Virtual Resource Allocation Research Articles

Operator Profit-Aware Wireless Virtualization for Device-to-Device Communications Underlaying LTE Networks

This paper investigates the operator profit from virtual resource allocation in device-to-device (D2D) communications underlaying long-term evolution networks. By utilizing the power-bandwidth product model as the metric of operator cost, the virtual resource allocation is formulated as a binary integer programming to maximize the difference between the sum rate of all the users and the consumed resources. To reduce the complexity, the formulated problem is decomposed to two subproblems, and for each subproblem, a heuristic algorithm is proposed to transfer the complexity of exponent into polynomial. Simulation results show that the proposed scheme is close to the optimal solution with lower complexity. Meanwhile, it also outperforms the benchmark in terms of the total profit, and enables the desired tradeoff between the operator cost and sum rate of both cellular and D2D users.

Energy-efficient virtual network embedding in networks for cloud computing

Cloud computing is based on several service models such as Network as a Service (NaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). NaaS is a business model that aims to provide virtual network (VN) services over the internet from shared cloud data centres. Virtual network embedding (VNE) is one of the core technologies in NaaS resource allocation. In view of the enormous energy consumption by numerous cloud data centres, energy-efficient VNE becomes a new research focus. In this paper, we propose an energy efficient virtual network embedding (EEVNE) approach for cloud computing networks, in which power savings are introduced by consolidating resources in the network and data centres. EEVNE is based on the evaluation of the energy consumption by hosts, the allocation of virtual resources both in nodes and links, as well as the cost and revenue in network virtualisation. We also formulated a heuristic embedding algorithm based on Group Search Optimiser (GSOVNE) to solve the NP-hard problem. Furthermore, a reconfiguration algorithm is developed to improve the utility of fragmented resources. Simulation experiments show that the proposed algorithm can effectively increase VN acceptance ratio and reduce energy consumption when large quantities of virtual networks arrive and depart over time.

공유 백업 대역폭을 갖는 다중 경로 지원 가상 네트워크 자원 할당 방안

인터넷의 성장은 다양한 분야에서 정보통신 기술의 활용한 새로운 서비스를 창출하고 있지만, 인터넷의 구조 적인 문제로 인하여 다양한 네트워크 구조와 서비스의 발전을 저해하고 있다. 네트워크 가상화는 현재 인터넷의 구 조적 문제를 극복할 수 있는 대안으로 연구되고 있으며, 많은 연구 분야 중에서도 가상 네트워크 자원 할당 방안이 중요 연구 분야중의 하나이다. 본 논문에서는 가상 네트워크의 신뢰성을 향상시켜 네트워크상에서의 단일 링크 장애 를 복구할 수 있는 방안을 제안한다. 다수의 가상 네트워크가 하나의 공유 백업 대역폭을 이용하여 다중 경로를 지원 함으로 신뢰성을 향상시킬 수 있다. 가상 네트워크들이 백업자원을 공유함으로 한정된 물리 자원의 효율성을 향상시 키고, 사전에 할당된 백업 자원을 이용하여 빠른 장애 복구가 가능하다는 장점이 있다. 실험을 통하여 제안된 알고리 즘의 다중 경로 생성 소요 시간이 기존 연구보다 약 50% 정도 성능 향상되었음을 확인하였다.

Virtual Resource Allocation in Software-Defined Information-Centric Cellular Networks With Device-to-Device Communications and Imperfect CSI

In this paper, we propose an architecture of software-defined information-centric network virtualization with device-to-device (D2D) communications, which facilitates dynamic virtual resource allocation and content caching via a software-defined networking (SDN) controller with a global view of the system. In our proposed framework, substrate physical resources can be virtualized and shared among multiple mobile virtual network operators (MVNOs). Meanwhile, by means of integrating D2D communications into information-centric wireless networks, content caching is enabled not only in the air but in mobile devices as well. In addition, taking into consideration inaccurate channel estimation and measurement, we formulate the virtual resource allocation and caching optimization as a discrete stochastic optimization problem in which imperfect channel state information is incorporated. Because the formulated virtual resource allocation problem is a large-scale combinational optimization problem, we exploit discrete stochastic approximation approaches to cope with it. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme with different system parameters. Simulation results show that MVNOs can benefit from not only the sharing of physical infrastructure but from the caching functionality that exists in both the air and mobile devices as well.

