Bandwidth Resource Allocation Research Articles

Efficient Privacy Preserving Data Collection and Computation Offloading for Fog-Assisted IoT

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

Security and performance-aware resource allocation for enterprise multimedia in mobile edge computing

Mobile edge computing (MEC) is a promising computing model and has gained remarkable popularity, as it deploys the resources (e.g., computation, network, and storage) to the evolved NodeB (eNB) to provide enormous benefits such as low delay and energy consumption. More and more enterprises construct their edge computing platforms to store multimedia contents (i.e., video, audio, photos, and text data) for the user equipment (UE). However, both the eNB and UEs will experience serious security attacks when transmitting or receiving multimedia data via the wireless network. Existing MEC studies mainly focus on task offloading and performance improvement without considering the enterprise multimedia security problem. This paper proposes a security and performance-aware resource allocation (Spara) algorithm for enterprise multimedia in MEC environment. More specifically, we first build the architecture of enterprise multimedia security for sending the data requests to UEs, which mainly consists of computing and bandwidth resource allocation. Then, we formulate the stochastic data transmission problem to minimize the delay and energy consumption of UEs subject to the security guarantee. To achieve this goal, two queues, namely front-end queue and back-end queue, are used for each UE, and the Lyapunov optimization technique is applied to determine how to allocate the computing and bandwidth resources. Rigorous theoretical analysis shows that Spara algorithm meets the [O(1/V), O(V)] energy-delay tradeoff. Extensive simulation experiments validate this analysis result and the effectiveness of Spara algorithm.

Evaluation of Bandwidth Resource Allocation using Dynamic LSP and LDP in MPLS for Wireless Networks

Evaluation of Bandwidth Resource Allocation using Dynamic LSP and LDP in MPLS for Wireless Networks

Heterogeneous Network Bandwidth Control Scheme for the Hybrid OMA-NOMA System Platform

The emerging non-orthogonal multiple access (NOMA) has been considered as a promising technology to reach the goals of 5G cellular systems. Due to its superior spectral efficiency, the NOMA has attracted a lot of attention recently, and could play a vital role in improving the capacity of future Heterogeneous networks (HetNets). In this paper, we present a joint bandwidth control scheme, which incorporates both NOMA and orthogonal multiple access (OMA) modes into one unified scheme. For the effective collaboration of different multiple access methods, we adopt the ideas of cooperative bargaining solution concepts. In the proposed scheme, the bandwidth allocation process for base stations and channels follows the OMA mode, and mobile devices within a same channel form a group. The power level selection process for mobile devices in each group follows the NOMA mode. In the OMA mode, traditional Nash bargaining solution is adopted to allocate bandwidth resources. In the NOMA mode, groups of individual devices bargain both within and across groups based on the group bargaining solution. Under the dynamic changing HetNet environments, our joint bargaining approach takes various benefits in a rational way while maximizing the bandwidth efficiency. Comprehensive numerical experiments are provided to show that the performance of our proposed scheme is superior to existing HetNet spectrum control protocols.

Joint computation and bandwidth resources allocation for generalized computing model in mobile edge computing

Joint computation and bandwidth resources allocation for generalized computing model in mobile edge computing

Dynamic Bandwidth Scheduling of Software Defined Networked Collaborative Control System

From the perspective of collaborative design of control and scheduling, a software-defined networked collaborative control system (SD-NCCS) is established. On this basis, the system performance of SD-NCCS is analyzed and the average sensitivities of reference are used in utility function to evaluate the effects of network-induced delays. Moreover, the network pricing mechanism and game theory are introduced and a dynamic bandwidth resource allocation model of the overall control system for optimal performance is obtained. Thus, the problem of the network resource allocation of the SD-NCCS has been converted to be the problem of solving the Nash equilibrium point under the non-cooperative game model. Furthermore, the Nash equilibrium solution under this frame is obtained using the estimation distributed algorithm (EDA). Finally, a simple example is included to illustrate the performance of our scheme.

