A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

异构蜂窝网络中分层任务卸载：建模与优化

为提高计算任务卸载的效率,提出了一种基于D2D通信、移动边缘计算和云计算的分层任务卸载框架,并引入D2D协作中继技术辅助用户接入远端计算资源。针对所提任务卸载框架在多用户场景中可能存在上行通信拥塞、边缘计算资源受限、D2D复用干扰和云计算回程时延等问题,设计了一种基于博弈论的卸载调度和负载均衡方案,充分利用了所提任务卸载框架中各层计算和通信资源。仿真结果表明,所提方案能够有效降低端到端时延和卸载能耗,并在资源受限的条件下具有良好的稳定性。     

云计算环境下能量感知的任务调度算法

云计算环境下传统独立任务调度算法容易导致较高资源能耗或较大任务时间跨度.针对该问题,文中提出了两种能量感知的任务调度算法,并利用遗传算法并行化搜索合理调度方案.两种算法在搜索过程中,分别通过能耗时间归一和能耗时间双适应度方法定义适应度函数并进行个体选择.仿真结果表明,与单独考虑时间或能耗相比,这两种算法能够更有效地缩短任务执行时间跨度,降低资源能耗.     

EcoPlan: energy-efficient downlink and uplink data transmission in mobile cloud computing

Mobile cloud computing (MC2) is emerging as a new computing paradigm that seeks to augment resource-constrained mobile devices for executing computing- and/or data-intensive mobile applications. Nonetheless, the energy-poverty nature of mobile devices has become a stumbling block that greatly impedes the practical application of MC2. Fortunately, for delay-tolerant mobile applications, energy conservation is achievable via two means: (1) dynamic selection of energy-efficient links (e.g., WiFi interface); and (2) deferring data transmission in bad connectivity. In this paper, we study the problem of energy-efficient downlink and uplink data transmission between mobile devices and clouds. In the presence of unpredictable data arrival, network availability and link quality, our objective is to minimize the time average energy consumption of a mobile device while ensuring the stability of both device-end and cloud-end queues. To achieve this goal, we propose an online control framework named EcoPlan under which mobile users can make flexible link selection and data transmission scheduling decisions to achieve arbitrary energy-delay tradeoffs. Real-world trace-driven simulations demonstrate the effectiveness of EcoPlan, along with its superior energy-efficiency over alternative WiFi-prioritized, minimum-delay and SALSA schemes.     

Phased Scheduling for Resource-Constrained Mobile Devices in Mobile Cloud Computing

Mobile cloud computing combines wireless access service and cloud computing to improve the performance of mobile applications. Mobile cloud computing can balance the application distribution between the mobile device and the cloud, in order to achieve faster interactions, battery savings and better resource utilization. To support mobile cloud computing, the paper proposes a phased scheduling model of mobile cloud such that mobile device’s users experience lower interaction times and extended battery life. The phased scheduling optimization is solved by two subproblems: mobile device’s batch application optimization and mobile device’s job level optimization. At the first stage, the mobile cloud global scheduling optimization implements the allocation of the cloud resources to the mobile device’s batch applications. At the second stage, mobile device’s job level optimization adjusts the cloud resource usages to optimize the utility of single mobile device’s application. In the simulations, compared with other algorithm, our proposed mobile cloud phased scheduling algorithms achieve the better performance with acceptable overhead.     

Improving Energy Efficiency of Centrally Controlled Wireless Data Networks

Wireless network access protocols can assist nodes to conserve energy by identifying when they can enter low energy states. The goal is to put all nodes not involved in a transmission into the doze state. However, in doing so, one must tradeoff the energy and other costs associated with the overhead of coordinating dozing with the energy savings of putting nodes to sleep. In this paper, we define three alternative directory protocols that may be used by a central node to coordinate the transmission of data and the dozing of nodes. We attempt to optimize their performance by using scheduling and protocol parameter tuning. In addition, we consider the impact of errors and error recovery methods on energy consumption. Although one can argue that carefully scheduling transmissions will improve performance, ultimately, appropriately tuning protocols reduces scheduling's significance. In most cases, scheduling transmissions between the same nodes contiguously and ordering such transmissions shortest processing time first results in good performance. The most critical feature that contributes to an access protocol's effectiveness is its ability to minimize the time it takes to inform nodes that they may doze. However, the ability of our protocols to conserve energy is highly dependent on (1) network size, (2) traffic type (e.g., down/uplink, and peer-to-peer) and (3) channel bit error rate. In particular, we show that when protocols are faced with packet errors, more elaborate schemes to coordinate the dozing of nodes can pay-off. We conclude by recommending an energy conserving implementation of the IEEE 802.11 Point Coordination Function.     

