Network Bandwidth Constraints Research Articles

Modelling VM Migration in a Fog Computing Environment

Fog Computing is created to efficiently store and access data without the limitations challenging Cloud Computing deployments, such as network latency or bandwidth constraints. This is achieved by performing most of the processing on servers located as close as possible to where data is being collected. When mobile devices are equipped with limited resources and small capabilities, it would be convenient to make their associated computing and network resources follow them as much as possible. In this paper, migration process is studied and an algorithmic model is designed, selecting a generic Fat Tree architecture as the underlying topology, which may be useful to get a list of all devices being traversed through each of the redundant paths available.

Guest Editor's Introduction: Special Section on Fog/Edge Computing and Services

The papers in this special section focus on fog computing and services. The emerging Internet of Things (IoT) and rich cloud services have helped create the need for fog computing (also known as edge computing), in which data processing occurs in part at the network edge or anywhere along the cloud-to-endpoint continuum that can best meet user requirements, rather than completely in a relatively small number of massive clouds. Fog computing could address latency concerns, devices’ limited processing and storage capabilities and battery life, network bandwidth constraints and costs, and many security and privacy concerns that arise from the emerging IoT.

Real-Time Cooperative Analytics for Ambient Noise Tomography in Sensor Networks

The transformative integration of sensor networks and geophysical imaging techniques enables the creation of a system to monitor and analyze seismic data in real time as well as image various subsurface structures, properties, and dynamics. Ambient noise seismic imaging is a technique widely used in geophysical exploration for investigating subsurface structures using recorded background raw ambient noise data. The current state-of-the-art of ambient noise monitoring relies on gathering these high volumes of raw data back to a centralized server or base station to pre-process, cross-correlate, analyze frequency-time components, and generate subsurface tomography. However, modern computational sensors (for example, those with $\sim$ 1.2 GHz of processor and $\sim$ 1 GB of memory) can be not only used for recording raw vibration data but also performing in situ processing and cooperative computing to generate subsurface imaging in real time. In this paper, we present a distributed solution to apply ambient noise tomography over large dense networks and perform in-network computing on huge seismic samples while avoiding centralized computation and expensive data collection. Results show that our approach can detect subsurface velocity variations in real time while meeting network bandwidth constraints and reducing communication cost ( $\sim -75\%$ ).

Energy efficient-Dynamic and Self-optimized Routing In Wireless Sensor Network Based on Ant Colony Optimization.

A wireless sensor network (WSN) consists of low power, low cost, limited memory and small in size sensor nodes, organise them in a cooperative network and perform three basic operations - sensing data from environment, computation, and data routing. Routing in WSN mainly focuses on data dissemination, data aggregation, life time of the network and limited bandwidth constraints in order to facilitate the efficient working of the network. Due to the limited battery power, the energy conservation is a critical aspect to design an efficient routing protocol in WSN. This paper proposes the energy efficient dynamic and self-optimised routing to prolong the life time of the network, QoS and solve the stagnation problem in the network.

Energy efficient-dynamic and self-optimised routing in wireless sensor network based on ant colony optimisation

A wireless sensor network (WSN) consists of low power, low cost, limited memory and small in size sensor nodes, organise them in a cooperative network and perform three basic operations - sensing data from environment, computation, and data routing. Routing in WSN mainly focuses on data dissemination, data aggregation, life time of the network and limited bandwidth constraints in order to facilitate the efficient working of the network. Due to the limited battery power, the energy conservation is a critical aspect to design an efficient routing protocol in WSN. This paper proposes the energy efficient dynamic and self-optimised routing to prolong the life time of the network, QoS and solve the stagnation problem in the network.

An Efficient Framework Model for Optimizing Routing Performance in VANETs.

Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED).

Dual-Domain Compressed Sensing Method for Oceanic Environmental Elements Collection with Underwater Sensor Networks

Bandwidth and energy constraints of underwater wireless sensors networks necessitate an efficient data transmission between sensor nodes and the fusion center. This paper considers the data gathering underwater networks for monitoring oceanic environmental elements (e.g. temperature, salinity) and only a portion of measurements from sensors allows for oceanic information map reconstruction under compressed sensing (CS) theory. By utilizing the spatial sparsity of active sensors’ data, we introduce an activity and data detection based on CS at the receiver side resulting in an efficient data communication by avoiding the necessity of conveying identity information. For an interleave division multiple access (IDMA) sporadic transmission, CS-CBC detection that combines the benefits from chip-by-chip (CBC) multi-user detection and CS detection is proposed. Further, by successively exploring the sparsity of sensor data in spatial and frequency domain, we propose a novel efficient data gathering scheme named Dual-domain compressed sensing (DCS). Simulation results validate the effectiveness of the proposed scheme compared to IDMA-CS scheme and an optimal sensing probability problem related to minimum reconstruction error is explored.

