Load-Balancing Based Cross-Layer Elastic Resource Allocation in Mobile Cloud

The paper proposes a hybrid mobile cloud computing system, in which mobile applications can use different resources or services in local cloud and remote public cloud such as computation, storage and bandwidth. The cross-layer load-balancing based mobile cloud resource allocation optimization is proposed. The proposed approach augments local cloud service pools with public cloud to increase the probability of meeting the service level agreements. Our problem is divided by public cloud service allocation and local cloud service allocation, which is achieved by public cloud supplier, local cloud agent and the mobile user. The system status information is used in the hybrid mobile cloud computing system such as the preferences of mobile applications, energy, server load in cloud datacenter to improve resource utilization and quality of experience of mobile user. Therefore, the system status of hybrid mobile cloud is monitored continuously. The mathematical model of the system and optimization problem is given. The system design of load-balancing based cross-layer mobile cloud resource allocation is also proposed. Through extensive experiments, this paper evaluates our algorithm and other approaches from the literature under different conditions. The results of the experiments show a performance improvement when compared to the approaches from the literature.

     

Cross‐Layer Resource Allocation Scheme for WLANs with Multipacket Reception

Tailored for wireless local area networks, the present paper proposes a cross‐layer resource allocation scheme for multiple‐input multiple‐output orthogonal frequency‐division multiplexing systems. Our cross‐layer resource allocation scheme consists of three stages. Firstly, the condition of sharing the subchannel by more than one user is studied. Secondly, the subchannel allocation policy which depends on the data packets’ lengths and the admissible combination of users per subchannel is proposed. Finally, the bits and corresponding power are allocated to users based on a greedy algorithm and the data packets’ lengths. The analysis and simulation results demonstrate that our proposed scheme not only achieves significant improvement in system throughput and average packet delay compared with conventional schemes but also has low computational complexity.     

基于终端能耗和系统时延最小化的边缘计算卸载及资源分配机制

通过移动边缘计算下移云端的应用功能和处理能力支撑计算密集或时延敏感任务的执行成为当前的发展趋势。但面对众多移动终端用户时,如何有效利用计算资源有限的边缘节点来保障终端用户服务质量(QoS)成为关键问题。为此,该文融合边缘云与远端云构建了一种分层的边缘云计算架构,以此架构为基础,以最小化移动设备能耗和任务执行时间为目标,将问题形式化描述为资源约束下的最小化能耗和时延加权和的凸优化问题,并提出基于乘子法的计算卸载及资源分配机制解决该问题。实验结果表明,在计算任务量很大的情况下,提出的计算卸载及资源分配机制能够有效降低移动终端能耗,并在任务执行时延方面较局部计算与计算卸载机制分别降低最高60%与10%,提高系统性能。     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.     

Penalty‐aware and cost‐efficient resource management in cloud data centers

Today, data centers are the main source of providing cloud services through a service level agreement (SLA). Most research papers for cloud resource management concentrate on how to reduce host energy consumption and SLA violation (SLAV) to minimize operational cost. However, they do not consider the amount of penalty that cloud provider should pay to users because of SLAV. In this paper, we propose a new penalty‐aware and cost‐efficient method that considers cloud resource management as a cost problem. In this method parameters such as user budget, penalty, and host energy consumption cost play an important role in minimizing operational cost which leads to higher profit for cloud provider. The simulation results with CloudSim show that our proposed method minimizes operational cost compared to the prior resource managements. Copyright © 2016 John Wiley & Sons, Ltd.     

A self‐organized resource allocation scheme for heterogeneous macro‐femto networks

This paper investigates the radio resource management (RRM) issues in a heterogeneous macro‐femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user‐deployed femtos is not known a‐priori. This makes interference management crucial. In particular, with co‐channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross‐layer and co‐layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro‐femto network. Then, we propose a self‐organized resource allocation (SO‐RA) scheme for an orthogonal frequency division multiple access based macro‐femto network to mitigate co‐layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse‐1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse‐1, while the SO‐RA scheme achieves improved throughput and fairness performance. SO‐RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state‐of‐the‐art resource allocation schemes.Copyright © 2014 John Wiley & Sons, Ltd.     

基于二次规划的任务卸载决策和资源分配方法

主要研究移动用户均有多个独立任务的多用户移动云计算系统,这些移动用户将任务卸载到云端时共享通信资源。如何对所有用户的任务卸载决策和通信资源分配进行联合优化,以便使所有用户的能耗、计算量和延时降到最低是目前研究的难点。将该问题建模为NP难度的非凸的具有二次约束的二次规划(QCQP)问题,提出一种高效的近似算法进行求解,通过单独的半正定松驰(SDR)处理后,确定二元卸载决策和通信资源最优分配。采用代表最小系统成本的性能下界作为性能基准进行仿真实验,结果表明,本文算法在多种参数配置下的性能均接近最优性能。     

Survey on computation offloading in mobile edge computing

Computation offloading in mobile edge computing would transfer the resource intensive computational tasks to the edge network.It can not only solve the shortage of mobile user equipment in resource storage,computation performance and energy efficiency,but also deal with the problem of resource occupation,high latency and network load compared to cloud computing.Firstly the architecture of MEC was introduce and a comparative analysis was made according to various deployment schemes.Then the key technologies of computation offloading was studied from three aspects of decision on computation offloading,allocation of computing resource within MEC and system implement of MEC.Based on the analysis of MEC deployment scheme in 5G,two optimization schemes on computation offloading was proposed in 5G MEC.Finally,the current challenges in the mobility management was summarized,interference management and security of computation offloading in MEC.     