Virtual Resource Allocation in Information-Centric Wireless Networks With Virtualization

Wireless network virtualization and information-centric networking (ICN) are two promising technologies in next-generation wireless networks. Traditionally, these two technologies have been addressed separately. In this paper, we show that jointly considering wireless network virtualization and ICN is necessary. Specifically, we propose an information-centric wireless network virtualization framework for enabling wireless network virtualization and ICN. Then, we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering not only the revenue earned by serving the end users but the cost-of-leasing infrastructure as well. In addition, with recent advances in distributed convex optimization, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation and in-network caching scheme. Simulation results are presented to show the effectiveness of the proposed scheme.

A three-dimensional virtual resource scheduling method for energy saving in cloud computing

Cloud computing is growing in popularity among computing paradigms, but in practice the energy consumption of the cloud data centers is very high. In this paper, we build the resource model of the cloud data center and the dynamic power model of the physical machine, and then propose a three-dimensional virtual resource scheduling method for energy saving in cloud computing (TVRSM), in which the process of virtual resource scheduling are divided into three stages: virtual resource allocation, virtual resource scheduling and virtual resource optimization. For the different objective of each stage, we design three different algorithms, respectively. The simulation results prove that the TVRSM is able to efficiently allocate and manage the virtual resources in the cloud data center. And compared with other traditional algorithms, the TVRSM can effectively reduce the energy consumption of the cloud data center and significantly minimize the amount of violations of Service Level Agreement (SLA).

Information-Centric Virtualized Cellular Networks With Device-to-Device Communications

Information-centric wireless network virtualization enables the sharing of not only the infrastructure but also the content among different service providers, enabling the gain of not only virtualization but in-network caching as well. In this paper, we propose a novel framework with information-centric wireless virtualization and device-to-device (D2D) communications, which enables content caching not only in the air but in the mobile device as well. Moreover, the content is virtualized and can be shared by users from different virtual service providers. In this framework, we formulate the virtual resource allocation and caching strategies as a joint optimization problem, considering the gain of not only virtualization but of caching in the proposed information-centric wireless network virtualization architecture with D2D communications as well. 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 parties only need to solve their own problems without the exchange of channel state information (CSI) with fast convergence rate. Extensive simulations are conducted with different system parameters to show the effectiveness of the proposed scheme.

Need for a Transport API in 5G for Global Orchestration of Cloud and Networks Through a Virtualized Infrastructure Manager and Planner [Invited

The new 5G paradigm seeks for a scalable architecture that is able to efficiently manage the increasing volume of traffic generated by smart devices to be processed in a distributed cloud infrastructure. To this end, coordinated management of the network and the cloud resources forming an end-to-end system is of great importance. Software defined networking and network function virtualization architectures are the key enablers for integrating network and cloud resources, enabling cross optimization on both sides. This optimization requires efficient resource allocation algorithms, which take into account computing and network resources. In this paper, we propose an end-to-end orchestration architecture for distributed cloud and network resources aligned with the European Telecommunications Standards Institute management and orchestration architecture. The proposed architecture includes the virtual infrastructure manager and planner (VIMaP) component to enable dynamic resource allocation for interconnected virtual instances in distributed cloud locations. A heuristic algorithm for dynamic virtual machine graphs resource allocation is included to validate the VIMaP architecture and exploit its functionalities. Moreover, the control orchestration protocol is included between the architecture components to offer end-to-end transport services. Finally, the proposed architecture is experimentally validated, and the heuristic algorithm performance is evaluated.