On Distributed Linear Estimation With Observation Model Uncertainties

We consider distributed estimation of a Gaussian source in a heterogenous bandwidth constrained sensor network, where the source is corrupted by independent multiplicative and additive observation noises, with incomplete statistical knowledge of the multiplicative noise. For multi-bit quantizers, we derive the closed-form mean-square-error (MSE) expression for the linear minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous communication channels, we propose several rate allocation methods named as longest root to leaf path, greedy and integer relaxation to (i) minimize the MSE given a network bandwidth constraint, and (ii) minimize the required network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao lower bound (CRLB) and compare the MSE performance of our proposed methods against the CRLB. Our results corroborate that, for low power multiplicative observation noises and adequate network bandwidth, the gaps between the MSE of our proposed methods and the CRLB are negligible, while the performance of other methods like individual rate allocation and uniform is not satisfactory.

Remote data integrity check for remotely acquired and stored stream data

Numerous sensors have been deployed to monitor processes or environments in various fields. These sensors produce stream data that are difficult to store in a central storage area owing to network bandwidth constraints and the massive amounts of storage necessary. Thus, it is sometimes efficient to store these data using remote storage services. However, integrity concerns arise when data are stored in remote storage. This paper presents a new integrity check method for remotely acquired and stored stream data. The proposed method uses a secure data acquisition and signature extraction module to produce integrity check metadata for the stream data. To share encryption keys used in signature generation and exchange messages like signatures, the module establishes a secure communication channel with the verifier that checks the integrity of the remote data. The signatures are the metadata of data records, which are used for integrity verification. Signatures for each data record are generated by a chain hash technique, but only some of them are kept in the signature store. The proposed method can successfully detect losses and modifications for remotely acquired and collected stream data.

Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka

Cloud computing has emerged as a mainstream paradigm for hosting various types of applications by supporting easy-to-use computing services. Among the many different forms of cloud computing, hybrid clouds, which mix on-premises private cloud and third-party public cloud services to deploy applications, have gained broad acceptance. They are particularly relevant for applications requiring large volumes of computing power exceeding the computational capacity within the premises of a single organization. However, the use of hybrid clouds introduces the challenge of how much and when public cloud resources should be added to the pool of resources – and especially when it is necessary to support quality of service requirements of applications with deadline constraints. These resource provisioning decisions are far from trivial if scheduling involves data-intensive applications using voluminous amounts of data. Issues such as the impact of network latency, bandwidth constraints, and location of data must be taken into account in order to minimize the execution cost while meeting the deadline for such applications. In this paper, we propose a new resource provisioning algorithm to support the deadline requirements of data-intensive applications in hybrid cloud environments. To evaluate our proposed algorithm, we implement it in Aneka, a platform for developing scalable applications on the Cloud. Experimental results using a real case study executing a data-intensive application to measure the walkability index on a hybrid cloud platform consisting of dynamic resources from the Microsoft Azure cloud show that our proposed provisioning algorithm is able to more efficiently allocate resources compared to existing methods.

Live Broadcast With Community Interactions: Bottlenecks and Optimizations

Recent years have witnessed the rapid growth of new live broadcast services, represented by Twitch.tv and YouTube live events, where videos are crowdsourced from amateur users (e.g., game players), rather than from commercial and professional TV broadcaster or content providers. The viewers also actively contribute to the content through embedded open-chat channels. Such community interactions among viewers, or even between broadcasters and viewers, make content generation highly diversified and engaging, particularly for the young generation. In this context, cross-viewer synchronization is highly desirable; otherwise the viewers with shorter broadcast latency may act as spoilers, significantly affecting the user experience of other viewers. In this paper, we show that the end-to-end delay has a dramatically amplified impact on the broadcast latency for individual viewers. We suggest smart rate adaptation to achieve cross-viewer synchronization, and develop distributed algorithms based on dual decomposition. We further extend our solution to the cloud environment, and present the concept of ShadowCast, which moves broadcasters to the cloud to provide high-quality streams beyond broadcasters’ network bandwidth constraint. Its practicability and effectiveness is demonstrated by our implementation and test bed experiments.