A Novel Markov Decision Process-Based Solution for Improved Quality Prioritized Video Delivery

The recent growth in both number of high specification mobile devices and network multimedia data demand, has made difficult supporting access to the multimedia content at high user Quality of Experience (QoE) levels. It is even more challenging to support offering such services adjusted to user specific requirements in current heterogeneous wireless network environments (HWNE). This paper proposes a novel Prioritized Adaptive Real-time Multi-user Access Network Selection framework (P-ARMANS) which employs a Markov Decision Process (MDP) solution to perform improved bandwidth resource allocation. P-ARMANS enables load balancing during multimedia delivery when users with diverse priority and different device screen resolutions access various services. Modeling and simulations show how P-ARMANS distributes content with different types of traffic and load among typical and business users at higher QoE levels compared to a classic no-priority approach.

Impact of the Maximum Number of Switching Reconfigurations on the Cost Saving in Network Function Virtualization Environments with Elastic Optical Interconnection

Network Function Virtualization is based on the virtualization of the network functions and it is a new technology allowing for a more flexible allocation of cloud and bandwidth resources. In order to employ the flexibility of the technology and to adapt its use according to the traffic variation, reconfigurations of the cloud and bandwidth resources are needed by means of both migration of the Virtual Machines executing the network functions and reconfiguration of circuits interconnecting the Virtual Machines. The objective of the paper is to study the impact of the maximum number of switch reconfigurations on the cost saving that the Networking Function Virtualization technology allows us to achieve. The problem is studied in the case of a scenario with an elastic optical network interconnecting datacenters in which the Virtual Machines are executed. The problem can be formulated as an Integer Linear Programming one introducing a constraint on the maximum number of switch reconfigurations but due to its computational complexity we propose a low computational complexity heuristic allowing for results close to the optimization ones. The results show how the limitation on the number of possible reconfigurations has to be taken into account to evaluate the effectiveness in terms of cost saving that the Virtual Machine migrations in Network Function Virtualization environment allows us to achieve.

An Improved Distributed Gradient-Push Algorithm for Bandwidth Resource Allocation over Wireless Local Area Network

Bandwidth allocation problems over wireless local area network have attracted extensive research recently due to the rapid growth in the number of users and bandwidth-intensive applications. In this paper, a bandwidth allocation problem over wireless local area network with directed topologies is investigated and the global objective function of the problem consists of local downloading and uploading cost with both constraints of feasible allocation region and network resources. An improved high-efficiency gradient-push algorithm is proposed for the bandwidth allocation problem which not only guarantees successful data transmission but also minimizes the global objective function. Compared with the existing distributed algorithms, firstly, we use weighted running average bandwidth to replace the current state variables which can ensure the solution converge to the optimal value asymptotically with probability one. Next, noisy gradient samples are used in the proposed algorithm instead of accurate gradient information which enhances the robustness and expands the scope of application. Theoretical analysis shows the convergence rate of the time-averaged value to the optimal solution. Finally, numerical examples are presented to validate the proposed algorithm.

Q-SQUARE: A Q-learning approach to provide a QoE aware UAV flight path in cellular networks

This paper deals with the adoption of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations providing video streaming services within a cellular macro area. We devise a Q-learning based UAV flight planning algorithm aimed at improving the Quality of Experience (QoE) of video users. Specifically, the proposed algorithm, herein denoted as Q-SQUARE, leverages the well-established Q-learning algorithm by introducing a reward related to a key QoE metric that is the video segment delay. The Q-SQUARE algorithm also accounts for different UAV recharging stations being available in the covered area. The performance analysis, as a function of the number of UAVs and recharging stations, show that Q-SQUARE identifies the UAV flight paths, i.e. specific space-time allocation of the available bandwidth resources, that definitely improve the QoE of the streaming services.

Effectiveness of Segment Routing Technology in Reducing the Bandwidth and Cloud Resources Provisioning Times in Network Function Virtualization Architectures

Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs.

Fog computing support scheme based on fusion of location service and privacy preservation for QoS enhancement

In the case of large-scale concurrent multimedia services, the resource utilization, QoS guarantee ability and system execution efficiency of distributed multimedia systems are facing many challenges. In order to further solve the above problems, this paper introduces fog computing into distributed multimedia systems and large-scale concurrent multimedia services, and considers the privacy protection of location services. Firstly, in order to solve the limitations of computing, storage, energy continuity, network access and other aspects of the fog node mobile devices, the framework of the fog computing multimedia information service system is constructed to obtain accurate location service data of the mobile terminal. At the same time, collaborative control of fog nodes, fog computing support nodes, public networks and other devices, to achieve the various functions of multimedia services. Secondly, to further improve the efficiency and security of fog computing, this section studies the time slice, bandwidth, hardware and other network resource allocation and management algorithms. Finally, the superiority and feasibility of the proposed algorithm in enhancing QoS performance are validated from different aspects of mathematical analysis and simulation experiments. For example, the resource utilization of multimedia system is increased by 1.34 times, and the load balancing capability and real-time guarantee are improved.

