QoS-Aware Dynamic Resource Management in Heterogeneous Mobile Cloud Computing Networks

In mobile cloud computing(MCC) systems,both the mobile access network and the cloud computing network are heterogeneous,implying the diverse configurations of hardware,software,architecture,resource,etc.In such heterogeneous mobile cloud(HMC) networks,both radio and cloud resources could become the system bottleneck,thus designing the schemes that separately and independently manage the resources may severely hinder the system performance.In this paper,we aim to design the network as the integration of the mobile access part and the cloud computing part,utilizing the inherent heterogeneity to meet the diverse quality of service(QoS)requirements of tenants.Furthermore,we propose a novel cross-network radio and cloud resource management scheme for HMC networks,which is QoS-aware,with the objective of maximizing the tenant revenue while satisfying the QoS requirements.The proposed scheme is formulated as a restless bandits problem,whose "indexability" feature guarantees the low complexity with scalable and distributed characteristics.Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing ones.

Resource Allocation for Physical Layer Security in Heterogeneous Network with Hidden Eavesdropper

The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.

Context-Oriented Multi-RAT User Association and Resource Allocation with Triple Decision in 5G Heterogeneous Networks

In the upcoming 5 G heterogeneous networks, leveraging multiple radio access technologies(RATs) shows to be a crucial issue in achieving RAT multiplexing gain to meet the explosive traffic demand. For always best connection(ABC), users tend to activate parallel transmission across all available RATs. However from a system-wide perspective, this might not be optimal given the context of network load, interference and diverse service requirements. To intelligently determine how to use these multi-RAT access resources concurrently, this paper proposes a joint multi-RAT user association and resource allocation strategy with triple decision and integrated context awareness of users and networks. A dynamic game based ant colony algorithm(GACA) is designed to simultaneously maximize the system utility and the fairness of resource allocation. Simulation results show that it's more reasonable to make multi-RAT association decision from a system-wide viewpoint than from an individual one. Compared to max-SNR based and ABC based strategies, the proposed method alleviates network congestion and optimizes resource allocation. It obtains 39%~70% performance improvement.

A Resource Scheduling Scheme for Spectrum Aggregation in Cognitive Radio Based Heterogeneous Networks

In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.

Resource Control Architecture in Heterogeneous Networks

TISPAN,from a fixed access perspective,proposes Resource and Admission Control Subsystem[0](RACS) as a solution to Quality of Service(QoS) problem for NGN bearer network.In contrast,3GPP has an approach to this from the perspective of mobile access.In the latest 3GPP R7 draft,integration of Policy Control Function(PCF) with Flow Based Charging(FBC) function of the R6 brought forward policy control and charging.With the development of fixed mobile convergence,the inconsistence in architectures and interfaces of different resource and admission control[0] solutions will have a huge impact on manufacture and network implementation of NGN related equipment.To solve this problem,both 3GPP and TISPAN have been working on the convergence of Gq’/Rx reference points.Harmonized Policy Control and Charging(PCC) proposed by the Next Generation Mobile Network(NGMN) forum,i.e.cooperative resource control architecture for heterogeneous networks,represents an evolutional sign post for resource control technology for heterogeneous network architecture.

An Enhanced Spectrum Resource Allocation Algorithm in Hetnets

Efficient spectrum resource allocation in wireless heterogeneous networks is important for improving the system throughput and guaranteeing the user's Quality-of-Service(QoS).In this paper,we propose an enhanced algorithm for spectrum resource allocation in heterogeneous networks.First,the bandwidth of each user is determined by the user's rate demand and the channel state.Second,graph theory is enhanced and used to improve the spectrum efficiency.Third,spectrum resource is dynamically split between macrocell and femtocells with the changes of users' conditions.Our simulation results show that the proposed algorithm improves the system throughput significantly and also guarantees the fairness for the users.

Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency

More demand-side flexible resources(DFRs)are participating in the frequency regulation of renewable power systems,whose heterogeneous characteristics have a significant impact on the system frequency response.Consequently,selecting suitable DFRs poses a formidable challenge for independent system operators(ISO).In this paper,a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system frequency.Firstly,a performance curve is developed to describe the cost,capacity,and response speed of DFRs.Moreover,a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty coefficients.Then,the frequency security criterion considering DFRs’performance is constructed,whose linearity makes it can be easily coupled into the system scheduling model and solved.Furthermore,a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency security.Finally,the model is transformed into mixed integer second-order cone programming(MISOCP)and solved by the commercial solver.The proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

Intelligent Scheduling Strategies for Computing Power Resources in Heterogeneous Edge Networks