A hierarchical approach for resource allocation in hybrid cloud environments

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.     

多小区蜂窝网络中计算卸载与资源分配联合优化算法

本文在多基站和远端云构成的多层计算卸载场景中,提出了一种多小区蜂窝网络中基站选择、计算卸载与资源分配联合优化算法。该算法考虑多基站重叠覆盖用户的基站选择,在边缘服务器计算资源约束条件下,构建了能耗与时延加权和的最小化问题,这是NP-hard问题。本文首先对单用户多基站计算卸载问题,采用拉格朗日乘子法对其进行求解;然后针对多用户多基站场景,考虑用户的基站选择以及边缘服务器计算资源的竞争,基于定义的选择函数对接入基站进行选择,采用次优的迭代启发式算法对单用户场景下的卸载决策做出动态修正,获得卸载决策和边缘服务器资源分配。仿真结果表明,提出的计算卸载及资源分配算法能有效的降低任务完成的时延与终端的能耗。      

An autonomic risk‐ and penalty‐aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioning

Cloud computing environment allows presenting different services on the Internet in exchange for cost payment. Cloud providers can minimize their operational costs by auto‐scaling of the computational resources based on demand received from users. However, the time and cost required to increase and decrease the number of active computational resources are among the biggest limitations of scalability. Thus, auto‐scaling is considered as one of the most important challenges in the field of cloud computing. The present study aimed to present a new solution to automatic scalability of resources for multilayered cloud applications under the Monitor‐Analysis‐Plan‐Execute‐Knowledge loop. In addition, the Google penalty payment model was used to model the penalty costs in the problem and to accurately evaluate the earned profit. A hybrid resource load prediction algorithm was proposed to evaluate the future of resources in each cloud layer. Further, we used statistical solution to determine the statuses of VMs in addition to presenting a risk‐aware algorithm to allocate the user requests to active resources. The experimental results by Cloudsim indicated the improvement of the proposed approach in terms of operational costs, the number of used resources, and the amount of profit.     

A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Mobile cloud computing is a promising approach to improve the mobile device's efficiency in terms of energy consumption and execution time. In this context, mobile devices can offload the computation‐intensive parts of their applications to powerful cloud servers. However, they should decide what computation‐intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP‐complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best‐possible partitioning for offloading to multisite clouds/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best‐possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.     

Energy‐efficient resource allocation for device‐to‐device communication through noncooperative game theory

Wireless network with high data rate applications has seen a rapid growth in recent years. This improved quality of service (QoS) leads to huge energy consumption in wireless network. Therefore, in order to have an energy‐efficient resource allocation in cellular system, a device‐to‐device (D2D) communication is the key component to improve the QoS. In this paper, we propose a noncooperative game (NCG) theory approach for resource allocation to improve energy efficiency (EE) of D2D pair. A three‐tier network with macrocell base station (MBS), femtocell base station (FBS), and D2D pair is considered, which shares the uplink resource block. A resource allocation strategy with constraints is arrived, which maintains minimum throughput for each user in the network. The proposed resource allocation strategy optimizes the EE of D2D pair in the three‐tier network, which achieves Nash equilibrium (NE) and Pareto optimality (PO). Simulation results validate that EE is uniform and optimum for all D2D pair, which converges to NE when channel is static and it converges to PO when the channel is dynamic.     

QoS‐aware cross‐domain collaborative energy‐saving mechanism for FiWi virtual networks

The fiber‐wireless (FiWi) access network is a very promising solution for next‐generation access networks. Because of the different protocols between its subnets, it is hard to globally optimize the operation of FiWi networks. Network virtualization technology is applied to FiWi networks to realize the coexistence of heterogeneous networks and centralized control of network resource. The existing virtual resource management methods always be designed to optimize virtual network (VN) request acceptance rate and survivability, but seldom consider energy consumption and varied requirements of quality of service (QoS) satisfaction, which is a hot and important topic in the industrial field. Therefore, this paper focuses on the QoS‐aware cross‐domain collaborative energy saving mechanism for FiWi virtual networks. First, the virtual network embedding (VNE) model, energy consumption model, and VNE profit model of FiWi networks are established. Then, a QoS‐aware in‐region VN embedding mechanism is proposed to guarantee service quality of different services. After that, an underlying resource updating mechanism based on energy efficiency awareness is designed to realize low‐load ONU and wireless routers co‐sleep in FiWi networks. Finally, a QoS‐aware re‐embedding mechanism is presented to allocate proper resource to the VNs affected by the sleeping mechanism. Especially for video VNs, a re‐embedding scheme which adopts traffic splitting and multipath route is introduced to meet resource limitation and low latency. Simulation results show that the proposed mechanism can reduce FiWi network's energy consumption, improve VNE profit, and ensure high embedding accepting rate and strict delay demand of high‐priority VNs.     

