Network Bandwidth Cost Research Articles

On stability and event trigger control of fractional neural networks by fractional non-autonomous Halanay inequalities

This paper studies the stability and control of fractional neural networks by Halanay inequality technique. Based on the fractional comparison principle and supremum and infimum principle, a novel fractional non-autonomous Halanay inequality is developed. The fractional non-autonomous Halanay inequality is in a form of integral, which considers the global nature of the system parameters and reduces estimation error. By combining the Halanay inequality with a maximum auxiliary function, an asymptotically stable discriminant condition for fractional Hopfield time-delay neural networks is established in an algebraic form. Moreover, event trigger control for fractional neural networks is studied. Low network bandwidth costs and high control efficiency are guaranteed by a Mittag-Leffler type event-triggered mechanism. Then, a discriminant condition on the event trigger control for fractional neural networks is established. The advantages of the proposed methods are demonstrated by three numerical examples.

A Data Deduplication Scheme Based on DBSCAN With Tolerable Clustering Deviation

To protect data privacy, users prefer to store encrypted data in cloud servers. Cloud servers reduce the cost of storage and network bandwidth by eliminating duplicate copies. To address the potential internal data leakage problem, the concept of clustering deviation is proposed for the first time. We improve the DBSCAN algorithm to tolerate clustering deviation. A data deduplication scheme is built upon the new algorithm, which considers users as clustering samples. Instead of immediately re-clustering new users, a certain deviation is tolerated to assign the users to the existing classes. We determine the popularity of the data according to user clustering results and apply different encryption schemes to protect the security of unpopular data more effectively. The performance of the algorithm is analyzed and compared with other methods through experiments, and the results verify the feasibility and efficiency of the proposed deduplication scheme.

Dynamic Resource Allocation for Virtual Network Function Placement in Satellite Edge Clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds.

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing.

Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as “pay as per usage”. However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

Memory-event-triggering H ∞ reliable control for networked jacket platforms against earthquakes and stochastic actuator faults

This paper deals with the problems of networked modelling and memory-event-triggering reliable control for steel jacket-type platforms in network environments. First, a networked model of the jacket platform against earthquakes and probabilistic actuator faults is established. Based on this model, a memory-based event-triggering communication scheme is introduced to account for the constrained communication resources. A distinct feature of the proposed triggering scheme is that it has great potential to identify and trigger the significantly changed data than the existing ones without ‘memory’. Then, by formulating the network-based closed-loop platform system as a stochastic delay system, some sufficient conditions are derived for co-designing the desired controller and the triggering scheme. Finally, comparative simulation results are provided to demonstrate the effectiveness and merits of the proposed triggering and control co-design method. It is shown that compared with some existing event-triggering control methods, the proposed memory-event-triggering reliable controller is effective to mitigate vibrations of the jacket platform and is potentially advantageous to save more network bandwidth and control cost.

A Cloud-Guided Feature Extraction Approach for Image Retrieval in Mobile Edge Computing

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost : they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Low retrieval accuracy : they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix ${{\mathbf P}}$ P . Then, it uses the matrix ${{\mathbf P}}$ P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix ${{\mathbf P}}$ P to the edge server and uses it to multiply the image ${{\mathbf x}}$ x . The result ${{\mathbf P}}^T{{\mathbf x}}$ P T x , i.e. , image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix ${{\mathbf P}}$ P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.

Mitigating the Impact of Data Sampling on Social Media Analysis and Mining

The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.