AutoMigrate: a framework for developing intelligent, self-managing cloud services with maximum availability

Cloud services are on-demand services provided to end-users over the Internet and hosted by cloud service providers. A cloud service consists of a set of interacting applications/processes running on one or more interconnected VMs. Organizations are increasingly using cloud services as a cost-effective means for outsourcing their IT departments. However, cloud service availability is not guaranteed by cloud service providers, especially in the event of anomalous circumstances that spontaneously disrupt availability including natural disasters, power failure, and cybersecurity attacks. In this paper, we propose a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. The framework connects an autonomic computing agent to the cloud to automatically migrate cloud services based on anticipated cloud disruption. The autonomic agent employs a modular design to facilitate the incorporation of different techniques for deciding when to migrate cloud services, what cloud services to migrate, and where to migrate the selected cloud services. We incorporated a virtual machine selection algorithm for deciding what cloud services to migrate that maximizes the availability of high priority services during migration under time and network bandwidth constraints. We implemented the framework and conducted experiments to evaluate the performance of the underlying techniques. Based on the experiments, the use of this framework results in less down-time due to migration, thereby leading to reduced cloud service disruption.

Integrated finely tunable microring laser on silicon

Finely tunable microring laser exploits integrated capacitive structure. Large-scale computer installations are severely limited by network-bandwidth constraints and energy costs that arise from architectural designs originally based on copper interconnects1. Wavelength-division multiplexed (WDM) photonic links can increase the network bandwidth but are sensitive to environmental perturbations and manufacturing imperfections that can affect the precise emission wavelength and output power of laser transmitters2,3. Here, we demonstrate a new design of a three-terminal hybrid III–V-on-silicon laser that integrates a metal-oxide-semiconductor (MOS) capacitor into the laser cavity. The MOS capacitor makes it possible to introduce the plasma-dispersion effect4 and thus change the laser modal refractive index and free-carrier absorption (FCA) loss to tune the laser wavelength and output power, respectively. The approach enables a highly energy efficient method to tune the output power and wavelength of microring lasers, with future prospects for high-speed, chirp-free direct laser modulation. The concept is potentially applicable to other diode laser platforms.

Enhancing Video Encoding for Cloud Gaming Using Rendering Information

Cloud gaming allows games to be rendered on the cloud server and allows the rendered videos to be encoded and streamed in real time to the player's devices. Compared with other video streaming applications, cloud gaming offers a unique opportunity to enhance the video encoding process by exploiting rendering information. In this paper, we propose two techniques to improve cloud gaming video encoding, aiming at enhancing the perceived video quality and reducing the computational complexity, respectively. First, we develop a rendering-based prioritized encoding technique to improve the perceived game video quality according to network bandwidth constraints. We first propose a technique to generate a macroblock (MB)-level saliency map for every game video frame using rendering information. Furthermore, based on such a saliency map, a prioritized rate allocation scheme is proposed to dynamically adjust the value of quantization parameter of each MB. The experimental results indicate that the perceptual quality can be greatly improved using the proposed technique. We also develop a rendering-based encoding acceleration technique that utilizes rendering information to reduce the computational complexity of video encoding. This technique mainly consists of two parts. First, we propose a method to directly calculate the motion vectors (MVs) without employing the compute intensive motion search procedure. Second, based on the computed MVs, we propose a fast mode selection algorithm to reduce the number of candidate modes of each MB. The experimental results show that the proposed technique can achieve more than 42% saving in encoding time with very limited degradation in video quality.

Novel $\varepsilon$ -Approximation to Data Streams in Sensor Networks

We are gradually moving into a realm where sensors, processors, memory and wireless transceivers would be seamlessly integrated together in the physical world and form a wireless sensor network. Such networks pose new challenges in data processing and transmission due to the characteristic of limited communication bandwidth and other resource constraints of sensor networks. To reduce the cost of storage, transmission and processing of time series data generated by sensor nodes, the need for more compact representations of time series data is compelling. Although a large number of data compression algorithms have been proposed to reduce data volume, their offline characteristic or super-linear time complexity prevents them from being applied directly on time series data generated by sensor nodes. Motivated by these observations, we propose an optimal online algorithm GDPLA for constructing a disconnected piecewise linear approximation of a time series which guarantees that the vertical distance between each real data point and the corresponding fit line is less than or equal to $\varepsilon$ . GDPLA not only generates the minimum number of segments to approximate a time series with precision guarantee, but also only requires linear time $O(n)$ bounded by a constant coefficient $6$ , where unit $1$ denotes the time complexity of comparing the slopes of two lines. The low cost characteristic of our method makes it a proper choice for resource-constrained WSNs. Extensive experiments on two real data sets have been conducted to demonstrate the superior compression performance of our method.

