Unnecessary Bandwidth Consumption Research Articles

Viewport-Driven Adaptive 360◦ Live Streaming Optimization Framework

Virtual reality (VR) video streaming and 360◦ panoramic video have received extensive attention in recent years, which can bring users an immersive experience. However, the ultra-high bandwidth and ultra-low latency requirements of virtual reality video or 360◦ panoramic video also put tremendous pressure on the carrying capacity of the current network. In fact, since the user’s field of view (a.k.a viewport) is limited when watching a panoramic video and users can only watch about 20%∼30% of the video content, it is not necessary to directly transmit all high-resolution content to the user. Therefore, predicting the user’s future viewing viewport can be crucial for selective streaming and further bitrate decisions. Combined with the tile-based adaptive bitrate (ABR) algorithm for panoramic video, video content within the user’s viewport can be transmitted at a higher resolution, while areas outside the viewport can be transmitted at a lower resolution. This paper mainly proposes a viewport-driven adaptive 360◦ live streaming optimization framework, which combines viewport prediction and ABR algorithm to optimize the transmission of live 360◦ panoramic video. However, existing viewport prediction always suffers from low prediction accuracy and does not support real-time performance. With the advantage of convolutional network (CNN) in image processing and long short-term memory (LSTM) in temporal series processing, we propose an online-updated viewport prediction model called LiveCL which mainly utilizes CNN to extract the spatial characteristics of video frames and LSTM to learn the temporal characteristics of the user’s viewport trajectories. With the help of the viewport prediction and ABR algorithm, unnecessary bandwidth consumption can be effectively reduced. The main contributions of this work include: (1) a framework for 360◦ video transmission is proposed; (2) an online real-time viewport prediction model called LiveCL is proposed to optimize 360◦ video transmission combined with a novel ABR algorithm, which outperforms the existing model. Based on the public 360◦ video dataset, the tile accuracy, recall, precision, and frame accuracy of LiveCL are better than those of the latest model. Combined with related adaptive bitrate algorithms, the proposed viewport prediction model can reduce the transmission bandwidth by about 50%.

A loss aware MPTCP scheduler for highly lossy networks

Varieties of wireless communication links, such as HSPA+ access in high speed trains, balloon-based aerial wireless networks and satellite Internet connections have high loss rates. In such environments, Multipath TCP (MPTCP) offers a robust solution compared to regular TCP. However, MPTCP and existing schedulers suffer from performance degradation for both constant bit rate (CBR) and bulk traffic. To tackle this challenge, we develop LAMPS, a novel scheduler for MPTCP, which considers both the loss and delay when selecting subflows, and chooses segments based on subflows’ state. The design goal of LAMPS is to achieve a steady performance for different traffic and significantly reduce the unnecessary bandwidth consumption, especially in case of bursty losses. We have implemented LAMPS and evaluated its performance for Dynamic Adaptive Streaming over HTTP (DASH), CBR, and bulk traffic. Our experiment results show that LAMPS preserves application latency, keeps low memory consumption, and significantly reduces extra bandwidth consumption in the presence of high packet loss rate.

Efficient resource provisioning for virtual clusters on the cloud

A virtual cluster could consume a considerable amount of network bandwidth due to its internal communication demands. Since communication among VMs on the same physical machine is done through emulation, it does not use any network bandwidth. Therefore, placement of virtual clusters should utilise this fact to reduce unnecessary bandwidth consumption. To achieve this goal, one has to consider the constraints of the physical machines, as well as the communication relationship among the virtual machines of the virtual cluster. In this paper, we propose to use a monitoring/profiling mechanism to track and to classify virtual clusters. The information is then used by a resource provisioning mechanism to place virtual machines on physical machines, such that unnecessary network bandwidth consumption is reduced. Our experimental results show that, the proposed mechanism significantly outperforms the default VM scheduler provided by OpenStack.

Efficient resource provisioning for virtual clusters on the cloud

A virtual cluster could consume a considerable amount of network bandwidth due to its internal communication demands. Since communication among VMs on the same physical machine is done through emulation, it does not use any network bandwidth. Therefore, placement of virtual clusters should utilise this fact to reduce unnecessary bandwidth consumption. To achieve this goal, one has to consider the constraints of the physical machines, as well as the communication relationship among the virtual machines of the virtual cluster. In this paper, we propose to use a monitoring/profiling mechanism to track and to classify virtual clusters. The information is then used by a resource provisioning mechanism to place virtual machines on physical machines, such that unnecessary network bandwidth consumption is reduced. Our experimental results show that, the proposed mechanism significantly outperforms the default VM scheduler provided by OpenStack.

