Towards Flexible Guarantees in Clouds: Adaptive Bandwidth Allocation and Pricing

In this paper we propose a general utility-oriented adaptive Quality of Service (QoS) model for wireless networks and establish a framework for formulating the bandwidth allocation problem for users with time-varying links. For slow link variations, it is inadequate to only employ low-level adaptive mechanisms at the symbol or packet level, such as error correetion coding or swapping packet transmission opportunities. To improve bandwidth utilization and satisfy users' QoS requirements, high-level adaptive mechanisms working at larger time scale is needed. We propose an adaptive bandwidth allocation scheme, which is capable of providing QoS guarantees, ensuring long-term fairness, and achieving high bandwidth utilization. A finite-state Markov channel model (FSMC) is used to model wireless links.

Bandwidth allocation and admission control scheme based on Nash bargaining solution for WiMAX systems

A novel multibeam satellite system design is proposed in this paper based on jointly optimizing power and carrier allocation in order to best match the asymmetric traffic requests. This design introduces higher and asymmetric interference levels throughout the coverage. However, both power and bandwidth will be used more efficiently. Even though the problem of power and bandwidth allocation has been addressed in terrestrial wireless communications, it is new in satellite systems and since architecture and channel are different, available results and algorithms are not applicable to satellite payload systems. In this paper we formulate the resource allocation problem as max-min SINR balancing based on the recently introduced axiomatic-based interference model, but in addition, we also optimize the carrier allocation when performing the SINR balancing problem. An analytical solution for the optimal carrier allocation is proposed and we iteratively find the optimal power allocation for each beam. The Shannon (upper bound) and current state-of-the art PHY layer technology: DVB-S2 are proposed to be implemented in order to obtain the gap between them. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio comparing with conventional system, which is designed based on uniform bandwidth and power allocation.

Heterogeneous networks (HetNets) composed of macrocells and small cells are expected to improve the transmission performance of users significantly. Designing efficient resource allocation schemes, specifically, bandwidth allocation and power allocation schemes for HetNets is of particular importance for it may affect user quality of service (QoS) and network performance severely. In this paper, we consider the resource allocation problem of a HetNet composed of a macrocell and a number of femtocells and propose a resource characteristic and user QoS oriented bandwidth and power allocation algorithm for the femto base stations (FBSs) which share spectrum with the macro base station. By taking into account the service requirements of the femto user equipments (FUEs) and the bandwidth resource characteristics of the network, various bandwidth resource allocation schemes are proposed. Particularly, in the case of slightly insufficient bandwidth resource, we propose a bankruptcy game based bandwidth resource allocation algorithm for the FBSs, and solve the bandwidth allocation problem by means of Shapley value method. To stress the tradeoff between data rate and power consumption, we examine the energy efficiency of the FBSs and formulate the power allocation problem as a multi-objective optimization problem with the objectives of maximizing the energy efficiency of all the FBSs. The optimal power allocation strategy can be obtained by solving the optimization problem via ideal point method and generic algorithm. Simulation results demonstrate the efficiency of the proposed algorithm.

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.

A new economic generalized particle model for flow control

In this research, we focused on fair bandwidth allocation on the Internet. The Internet provides communication services based on exchanged packets. The bandwidth available for each customer is often fluctuated. Fair bandwidth allocation is an important issue for ISPs to gain customer satisfaction. Static bandwidth allocation allows an exclusive bandwidth for specific traffic. Although it gives communications a QoS guarantee, it requires muany bandwidth resources as known as over-provisioning. In contrast with static dynamic control allocates bandwidth resources dynamically. It therefore utilizes bandwidth use more effectively. However, it needs control overhead in monitoring traffic and estimating the optimum allocation. The Transmission Control Protocol, or TCP is the dominant protocol on the Internet. It is also equipped with a traffic-rate-control mechanism. An adaptive bandwidth-allocation mechanism must control traffic that is under TCP Rapid feedback makes it possible to gain an advantage over TCP In this paper, we propose an Adaptive Bandwidth Allocation (ABA) mechanism as a feedback system for MPLS. Our proposal allows traffic to be regulated adaptively as its own weight value which can be assigned by administrators. The feedback bandwidth allocation in the previous work needs control delay in collecting network status along the communication path. We call this round-trip feedback control. Our proposal, called feedback control, collects network status in half the time of roundtrip delay. We compare the performance of our one-way feedback-based mechanism and traditional feedback control under a simulation environment. We demonstrate the advantages of our rapid feedback control has using experimental results.