Hybrid cooperation for machine‐to‐machine data collection in hierarchical smart building networks

Machine-to-machine (M2M) communication plays an important role in various kinds of intelligent networks. In this study, a hybrid cooperation scheme for data collection in hierarchical smart building networks (SBN) is proposed under the framework of M2M communications. The hierarchical network structure means that the data collection process is carried out via multi-layer communications. In the first layer, smart metres organise themselves into clusters and send information to the cluster-heads. Then all cluster-heads forward the received information to the base station automatically in the second layer. In particular, the roles of cluster-head can be acted by either fixed nodes or user terminals in the building, and this endow a hybrid cooperation mode to the data collection process. To construct the network structure and utilise the resources efficiently, the authors first provide some theoretical analysis on the influence of network structure and bandwidth constraints. Then a distributed scheme for joint structure formation and subband allocation is proposed based on coalitional game theory. Furthermore, for the feasibility of this scheme in practical applications, some improvements of the proposed scheme have also been made at last. The advantages of the proposed scheme are verified by simulation results.

Robust haptic large distance telemanipulation for ITER

During shutdowns, maintenance crews are expected to work in 24/6 shifts to perform critical remote handling maintenance tasks on the ITER system. In this article, we investigate the possibility to safely perform these haptic maintenance tasks remotely from control stations located anywhere around the world. To guarantee stability in time delayed bilateral teleoperation, the symmetric position tracking controller using wave variables is selected. This algorithm guarantees robustness against communication delays, can eliminate wave reflections and provide position synchronization of the master and slave devices. Experiments have been conducted under realistic local network bandwidth, latency and jitter constraints. They show sufficient transparency even for substantial communication delays.

Reference Viewpoints Selection for Multi-View Video Plus Depth Coding Based on the Network Bandwidth Constraint

In multi-view video plus depth (MVD) coding based free viewpoint video applications, a few reference viewpoints’ texture and depth videos should be compressed and transmitted at the server side. At the terminal side, the display view videos could be the decoded reference view videos or the virtual viewpoints’ videos which are synthesized by DIBR technology. The entire video quality of all display views are decided by the number of reference viewpoints and the compression distortion of each reference viewpoint’s texture and depth videos. This paper studies the impact of the reference viewpoints selection on the entire video quality of all display views. The results show that depending on the available network bandwidth, the MVD coding requires different selections of reference viewpoints to maximize the entire video quality of all display views.

Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds

Cloud computing has found broad acceptance in both industry and research, with public cloud offerings now often used in conjunction with privately owned infrastructure. Technical aspects such as the impact of network latency, bandwidth constraints, data confidentiality and security, as well as economic aspects such as sunk costs and price uncertainty are key drivers towards the adoption of such a hybrid cloud model. The use of hybrid clouds introduces the need to determine which workloads are to be outsourced, and to what cloud provider. These decisions should minimize the cost of running a partition of the total workload on one or multiple public cloud providers while taking into account the application requirements such as deadline constraints and data requirements. The variety of cost factors, pricing models and cloud provider offerings to consider, further calls for an automated scheduling approach in hybrid clouds. In this work, we tackle this problem by proposing a set of algorithms to cost-efficiently schedule the deadline-constrained bag-of-tasks applications on both public cloud providers and private infrastructure. Our algorithms take into account both computational and data transfer costs as well as network bandwidth constraints. We evaluate their performance in a realistic setting with respect to cost savings, deadlines met and computational efficiency, and investigate the impact of errors in runtime estimates on these performance metrics.

Adaptive resource allocation algorithm for internet of things with bandwidth constraint

In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things (IoT) and ensure the system stability, an adaptive resource allocation algorithm is proposed, which dynamically assigns the network bandwidth and priority among components according to their signals’ frequency domain characteristics. A remote sensed and controlled unmanned ground vehicle (UGV) path tracking test-bed was developed and multiple UGV’s tracking error signals were measured in the simulation for performance evaluation. Results show that with the same network bandwidth constraints, the proposed algorithm can reduce the accumulated and maximum errors of UGV path tracking by over 60% compared with the conventional static algorithm.