Delay-Optimal Virtualized Radio Resource Scheduling in Software-Defined Vehicular Networks via Stochastic Learning

Due to the high density of vehicles and various types of vehicular services, it is challenging to guarantee the quality of vehicular services in current Long-Term Evolution (LTE) networks in a cost-efficient manner. Fortunately, with the development of fifth-generation (5G) technology, the installation of a large number of small cells is foreseen as one of the practical ways to achieve the low-delay requirement in vehicular environments. However, it may cause a huge operating expense and capital expenditure to mobile network operators due to the limited backhaul capacity and the explosion of signaling. In this paper, we integrate software-defined networking and radio resource virtualization into an LTE system for vehicular networks, i.e., software-defined heterogeneous vehicular network (SERVICE) . Based on this proposed system framework, a delay-optimal virtualized radio resource scheduling scheme is proposed via stochastic learning. The delay optimal problem is formulated as an infinite-horizon average-cost partially observed Markov decision process (POMDP). Then, an equivalent Bellman equation is derived to solve it. The proposed scheme can be divided into two stages, i.e., macro virtualization resource allocation (MaVRA) and micro virtualization resource allocation (MiVRA). The former is executed based on large timescale variables (traffic density), whereas the latter is operated according to short timescale variables (channel state and queue state). Simulation results show that the proposed scheme outperforms traditional schemes.

Distributed Resource Allocation in Virtualized Full-Duplex Relaying Networks

One significant challenge of wireless virtualization is how to allocate the virtual resources efficiently with various constraints and requirements. Most existing works have not considered relaying in wireless virtualization. However, relaying is regarded as one of the key components of future wireless networks. Furthermore, recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Different from existing works, we first introduce the idea of wireless virtualization into FDR networks and propose a virtual resource management architecture for virtualized FDR networks. Then, we formulate the virtual resource allocation as an optimization problem while taking the residual self-interference due to FDR and the requirements of wireless virtualization into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem, it involves high computational complexity to solve it. To solve it efficiently, we transform the original problem to a convex optimization problem, and an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation algorithm is developed to solve the convex problem. Extensive simulations are presented to show the effectiveness of the proposed scheme. It is demonstrated that the proposed virtualized FDR network is able to exploit the advantages of FDR and wireless virtualization.

A Differentiated Virtual Resource Allocation Strategy Based on Game Model for Multi-tenant Applications

Resource allocation for multi-tenancy application is one key issue for cloud service providers. To deal with the competitive problem of limited resources among tenants, this paper proposes a resource allocation strategy based on game model, which combines tenants’ SLA and their resource utilization, in order to deal with the shortcomings of the traditional allocation strategies that consider few about differentiated resource requirements. A framework for multi-tenant resources allocation based on game is presented, and the corresponding game model is established. The related processes auction and resource allocation are designed. The experimental results indicate that it can meet the personalized and differentiated demand of multi-tenant better, and help to improve the system’s overall resource utilization and decrease the operating costs of cloud service providers.

Equilibrium Price and Dynamic Virtual Resource Allocation for Wireless Network Virtualization

Economic and technical features are equally important to radio resource allocation in wireless network virtualization (WNV). Regarding virtual resource (VR) as commodity, this paper proposes an effective VR allocation scheme for WNV from the perspective of the market-equilibrium theory. First, physical meaning clear utility functions are defined to characterize the network benefits of user equipments (UEs), infrastructure providers (InPs) and virtual network operators (VNOs) in WNV. Then, the VR allocation problem between one InP and multiple VNOs is formulated as a multi-objective optimization problem. To reduce the algorithm complexity, the multiple-objective problem is first decoupled into two single-objective sub-problems. The supplier-layer sub-problem aims to maximize the benefit of the unique InP, while the customer-layer sub-problem aims to maximize the benefits of the multiple VNOs. Both of the separated sub-problems are solved by using standard convex optimization method, and are combined by searching for the equilibrium-price (EP) of the VR market. As a result, the Pareto optimal solution of the original multi-objective problem is found, at which no one (the InP or anyone of the VNOs) can increase its benefit by deviating the EP without hurting others’ benefits. The effectiveness of the proposed VR allocation scheme is testified through extensive experiments.

Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of network function virtualization (NFV) has emerged and become a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for virtual network functions (VNFs) creates more flexible and dynamic network services to meet a more diversified demand. Software-oriented network functions bring along a series of research challenges, such as VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with NFV infrastructure, among others. In this paper, we study the VNF scheduling problem and the corresponding resource optimization solutions. Here, the VNF scheduling problem is defined as a series of scheduling decisions for network services on network functions and activating the various VNFs to process the arriving traffic. We consider VNF transmission and processing delays and formulate the joint problem of VNF scheduling and traffic steering as a mixed integer linear program. Our objective is to minimize the makespan/latency of the overall VNFs’ schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, and cater to services with stringent delay requirements, thereby increasing operators’ revenues. Owing to the complexity of the problem, we develop a genetic algorithm-based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical evaluation. We show that dynamically adjusting the bandwidths on virtual links connecting virtual machines, hosting the network functions, reduces the schedule makespan by 15%–20% in the simulated scenarios.

Distributed Virtual Resource Allocation in Small-Cell Networks With Full-Duplex Self-Backhauls and Virtualization

Wireless network virtualization has attracted great attention from both academia and industry. Another emerging technology for next-generation wireless networks is in-band full-duplex (FD) communications. Due to its promising performance, FD communication has been considered to be an effective way of achieving self-backhauls for small cells. In this paper, we introduce wireless virtualization into small-cell networks and propose a virtualized small-cell network architecture with FD self-backhauls. We formulate the virtual-resource-allocation problem in virtualized small-cell networks with FD self-backhauls as an optimization problem. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem, its computational complexity is high. Moreover, the centralized scheme may suffer from signaling overhead, outdated dynamics information, and scalability issues. To solve it efficiently, we divide the original problem into two subproblems. For the first subproblem, we transfer it to a convex optimization problem and then solve it by an efficient alternating direction method of multipliers (ADMM)-based distributed algorithm. The second subproblem is a convex problem, which can be solved by each infrastructure provider. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

Virtual Machine Resource Allocation of Probabilistic Optimization Based on SME Algorithm

To further enhance optimizing effect of virtual machine resource allocation, virtual machine resource allocation algorithm of probabilistic optimization based on SME-FFD (Simulated Evolution – First Fit Decreasing ) is proposed aiming at NP hard optimization problem in the process of virtual machine resource allocation in cloud computing. First of all, an optimization evaluation scheme of virtual machine resource allocation is proposed, and strong climbing optimization ability of simulated evolutionary algorithm is adopted to carry out iterative evolution to selection, evaluation and ranking of virtual machine resource allocation ; after that, based on SME algorithm obtaining resource allocation ordering, the secondary allocation on virtual machine and physical host resources ranked is conducted using FFD to improve efficiency and effectiveness of resource allocation; in the end, experimental comparison is conducted in CloundSim grid lab in the University of Melbourne and gridbus cloud simulation platform, the results show that CPU usage rate of proposed algorithm reaches up to 47%, memory usage rate reaches up to 56%, therefore, it may effectively reduce physical machine usage quantity and realize the goal of energy consumption.