МЕТОДИКА ОЦЕНКИ КОЭФФИЦИЕНТА ГОТОВНОСТИ СИСТЕМЫ С РЕЗЕРВИРОВАНИЕМ. ПАССИВНЫЙ РЕЖИМ

The use of fiber-optic communication lines allows for the transfer of huge amounts of information per unit of time. Even short-term failures significantly affect the quality of services provided. The level of network reliability depends on how resilient the network infrastructure is to failures and how quickly recovery occurs after them. Reliability guarantees are the main component of quality of service agreements between operators and consumers. Most often, redundancy is used to ensure the required level of reliability indicators. The disadvantage of this approach is the high cost of network bandwidth, the advantage - is a short time to switch at the reserve after a failure is detected. In order to reduce costs, in some cases, the 1:N backup option is used (one backup path is allocated for N working paths). In this paper, we consider the case of 1:2, when one backup path is allocated for two working paths. In this case active and passive switching modes are possible. When switching is active, if both working paths fail, the traffic of one of them switches to the backup path. After restoring the working path, traffic switches to the restored path, i.e. the backup path is freed. It is replaced by traffic from another failed path. A special feature of passive mode consists in that after the backup path is released, it does not deal with the traffic of the second failed path. There are several ways to restore traffic over a failed work path:. the second working path starts to recover after the first one is restored; the second working path starts to recover simultaneously with the first one. In either case, the traffic of the first path is switched back from the backup to the working path after it is restored. This paper presents a method for calculating the conditional probability that the backup path will be used for transmitting traffic from one of the working paths if both paths fail. The formula for calculating the unavailability factor of working paths is given.

Bulk Savings for Bulk Transfers: Minimizing the Energy-Cost for Geo-Distributed Data Centers

With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic distribution and large delay-tolerance of such inter-data-center bulk data transfers provide cloud service providers opportunities to optimize the operating cost. Most existing studies on inter-data-center bulk data transfers focus on minimizing the network bandwidth cost. However, the energy-cost of the bulk data transfers, which also accounts for a large proportion of operating cost in the data centers, still remains unexplored. This is an important problem, especially in the multi-electricity-market environment, where the electricity price exhibits both spatial and temporal diversities. In this paper, we systematically study the problem of how to route and schedule inter-data-center bulk data transfers to minimize the energy-cost for geo-distributed data centers. We model this problem as a min-cost multi-commodity flow problem and develop an efficient two-stage optimization method to solve it. Extensive evaluations with real-life inter-data-center network and electricity prices show that our method brings significant energy-cost savings over existing bulk data transfer methods.

Checking certificate revocation efficiently using certificate revocation guard

In the Public Key Infrastructure (PKI) model, digital certificates play a vital role in securing online communication. Communicating parties exchange and validate these certificates and the validation should fail if the certificate has been revoked. However, some existing studies [1,2] raise an alarm as the certificate revocation check is skipped in the existing PKI model for a number of reasons including network latency overheads, bandwidth costs, storage costs and privacy issues. In this article, we propose a Certificate Revocation Guard (CRG) to efficiently check certificate revocation while minimising bandwidth, latency and storage overheads. CRG is based on OCSP, which caches the revocation status of certificates locally, thus strengthening user privacy for subsequent requests. CRG is a plug and play component that could be installed on the user’s machine, at the organisational proxy or even in the ISP network. Compared to a naive approach (where a client checks the revocation status of all certificates in the chain on every request), CRG decreases the bandwidth overheads and network latencies by 95%. Using CRG incurs 69% lower storage overheads compared to the CRL method. Our results demonstrate the effectiveness of our approach to improve the certificate revocation process.

Distributed Collaborative Execution on the Edges and Its Application to AMBER Alerts

In the Internet of Everything era, billions of geographically distributed things will connect to the Internet and generate hundreds of zettabytes of data per year. Pushing that data to the cloud requires tremendous network bandwidth cost and latency. This is too onerous for some latency-sensitive applications, such as vehicle tracking using city-wide cameras. One application currently limited by such obstacles is the America’s Missing Broadcast Emergency Response (AMBER) Alert system—but edge computing could transform this system’s capabilities. Edge computing is a new computing paradigm that greatly diminishes data transmission and response latency by processing data at the proximity of data sources. However, most vision-based analytics are compute-intensive, and an edge device might be overwhelmed given tens of frames each second for real-time analysis. Also, the system needs a customized and flexible interface to implement efficient tracking strategies. To meet these needs, here we extend a big data processing framework, called Firework , to support collaboration between multiple edge devices and customizable task-scheduling strategies. Based on this extended version of Firework , we implement the AMBER alert assistant (A3), which efficiently tracks and locates a vehicle by analyzing city cameras’ data in real time. We also propose two kinds of customized task-scheduling algorithms for vehicle tracking in A3. Comprehensive evaluation results show that A3 achieves real-time video analytics by collaborating among multiple edge devices; and the proposed location-direction-related diffusion strategy effectively controls the searching area for vehicle tracking by smartly selecting candidate cameras.

Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloud-centric video analytics requires that all video data must be preloaded to a centralized cluster or the cloud, which suffers from high response latency and high cost of data transmission, given the scale of zettabytes of video data generated by IoE devices. Moreover, video data is rarely shared among multiple stakeholders due to various concerns, which restricts the practical deployment of video analytics that takes advantages of many data sources to make smart decisions. Furthermore, there is no efficient programming interface for developers and users to easily program and deploy IoE applications across geographically distributed computation resources. In this paper, we present a new computing framework, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> , which facilitates distributed data processing and sharing for IoE applications via a virtual shared data view and service composition. We designed an easy-to-use programming interface for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> to allow developers to program on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> . This paper describes the system design, implementation, and programming interface of Firework. The experimental results of a video analytics application demonstrate that <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> reduces up to 19.52 percent of response latency and at least 72.77 percent of network bandwidth cost, compared to a cloud-centric solution.

EdgePlace: Availability‐aware placement for chained mobile edge applications

AbstractMobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted European Telecommunications Standards Institute network function virtualization architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows a configurable placement policy to deploy mobile edge applications as virtual machines (VMs): affinity can be used to put VMs on the same host for inter‐VM networking performance, whereas anti‐affinity is to separate VMs for high availability. In this paper, we propose a novel model to track the availability and cost impact from placement policy changes of the mobile edge applications. We formulate our model as a stochastic programming problem. To minimize the complexity challenge, we also propose a heuristic algorithm called EdgePlace. With our model, the unit resource cost increases when there are less resources left on a host. Applying affinity would take up more resources of the host but saves network bandwidth cost because of colocation. When enforcing anti‐affinity, experimental results show increases of both availability and interhost network bandwidth cost. For applications with different resource requirements, our model is able to find their sweet points with the consideration of both resource cost and application availability, which is vital in a less robust MEC environment.

DivORAM: Towards a practical oblivious RAM with variable block size

Oblivious RAM (ORAM) is important for applications that require hiding access patterns. Many ORAM schemes have been proposed but most of them support only storing blocks of the same size. For the variable length data blocks, they usually fill them upto the same length before uploading, which leads to an increase in storage space and network bandwidth usage. To develop the first practical ORAM with variable block size, we proposed the “DivORAM” by remodeling the tree-based ORAM structure. It employs an additively homomorphic encryption scheme (Damgård–Jurik cryptosystem) executing at the server side to save the client computing overhead and the network bandwidth cost. As a result, it saves network bandwidth 30% comparing with Ring ORAM and 40% comparing with HIRB ORAM. Experiment results show that the response time of DivORAM is 10 × improved over Ring ORAM for practical parameters.

Fog-Assisted Operational Cost Reduction for Cloud Data Centers

In this paper, we intend to reduce the operational cost of cloud data centers with the help of fog devices, which can avoid the revenue loss due to wide-area network propagation delay and save network bandwidth cost by serving nearby cloud users. Since fog devices may not be owned by a cloud service provider, they should be compensated for serving the requests of cloud users. When taking economical compensation into consideration, the optimal number of requests processed locally by each fog device should be decided. As a result, existing load balancing schemes developed for cloud data centers can not be applied directly and it is very necessary to redesign a cost-ware load balancing algorithm for the fog-cloud system. To achieve the above aim, we first formulate a fog-assisted operational cost minimization problem for the cloud service provider. Then, we design a parallel and distributed load balancing algorithm with low computational complexity based on Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM). Finally, extensive simulation results show the effectiveness of the proposed algorithm.

A minimum cost cache management framework for information-centric networks with network coding

In recent years, the increasing demand for media-rich content has driven many efforts to redesign the Internet architecture. As one of the main candidates, information-centric network(ICN) has attracted significant attention, where in-network cache is a key component in different ICN architectures. In this paper, we propose a novel framework for optimal cache management in ICNs that jointly considers caching strategy and content routing. Specifically, our framework is based on software-defined networking (SDN) where a controller is responsible for determining the optimal caching strategy and content routing via linear network coding (LNC). For the proposed cache management framework, we first formulate an optimization problem to minimize the network bandwidth cost and cache cost by jointly considering caching strategy and content routing with LNC. We then develop an efficient network coding based cache management (NCCM) algorithm to obtain a near-optimal caching and routing solution for ICNs. We further derive the upper and lower bounds of the problem and conduct extensive experiments to compare the performance of the NCCM algorithm with these bounds. Simulation results validate the effectiveness of the NCCM algorithm and the framework.