New dual-game-based cooperative bandwidth control scheme for ultra-dense networks

Future 5G cellular networks are being designed to address the explosive traffic growth of mobile users. In emerging new wireless system paradigms, the ultra-dense network (UDN) is considered to be one of the key technologies for potentially achieving the next generation network capacity. For UDN operations, the main challenge is to design efficient bandwidth resource allocation algorithms while considering quality-of-service provisioning. In this paper, we present an intelligent UDN bandwidth control scheme using efficient and innovative methodologies. Based on the basic ideas of cooperative game theory, the proposed scheme can adaptively assign the limited bandwidth resource for each small cell operator. By employing two different game solutions, the proposed approach is designed as a novel dual-game model. First, individual cell operators estimate their bandwidth requirements according to the iterative Nash bargaining solution. Then, limited bandwidth is distributed to each cell operator based on the τ-value. To reduce the computation complexity, the proposed algorithms are hierarchically implemented with cascade interactions. Through simulation results, we confirm that system throughput, bandwidth utilization, and load balancing among the cells can be improved with the proposed approach compared to the existing schemes. Finally, we propose further challenges and opportunities in the research area of UDN operations.

Multi-Topology Routing Algorithms in SDN-Based Space Information Networks

Aiming at the complex structure of the space information networks (SIN) and the dynamic change of network topology, in order to design an efficient routing strategy, this paper establishes a SIN management architecture based on Software-defined Networking (SDN). A routing algorithm flow of the spatial information network based on a snapshot sequence is designed. For different spatial tasks with different Quality of Service (QoS) requirements, the concept of integrated link weight is proposed. The Warshall–Floyd algorithm is used to design the optimal routing strategy. A Task-oriented Bandwidth Resource Allocation (TBA) algorithm is proposed for multiple spatial tasks in the same link. Simulation results show that the algorithm can effectively guarantee the priority transmission of important tasks and avoid the unnecessary waste of bandwidth resources.

Resource Allocation in Heterogeneous Cognitive Radio Network With Non-Orthogonal Multiple Access

In this paper, we study resource allocation problems for a two-tier cognitive heterogeneous network in interweave spectrum sharing mode. Secondary users (SUs) in small cells (SCs) opportunistically access the licensed spectrum resources. Non-orthogonal multiple access (NOMA) is used to boost the number of accessible SUs sharing the limited and dynamic licensed spectrum holes. Practically, there exists a tradeoff: an SC can increase its instantaneous sum throughput by accessing more idle bandwidth, which creates higher liability due to the dynamics of licensed spectrum and contention among the multiple SCs. Aiming to maximize the sum throughput of second-tier SCs network, we formulate a mixed integer non-linear programming problem with the constraints of the available idle bandwidth, the successive interference cancellation complexity, the transmission power budget, and the minimum data requirements. To efficiently solve this problem, we decompose the original optimization problem into bandwidth resource allocation subproblem, SUs clustering subproblem, and power allocation subproblem. Based on the scale of SCs network and the activities of licensed spectrum, we introduce an optimal bandwidth configuration to maximize the average sum throughput of SCs. By analyzing the derivation of the achievable rate expression of a NOMA-enabled SU, we develop a novel SUs clustering algorithm which can improve the throughput of a cluster by grouping SUs with more distinctive channel conditions. With the results of SUs clustering, we propose power allocation within a NOMA cluster by using Karush-Kuhn-Tucker optimality conditions. Furthermore, we perform power allocation across NOMA clusters by using the difference of convex programming. The simulation results validate the performance of the proposed resource allocation algorithms.