Virtualized Resource Allocation Algorithm in Mobile Internet

Virtualized Resource Allocation Algorithm in Mobile Internet

Virtual code resource allocation for energy-aware MTC access over 5G systems

The enormous traffic of machine-type communications (MTC) expected over 5G exacerbates the limitations of access schemes currently under investigation in the literature. This scenario becomes more challenging when considering smart city environments, which introduce further issues due to the heterogeneity in the level of residual battery energy of involved machines. Novel solutions are, therefore, required, which aim at drastically reducing the collision probability of devices with critical level of residual battery energy. In this paper, we propose a virtual code resource allocation (VCRA) approach which extends the code-expanded strategy to support a high number of devices simultaneously accessing the system. Besides, a virtual resource allocation scheme to guarantee energy-priority in the access procedure is introduced. The idea behind our proposal is the definition of different access levels that exploit disjoint sets of access codewords, properly tailored to guarantee high capacity for each access level. Simulation results show to the effectiveness of our scheme in terms of (i) reducing the collision probability of machines with limited battery capabilities also in scenarios with very high cell load and (ii) enhancing the efficiency with respect to legacy code-expanded strategy.

VM Consolidation for Cloud Data Center Using Median Based Threshold Approach

Cloud computing is an on demand computing model which requires large amount of physical devices and provide services to users on the basis of pay per usage model, therefore excessive demand of cloud computing have also led to the growth of computational power inside datacenters. These datacenters consumes huge amount of energy which results high carbon emission. For the optimization of resources and reduction of energy consumption, virtual machine consolidation can be used by switching the idle nodes to sleep mode or by turning them off and by using live migration of virtual machines. Here, we propose a novel method for consolidation of virtual machines such that it meets Service Level Agreements (SLA) and deals with energy-performance trade-off. Therefore, reduction of SLA violation and minimize the performance degradation during migration are two main objectives in this paper. For the allocation and reallocation of virtual resources depending upon their load, this threshold based approach can be used, in which Median method is used to find lower and upper threshold values. Proposed Median based threshold approach is implemented by using CloudSim and validation of this approach is performed across different workload traces of PlanetLab servers and using some random configuration of Datacenters. Experimental results show that this scheme can provide better SLA performance.

Backhaul-Aware User Association and Resource Allocation for Energy-Constrained HetNets

Growing attentions have been paid to renewable energy or hybrid energy powered heterogeneous networks (HetNets). In this paper, focusing on backhaul-aware joint user association and resource allocation for this type of HetNets, we formulate an online optimization problem to maximize the network utility reflecting proportional fairness. Since user association and resource allocation are tightly coupled not only on resource consumption of the base stations (BSs), but also in the constraints of their available energy and backhaul, the closed-form solution is quite difficult to obtain. Thus, we solve the problem distributively via employing some decomposition methods. Specifically, at first, by adopting primal decomposition method, we decompose the original problem into a lower-level resource allocation problem for each BS, and a higher-level user association problem. For the optimal resource allocation, we prove that a BS either assigns equal normalized resources or provides equal long-term service rate to its served users. Then, the user association problem is solved by Lagrange dual decomposition method, and a completely distributed algorithm is developed. Moreover, applying results of the subgradient method, we demonstrate the convergence of the proposed distributed algorithm. Furthermore, in order to efficiently and reliably apply the proposed algorithm to the future wireless networks with an extremely dense BS deployment, we design a virtual user association and resource allocation scheme based on the software-defined networking architecture. Lastly, numerical results validate the convergence of the proposed algorithm and the significant improvement on network utility, load balancing and user fairness.

A bargaining game theoretic method for virtual resource allocation in LTE-based cellular networks

In this paper, we study the virtual resource (VR) allocation problem in LTE-based wireless network virtualization (WNV). A practical network scenario, where multiple virtual wireless service providers (WSPs) request the VR from a unique mobile network operator (MNO) is considered. Our objective is two folds. The first is to guarantee the minimum rate requirements of the MNO and the WSPs. The second is to distribute the system rate among the MNO and the WSPs in the Pareto optimal manner. To this end, an efficient VR allocation scheme based on bargaining game theory is proposed, and the Nash bargaining solution (NBS) method is used to solve the proposed game problem. The proposed game problem is proved to be a convex optimization problem. By using standard convex optimization method, the global optimal NBS of the game is obtained in closed form. The effectiveness of the proposed VR allocation game is testified through numerical results.