HRSPC: a hybrid redundancy scheme via exploring computational locality to support fast recovery and high reliability in distributed storage systems

Replication and erasure codes are two popular schemes to provide fault tolerance in distributed storage systems. However, they both face some challenges when used in cloud storage. As the scale of data increases and failure becomes common in data centers, replication scheme suffers from high storage cost while erasure codes suffer from performance degradation and network bandwidth cost particularly under faulty conditions. In this paper, we present a novel scheme, named Hybrid Redundancy Scheme Plus Computing, or HRSPC for short, to achieve better performance and storage cost trade-off via exploiting the computational capability of storage nodes and taking advantage of both replication and erasure codes׳ features. It conducts degraded read or recovery with very low I/O cost and network bandwidth while the storage cost is relatively low, and thus to achieve high performance in both normal and faulty conditions. In addition, the reliability of HRSPC is higher than erasure codes and replication under similar configurations.

Reducing Network Bandwidth Cost on End User Resources Consumption in Cloud Environment

Cloud computing has been envisioned as the next generation and attributed to the increasing growth of mobile devices. Rapid increment, increase demands and bandwidth cost of access cloud resources became essential user perceive incur higher cost for utilizing cloud services. Since researchers have proposed new methods, service provider need to collaborate to ensure reasonable level of QoS. In various methods, bandwidth cost of service execution and delivery schemes have suggested service level agreement as a formal contract enforces resource level quality of service against the fee. However, not sufficient for the user in dynamic wireless domains in mobile cloud. This work is dedicated to the study of impact against ensure quality of cloud based application outsourcing that satisfies the bandwidth requirements for user. A key problem in application outsourcing in the cloud-based server is the problem of bandwidth consumption, which is critical to the bandwidth cost for utilizing service infrastructure. Using mathematics expression, methodology and algorithm we formulate the bandwidth consumption problem, taking into consideration of various metrics such as service level agreement, bandwidth allocation for user and bandwidth allocation threshold limit, trustworthy security for data storage. To employ user perceived performance in mobile application outsourcing for cloud-based server, which has always been an important aspect of bandwidth cost and quality of service.

Optimizing Cloud Bandwidth and Cost using Content Aware Chunk Prediction to Improve Efficiency of Retrieval

Bandwidth, an important most demanding computing resource is mainly focused on backup service in cloud computing technology. In addition to backup solutions, communication and transferring of data also involves a considerable amount of bandwidth for efficient throughput. Management of wide area network bandwidth provision and cost is a major challenge in recent periods. Due to enormous content in cloud, cost effective bandwidth provisioning solution should be adopted by the cloud providers for better user satisfaction. Here we use a min chunk hashing technique based on the content for preserving all chunks in order to reduce the overall transfer time for individuals as well as to reduce the resource allocation load on the cloud servers. Thus, content based chunking along with the metadata considerably reduces the cost of bandwidth utilization for individuals. The bandwidth usage is predicted using cloudsim simulator through BwProvisioner class provided in the cloudbus package. A sample of large text files with redundant data is refined, chunked and hashed using Message Digest5 to measure the transfer time and cost performance metrics for experimental values in a dynamic work load environment.

Real-Time QoS-Aware Video Streaming: A Comparative and Experimental Study

Due to its flexibility, scalability, real-time, and rich QoS features, Data Distribution Service (DDS) middleware provides seamless integration with high-performance, real-time, and mission-critical networks. Unlike traditional client-server communication models, DDS is based on the publish/subscribe communication model. DDS improves video streaming quality through its efficient and high-performance data delivery mechanism. This paper studies and investigates how DDS is suitable for streaming real-time full-motion video over a communication network. Experimental studies are conducted to compare video streaming using a the VLC player with the DDS overlay. Our results depict the superiority of DDS in provisioning quality video streams at the cost of low network bandwidth. The results also show that DDS is more scalable and flexible and is a promised technology for video distribution over IP networks where it uses much less bandwidth while maintaining high quality video stream delivery.