LIVE: Learning and Inference for Virtual Network Embedding

Network virtualization provides a promising tool for next-generation network management by allowing multiple heterogeneous virtual networks to run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively map these virtual networks onto the shared substrate network, known as the virtual network embedding (VNE) problem. Most heuristic VNE algorithms find practical solutions by leveraging a greedy matching strategy in node mapping. However, greedy node mapping may lead to unnecessary bandwidth consumption and increased network fragmentation because it ignores the relationships between the mapped virtual network requests and the mapping ones. In this paper, we re-visit the VNE problem from a statistical perspective and explore the potential dependencies between every two substrate nodes. We define a well-designed dependency matrix that represents the importance of substrate nodes and the topological relationships between them, i.e., every substrate node's degree of belief. Based on the dependency matrix generated from collecting and processing records of accepted virtual network requests, Bayesian inference is leveraged to iteratively select the most suitable substrate nodes and realize our novel statistical VNE algorithm consisting of a learning stage and an inference stage in node mapping. Due to the overall consideration of the relationships between the mapped nodes and the mapping ones, our statistical approach reduces unnecessary bandwidth consumption and achieves a better performance of embedding. Extensive simulations demonstrate that our algorithm significantly improves the long-term average revenue, acceptance ratio, and revenue/cost ratio compared to previous algorithms.

Optimal and Efficient Energy Utilization Using the Dual Mode Energy Driven Routing Protocol for Wireless Planetary Exploration

Wireless Sensor Network is the sensing network which is most useful for detecting the environmental conditions such as pressure, temperature, density. Different autonomous sensors are contained in the Wireless Sensor Network; each sensor node is sensing and detecting the environmental conditions. Planetary Exploration is the process of searching the information about the planets and its physical and chemical and biological characteristics of each and every part of the planetary system. Adhoc On demand Distance Vector routing protocol is commonly used protocol for planetary exploration in the wireless sensor network. It has several disadvantages like energy efficiency problem, unnecessary bandwidth consumption, connection setup delay is lower. To overcome all the disadvantages of AODV protocol, the Dual Mode Energy Driven Routing Protocol is proposed. DMED protocol is mainly contains two modes, the day mode and the night mode. The day mode is the charging state of the solar battery in the planetary sensor system and night mode is the sleeping state of the solar battery. Main objective of this paper is to implement a dual mode energy driven routing protocol for optimal and efficient energy utilization and improving the energy efficiency and energy balancing in the wireless sensor network for planetary exploration.

Distribution Aware Collaborative Spread Replication for Rare Objects in Unstructured Peer-to-Peer Networks

Existing unstructured peer-to-peer networks have low efficiency to locate the rare objects. Although various algorithms have been provided to address this problem, they do not distinguish whether the objects are rare or not and always incur unnecessary bandwidth consumption. In this paper, we presented a distribution aware replication method for rare objects. Unlike the traditional replication method, we not only replicate the objects but also replicate the peers’ requirement, because we believe that the rare objects are also always rarely queried. Distribution aware can control the number of the copy to avoid unnecessary overhead. Our replication methods are actually index replication named collaborative spread index replication. The distribution aware methods include the multi-hop aware and random walk sampling aware. When execute replicating, the algorithm will decide whether replicating the index or not according to the local distribution information obtained. The experimental results show that distribution aware replication can well support to find the rare objects in unstructured peer-to-peer networks with low overhead.

A modeling framework of content pollution in Peer-to-Peer video streaming systems

Peer-to-Peer (P2P) live video streaming systems are known to suffer from intermediate attacks due to its inherent vulnerabilities. The content pollution is one of the common attacks that have received little attention in P2P live streaming systems. In this paper, we propose a modeling framework of content pollution in P2P live streaming systems. This model considers both unstructured and structured overlays, and captures the key factors including churns, user interactions, multiple attackers and defensive techniques. The models are verified with simulations and implemented in a real working system, Anysee. We analyze content pollution and its effect in live streaming system. We show that: (1) the impact from content pollution can exponentially increase, similar to the random scanning worms, leading to playback interruption and unnecessary bandwidth consumption; (2) content pollution is influenced by peer cooperation, peer degree and bandwidth in unstructured overlays, and topology breadth in structured ones; (3) the structured overlay is more resilient to content pollution; (4) a hybrid overlay result in better reliability and pollution resistance; (5) hash-based chunk signature scheme is most promising against content pollution.