A joint resource allocation‐channel coding design based on distributed source coding

Wireless sensor networks (WSNs) have found a wide variety of applications recently. However, the challenges in WSNs still remain in improving the sensor energy efficiency and information quality (distortion reduction) of the sensing data transmissions. In this paper, we propose a novel cross‐layer design of resource allocation and channel coding to protect distributed source coding (DSC)‐based data transmission. Resource allocation strategies include rate adaptation and automatic repeat‐request retransmissions. Our proposed joint design of resource allocation, channel coding, and DSC can improve the network energy efficiency and information quality while meeting the data transmission latency requirements. Further, we investigate how the resource allocation enables the network to achieve unequal error protection among correlated DSC streams. Our simulation studies demonstrate that the proposed joint design significantly improves the DSC‐based data transmission quality and the network energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd.     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

Multilayer context cloud framework for mobile Web 2.0: a proposed infrastructure

Existing context‐aware systems focus only on characterizing the situation of an entity to exhibit the advantage of contextual information association, but they have no mechanism to facilitate the interoperation and reuse of contextual information. Cloud computing offers an adaptable and flexible solution for existing context‐aware applications, integrating Mobile Web 2.0 technologies. This work presents a multilayer context cloud framework (MCCF) that integrates Web 2.0 technologies into a mobile context‐aware system for use in a cloud computing environment. The proposed MCCF includes a context sensor layer, a context information layer, a context service layer, a context representation layer, a cloud computing layer, and a mobile Web 2.0 context‐aware Software as a Service layer. To demonstrate the feasibility of this approach, a Mobile Web 2.0‐based context‐aware Software as a Service platform, which is a cloud computing application based on MCCF, is implemented to provide continuous and context‐aware monitoring of a specific application. Copyright © 2011 John Wiley & Sons, Ltd.     

Energy‐efficient device‐to‐device communication using adaptive resource‐block allocation

Device‐to‐device (D2D) communication in the fifth‐generation (5G) wireless communication networks (WCNs) reuses the cellular spectrum to communicate over the direct links and offers significant performance benefits. Since the scarce radio spectrum is the most precious resource for the mobile‐network operators (MNOs), optimizing the resource allocation in WCNs is a major challenge. This paper proposes an adaptive resource‐block (RB) allocation scheme for adequate RB availability to every D2D pair in a trisectored cell of the 5G WCN. The hidden Markov model (HMM) is used to allocate RBs adaptively, promoting high resource efficiency. The stringent quality‐of‐service (QoS) and quality‐of‐experience (QoE) requirements of the evolutionary 5G WCNs must not surpass the transmission power levels. This is also addressed while using HMM for RB allocation. Thus, an energy‐efficient RB allocation is performed, with higher access rate and mean opinion score (MOS). Cell sectoring effectively manages the interference in the 5G networks amid ultrauser density. The potency of the proposed adaptive scheme has been verified through simulations. The proposed scheme is an essential approach to green communication in 5G WCNs.     

Design of energy‐efficient cloud systems via network and resource virtualization

Data centers play a crucial role in the delivery of cloud services by enabling on‐demand access to the shared resources such as software, platform and infrastructure. Virtual machine (VM) allocation is one of the challenging tasks in data center management since user requirements, typically expressed as service‐level agreements, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors to greenhouse gas emissions of IT services. In this paper, we propose a holistic approach for a large‐scale cloud system where the cloud services are provisioned by several data centers interconnected over the backbone network. Leveraging the possibility to virtualize the backbone topology in order to bypass IP routers, which are major power consumers in the core network, we propose a mixed integer linear programming (MILP) formulation for VM placement that aims at minimizing both power consumption at the virtualized backbone network and resource usage inside data centers. Since the general holistic MILP formulation requires heavy and long‐running computations, we partition the problem into two sub‐problems, namely, intra and inter‐data center VM placement. In addition, for the inter‐data center VM placement, we also propose a heuristic to solve the virtualized backbone topology reconfiguration computation in reasonable time. We thoroughly assessed the performance of our proposed solution, comparing it with another notable MILP proposal in the literature; collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization and fairness for medium size data centers. Copyright © 2013 John Wiley & Sons, Ltd.     

A coordinated multi‐point‐based quality of service provision resource allocation scheme with inter‐cell interference mitigation

The paper investigates resource allocation via power control for inter‐cell interference (ICI) mitigation in an orthogonal frequency division multiple access‐based cellular network. The proposed scheme is featured by a novel subcarrier assignment mechanism at a central controller for ICI, which is further incorporated with an intelligent power control scheme. We formulate the system optimization task into a constrained optimization problem for maximizing accepted users' requirements. To improve the computation efficiency, a fast yet effective heuristic approach is introduced for divide and conquer. Simulation results demonstrate that the proposed resource allocation scheme can significantly improve the network capacity compared with a common approach by frequency reuse. Copyright © 2014 John Wiley & Sons, Ltd.