprocessor systems-on-chips with predictable memory hierarchy. An iteration-based task-FIFO co-scheduling framework is proposed for this problem. We obtain FIFO size distributions using Pareto space searching, based on which the task-to-processor mapping is obtained with the potential FIFO allocation being taken into account; then, the FIFO-to-memory allocation is optimized to minimize the total memory access cost; finally, a self-timed throughput analysis method that considers memory and direct memory access controller contention is utilized to analyze the throughput. Our methods are validated by a set of synthesized and practical applications on different platforms.

Buffer allocation for real-time streaming applications running on heterogeneous multi-processors without back-pressure

The goal of buffer allocation for real-time streaming applications is to minimize total memory consumption, while reserving sufficient space for each data production, without overwriting any live data and guaranteeing the satisfaction of real-time constraints. Previous research has mostly focused on buffer allocation for systems with back-pressure. This paper addresses the problem of buffer allocation for systems without back-pressure. Since systems without back-pressure lack blocking behavior at the side of the producer, buffer allocation requires both best- and worst-case timing analysis.Our contributions are (1) extension of the available dataflow techniques with best-case analysis; (2) the closest common dominator-based and closest common predecessor-based lifetime analysis techniques; (3) techniques to model the initialization behavior and enable token reuse.Our benchmark set includes an MP3 decoder, a WLAN receiver, an LTE receiver and an LTE-Advanced receiver. We consider two key features of LTE-Advanced: (1) carrier aggregation and (2) EPDCCH processing. Through our experiments, we demonstrate that our techniques are effective in handling the complexities of real-world applications. For the LTE-Advanced receiver case study, our techniques enable us to compare buffer allocation required for different scheduling policies with effective impact on architectural decisions. A key insight in this comparison is that our improved techniques show a different scheduling policy to be superior in terms of buffer sizes compared to our previous technique. This dramatically changes the trade-off among different scheduling policies for LTE-Advanced receiver.

Maximizing real-time streaming services based on a multi-servers networking framework

In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client–server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a (1−ϵ) approximation algorithm to solve the NP-complete “maximum services”(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.

Maximizing the Number of Good Dies for Streaming Applications in NoC-Based MPSoCs Under Process Variation

Scaling CMOS technology into nanometer feature-size nodes has made it practically impossible to precisely control the manufacturing process. This results in variation in the speed and power consumption of a circuit. As a solution to process-induced variations, circuits are conventionally implemented with conservative design margins to guarantee the target frequency of each hardware component in manufactured multiprocessor chips. This approach, referred to as worst-case design, results in a considerable circuit upsizing, in turn reducing the number of dies on a wafer. This work deals with the design of real-time systems for streaming applications (e.g., video decoders) constrained by a throughput requirement (e.g., frames per second) with reduced design margins, referred to as better-than-worst-case design . To this end, the first contribution of this work is a complete modeling framework that captures a streaming application mapped to an NoC-based multiprocessor system with voltage-frequency islands under process-induced die-to-die and within-die frequency variations . The framework is used to analyze the impact of variations in the frequency of hardware components on application throughput at the system level . The second contribution of this work is a methodology to use the proposed framework and estimate the impact of reducing circuit design margins on the number of good dies that satisfy the throughput requirement of a real-time streaming application . We show on both synthetic and real applications that the proposed better-than-worst-case design approach can increase the number of good dies by up to 9.6% and 18.8% for designs with and without fixed SRAM and IO blocks, respectively.