Computing and Bandwidth Resource Allocation in Multi-Provider NFV Environment

We propose an algorithm for the cloud and bandwidth resource allocation in multi-provider NFV environments. The resources are allocated so as to take into account the different costs charged by the cloud infrastructure providers (InP). The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal problem. Its application in medium and large networks has shown that it can lead to cost saving as high as 65% with respect to algorithms that allocate resources without taking into account the cost differences charged by the InPs.

Resource allocation and distributed uplink offloading mechanism in fog environment

Applying fog computing technology to the shared pattern has two problems to cope with. One is to formulate a rational mechanism for resource allocation, and the other is to design computation offloading strategy of tasks based on resource allocation result. For solving these problems, we construct a three-layer F-RAN architecture first, which consists of terminal layer, access layer and network layer. Second, we adopt differential game and bipartite graph multiple matching algorithm to solve bandwidth resource allocation problem of fog node (FN)-access point (AP) and AP-shared terminal (ST), respectively. Third, Lyapunov theory and proposed deviation update decision algorithm (DUDA) are used to solve computation offloading decision-making and offloading update order-making. At last, simulation results show that our strategy can save 30%–60% system consumption, and the resource demand satisfaction rate can be guaranteed to reach 80% or more.

Joint Resource Allocation for Software-Defined Networking, Caching, and Computing

Although some excellent works have been done on networking, caching, and computing, these three important areas have traditionally been addressed separately in the literature. In this paper, we describe the recent advances in jointing networking, caching, and computing and present a novel integrated framework: software-defined networking, caching, and computing (SD-NCC). SD-NCC enables dynamic orchestration of networking, caching, and computing resources to efficiently meet the requirements of different applications and improve the end-to-end system performance. Energy consumption is considered as an important factor when performing resource placement in this paper. Specifically, we study the joint caching, computing, and bandwidth resource allocation for SD-NCC and formulate it as an optimization problem. In addition, to reduce computational complexity and signaling overhead, we propose a distributed algorithm to solve the formulated problem, based on recent advances in alternating direction method of multipliers (ADMM), in which different network nodes only need to solve their own problems without exchange of caching/computing decisions with fast convergence rate. Simulation results show the effectiveness of our proposed framework and ADMM-based algorithm with different system parameters.

Regional Load Balancing Circuitous Bandwidth Allocation Method Based on Dynamic Auction Mechanism

Aiming at the problem of link congestion caused by the shortage of network bandwidth resources at the user end, this paper first proposes a regional load balancing idea. Then, for the problem of bandwidth resource allocation in regional load balancing, a bandwidth allocation model is established and a dynamic auction algorithm is proposed. The algorithm calculates the link quality and stability by constructing a link model, and introduces the auction bandwidth to the auctioneer's incentive degree to obtain the auction bidding function. The simulation results show that the algorithm can effectively improve the user's network status, reduce the service response delay, increase the throughput, and at the same time can effectively prevent the auction user's false bidding behavior, so that the auction quote quickly converges to the maximum quote, reduces the number of auctions, and reduces Communication overhead.

Distributed Service Function Chaining

Middleboxes have become a vital part of modern networks by providing service functions such as content filtering, load balancing and optimization of network traffic. An ordered sequence of middleboxes composing a logical service is called service chain. Service Function Chaining (SFC) enables us to define these service chains. Recent optimization models of SFCs assume that the functionality of a middlebox is provided by a single software appliance, commonly known as Virtual Network Function (VNF). This assumption limits SFCs to the throughput of an individual VNF and resources of a physical machine hosting the VNF instance. Moreover, typical service providers offer VNFs with heterogeneous throughput and resource configurations. Thus, deploying a service chain with custom throughput can become a tedious process of stitching heterogeneous VNF instances. In this paper, we describe how we can overcome these limitations without worrying about underlying VNF configurations and resource constraints. This prospect is achieved by distributed deploying multiple VNF instances providing the functionality of a middlebox and modeling the optimal deployment of a service chain as a mixed integer programming problem. The proposed model optimizes host and bandwidth resources allocation, and determines the optimal placement of VNF instances, while balancing workload and routing traffic among these VNF instances. We show that this problem is NP-Hard and propose a heuristic solution called Kariz. Kariz utilizes a tuning parameter to control the trade-off between speed and accuracy of the solution. Finally, our solution is evaluated using simulations in data-center networks.