The edge computing model enables real-time and low-power processing of data,while contributing to data security and privacy protection.However,the heterogeneity and diversity of edge computing devices pose a great challenge to task scheduling and migration.Most of the existing studies only consider the allocation of computational resources,but lack comprehensive consideration of data resources,storage space,etc.In this paper,we proposed intelligent scheduling strategies for computing power resources in heterogeneous edge networks.We define the relevant models and construct a comprehensive matching matrix in terms of task matching with computing resources,data resources,storage resources,load balancing of computing devices and storage space matching,and design an intelligent scheduling algorithm based on iteration and load balancing according to the matching degree of tasks and computing devices in the heterogeneous edge network environment.The iterative and load-balanced scheduling algorithm is based on the least-cost flow solution scheduling strategy,which effectively reduces the task computation response time and improves the computation and storage resource utilization of computing devices.Experimental validation of the proposed intelligent scheduling strategy is carried out based on a simulation environment.The experimental results show that the proposed intelligent scheduling strategy has obvious advantages over random scheduling methods in terms of task processing delay,computing power resource utilization and number of satisfactory tasks.

Minimizing Energy Cost for Green Data Center by Exploring Heterogeneous Energy Resource

With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering this issue, many geographically distributed data centers are attempting to use clean energy as their energy supply, such as fuel cells and renewable energy sources. However, not all workloads can be powered by a single power sources, since different workloads exhibit different characteristics. In this paper, we propose a fine-grained heterogeneous power distribution model with an objective of minimizing the total energy costs and the sum of the energy gap generated by the geographically distributed data centers powered by multiple types of energy resources. In order to achieve these two goals, we design a two-stage online algorithm to leverage the power supply of each energy source. In each time slot, we also consider a chance-constraint problem and use the Bernstein approximation to solve the problem. Finally, simulation results based on real-world traces illustrate that the proposed algorithm can achieve satisfactory performance.

Genetic algorithm for autonomic joint radio resource management in end-to-end reconfigurable systems

This article presents the genetic algorithm （GA） as an autonomic approach for the joint radio resource management （JRRM） amongst heterogeneous radio access technologies （RATs） in the end-to-end reconfigurable systems. The joint session admission control （JOSAC） and the bandwidth allocation are combined as a specific decision made by the operations of the genetic algorithm with certain advisable modifications. The proposed algorithm is triggered on the following two conditions When a session is initiated, it is triggered for the session to camp on the most appropriate RAT and select the most suitable bandwidth for the desired service. When a session terminates, it is also used to adjust the distribution of the ongoing sessions through the handovers. This will increase the adjustment frequency of the JRRM controller for the best system performance. Simulation results indicate that the proposed autonomic JRRM scheme not only effectively reduces the handover times, but also achieves well trade-off between the spectrum utility and the blocking probability.

The relative and combined effects of herbivore assemblage and soil nitrogen on plant diversity

Plant diversity can be affected by both herbivore grazing and soil resources. However, it is unclear if the joint effects of herbivores and soil resources might vary with components of plant diversity. Here, we evaluated the relative and combined effects of herbivore assemblage and soil nitrogen(N) quantity and heterogeneity on the α and β components of plant diversity in a grassland that was subjected to four years of grazing under differing herbivore assemblages(no grazing, cattle grazing, sheep grazing, and mixed grazing). We found that herbivore assemblage combined with soil N quantity explained 41% of the variation in plant α-diversity, while herbivore assemblage combined with soil N heterogeneity explained 15% of the variation in plant β-diversity. The independent effects of herbivore assemblage explained more than those of soil N for both α-and β-diversity(α-diversity: 12% vs. 4%;β-diversity: 18% vs. 16%). We concluded that the effects of herbivores are stronger than those of soil N,and that grazing-induced changes in soil resources are important drivers of plant diversity change, especially α-diversity.Therefore, we suggest that managing herbivore species by accounting for the effects that their grazing can have on soil resources may be significant for plant diversity maintenance.

Multi-radio access based bandwidth allocation strategy for video communication in heterogeneous wireless networks

In order to make full use of the radio resource of heterogeneous wireless networks（HWNs） and promote the quality of service（Qo S） of multi-homing users for video communication, a bandwidth allocation algorithm based on multi-radio access is proposed in this paper. The proposed algorithm adopts an improved distributed common radio resource management（DCRRM） model which can reduce the signaling overhead sufficiently. This scheme can be divided into two phases. In the first phase, candidate network set of each user is obtained according to the received signal strength（RSS）. And the simple additive weighted（SAW） method is employed to determine the active network set. In the second phase, the utility optimization problem is formulated by linear combining of the video communication satisfaction model, cost model and energy efficiency model. And finding the optimal bandwidth allocation scheme with Lagrange multiplier method and Karush-Kuhn-Tucker（KKT） conditions. Simulation results show that the proposed algorithm promotes the network load performances and guarantees that users obtain the best joint utility under current situation.