Bandwidth allocation plays an important role to guarantee the quality of service (QoS) in streaming real-time scalable video to multiuser over wireless time-varying channels. In this letter, an adaptive bandwidth allocation (ABA) scheme is presented to maximize the bandwidth utilization while provisioning each individual user’s QoS. Cooperated with traffic rate adaptation, it jointly addresses the spatial diversity among multiuser and the temporal fluctuation of the individual channel condition. A continuous-time Markov decision processes model is introduced to formulate the QoS-guaranteed ABA problem as constrained stochastic optimization. Based on performance sensitivity analysis, a policy iteration algorithm is developed to find the optimal ABA policy online without any prior knowledge of channel statistics. The adaptability to unknown environments and high computation efficiency make it feasible to implement in running-time. Simulations results demonstrate the effectiveness of the proposed method.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of flows transferring data between two stages of a job. The job cannot start its next stage unless all flows in the coflow finish. To improve the execution performance of such a job, it is crucial to reduce the completion time of a coflow, as it can contribute more than 50 percent of the job completion time. While several coflow schedulers have been proposed, we observe that routing, as a factor greatly impacting the Coflow Completion Time (CCT), has not been well considered. In this article, we focus on the coflow scheduling problem and jointly consider routing and bandwidth allocation. We begin by providing an analytical solution to the problem of optimal bandwidth allocation with pre-determined routes. In the following, we formulate the problem of scheduling a single coflow as a Non-linear Mixed Integer Programming problem and present its relaxed convex optimization problem. We further propose two algorithms, CoRBA and its simplified version: CoRBA-fast that solve the single coflow scheduling problem with a joint consideration of routing and bandwidth allocation. Lastly, to address multiple coflows in online scheduling, we propose an online scheduler named OnCoRBA. By comparing with the start-of-the-art algorithms and schedulers via simulations, we demonstrate that CoRBA and CoRBA-fast reduce the CCT by 30-400 percent and the OnCoRBA scheduler reduces the average online CCT by 20-230 percent. In addition, CoRBA-fast can be hundreds times faster than CoRBA with around 8 percent performance degradation compared to CoRBA, which makes the use of CoRBA-fast very appropriate in practice.

Efficient and optimized data transfer in broadband is the anchor point of digital economy in modern era. One the most challenging issues for providing different services in broadband is the bandwidth allocation. That is a real-time multi dimensional problem solving requiring to satisfy QOS and SLA constraints at the same time. In this paper, we present a real-time adaptive framework for the problem of optimal bandwidth allocation to broadband services in Iran. Our approach for the allocation is based on Network Utility Maximization. In this way, the optimal bandwidth is the one which maximizes network users' utility. For this purpose, the bandwidth is dynamically allocated to the services with highest importance for the user (according to the SLA) and the remaining bandwidth is most appropriately allocated to the less important services. Each service has its own (non-linear) utility function which is considered in each allocation cycle. The utility values are calculated in real-time and the bandwidth allocation pattern which maximizes the total users' utilities is selected. Simulation results showed satisfactory bandwidth allocation patterns in different scenarios according to the network utility and user preferences in SLA.

In this paper, a priority-based bandwidth allocation in the context of heterogeneous wireless network is studied. By utilizing the mobile terminals with multi-homing capability, which simultaneously accesses bandwidth from different wireless network, a novel utility-based bandwidth allocation approach is introduced. An essential factor is the priority mechanism considering the services priority classification, which means the higher-priority service request can be guaranteed on bandwidth allocation prior to the lower-priority service request. The utility function is used to distinguish both the service priority and the network subscribers compared to other users. The bandwidth allocation problem is formulated as an optimization problem and solved by Lagrangian duality. Numerical results reveal that the optimal bandwidth allocation algorithm can differentiates the priority-based service and utilize all available resources within the network capacity.

In this paper, we propose an adaptive bandwidth allocation (BA) and connection admission control (CAC) mechanism based on game theory for polling services in IEEE 802.16 broadband wireless networks. A noncooperative two-person general-sum game is formulated where the base station and a new connection are the players of this game. The game formulation provides not only the decision on accepting or rejecting a connection, but also the amount of bandwidth allocated to a new connection (if admitted). A queueing model considering adaptive modulation and coding (AMC) in the physical layer is used to analyze quality of service (QoS) performances, namely, delay and throughput performances, respectively, for real-time and non-real-time polling services. This queueing model is used by the proposed bandwidth allocation and admission control game to ensure that the payoffs for both the base station and the new connection are maximized. The performance of the proposed scheme is evaluated by simulation and compared with that of traditional admission control with static and adaptive bandwidth allocation.

A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)–based hybrid detector from the composite probability distribution of the compound observations received by the fusion center, and then analyze its asymptotic detection performance. Subsequently, we optimize the sensor-wise quantization thresholds to achieve near-optimal asymptotic performance at the local sensor level. Moreover, we propose a mixed-integer linear programming approach to solve the optimization problem of transmission bandwidth allocation accounting for bandwidth constraints and error-prone channels. Finally, simulation results demonstrate the superiority of the proposed hybrid detector and the bandwidth allocation strategy, especially in challenging error-prone channel conditions.

Smart camera networks (SCNs) are increasingly used in applications such as homeland security, border control and traffic monitoring. The cameras are often wireless with an organically growing structure, to reduce the overhead of deployment. We frame a new and important problem in SCNs: how to transmit videos from multiple cameras with overlapping coverage given the limited available wireless bandwidth to maximize the quality of the received videos. We call this problem Coordinated Transport of Correlated Videos (CTCV). CTCV is a more general version of 3D video transport: in that problem, highly correlated videos from two cameras (that provide the 3D perspective) are jointly encoded exploiting their pre-defined and known overlap. In contrast, in CTCV there is an arbitrary number of cameras whose overlap is not known apriori and that require transmission as multiple video streams. To effectively support CTCV, we propose a video delivery protocol that consists of two primary components: (1) Consolidation of correlated videos from multiple cameras which removes spatially redundant fields-of-view; and (2) Network and coverage aware bandwidth allocation to optimize coverage quality cooperatively among the different video streams to match the available bandwidth. We formulate the problem of optimal bandwidth allocation for maximizing coverage. We propose and investigate different heuristic policies for bandwidth allocation. We evaluate CTCV using data from a small camera testbed as well as topologies from realistic deployments. Experiments show that CTCV achieves around 10 dB gains in video quality in the scenarios we consider.