Uplink scheduling scheme for cloud service based on software-defined wireless network

A LTE uplink scheduling scheme matching the features of wireless cloud services was proposed for the SDWN (software-defined wireless network). The scheme first solved the resource allocation problem by using the binary integer programming method, and then calculated the optimal transmission rate of cloud services in each time slot using the method of dynamic programming, finally adjusted the transmission rate of cloud services proportional to the current channel status using QoS control method in the framework of SDWN. The proposed scheme minimizes the energy con-sumption of cloud services while ensuring the transmission rate demand of multiple services. The performance of the al-gorithm is verified by simulation.     

Optimal energy-efficient pair-wise cooperative transmission scheme for wimax mesh networks

Recently, there has been a steady trend toward the development of subscriber stations (SSs) to enable the ubiquitous communications. In the mobile environment, the power consumption of an SS is an important performance indicator because its battery life is limited. Many existing power-saving schemes for the IEEE 802.16e Mobile WiMax system have been proposed, such as scheduling algorithms for the sleep intervals. However, this type of approaches may be unrealistic for heavy network traffic since the SSs almost always have data to transmit. In this paper, we propose a new power-saving scheme by introducing the pair-wise matching procedure between the SSs prior to uplink data transmission in the WiMax mesh mode. According to the quality-of-service (QoS) requirement, we can preset the desired packet error rate (PER) or signal-to-interference-plus-noise-ratio (SINR) at the receiver end. Given the network topology and the channel state information, the required transmitting power per coded bit at each SS can be calculated. Then we may establish a cost function associated with the required transmitting power per coded bit and thus the optimal matching can be achieved by our proposed minimum weight matching algorithm. The numerical results show the significant improvement of the transmitting power consumption using our proposed method over the conventional scheme when we consider three aspects such as channel effects, coding/modulation options and network topology.     

Energy-aware scheduling with probabilistic deadline constraints in wireless networks

In a shared-medium wireless network, an effective technique that allows for a tradeoff of message transmission time for energy savings is to transmit messages over multiple smaller hops as opposed to using the long direct source-destination hop. In this context, we address the problem of scheduling messages with probabilistic deadline constraints. Unlike most other works in this area, we consider the practical aspects of the erroneous channel condition and the receiver energy consumption while solving the scheduling problem. Our solution is three fold – first we prove that the problem is NP-hard. We then present an Integer Linear Program (ILP) formulation for the scheduling problem. Finally, we present efficient heuristic scheduling algorithms which minimize the energy consumption while providing the required guarantees. Our simulation studies show that the proposed heuristic algorithms achieve energy savings comparable to that obtained using the linear programming methodology under practical channel conditions.     

Reducing Power Consumption of Wireless Networks Through Collaborative DMC Mobile Clusters

Reducing the energy consumption of the wireless networks is significantly important for the economic and ecological sustainability of the ICT industry, as high energy consumption may limit the performance of wireless networks, and is one of the main network costs. To solve the energy consumption problem, especially on the terminal side, a scheme known as distributed mobile cloud (DMC) is considered to be a potential solution. Multiple mobile terminals (MTs) can cooperatively take advantage of good quality links among the MTs to save energy when receiving from the Base Station. In this paper, we aim to find the optimal transmit power to further reduce the energy consumption of DMC. From simulation studies, it is shown that up to 80% energy savings can be accomplished when using optimal transmit power, compared to using the standard DMC without exploring the optimal transmit power.     

LTE系统上行调度算法研究

无线资源调度是LTE系统研究的一个热点问题。L11E上行链路采用了单载波正交频多址（sc—FDMA）技术,要求在调度时为任一用户分配的RB必须是连续的,使得LTE下行调度算法不能直接应用上行调度。本文对LTE系统上行链路资源调度问题的研究状况进行了概述,分别从信道感知、比例公平、QoS保证三个方面,对现有调度算法进行了分析和比较。重点分析比较了各个算法在保证分配的RB连续的前提下系统性能,并提出了进一步研究的方向。     

Joint optimal power control and beamforming in wireless networksusing antenna arrays

The interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. The minimum variance distortionless response beamformer maximizes the signal-to-interference-and-noise ratio (SINR) when it is employed in the receiver of a wireless link. In a system with omnidirectional antennas, power control algorithms are used to maximize the SINR as well. We consider a system with beamforming capabilities in the receiver, and power control. An iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that it converges to the jointly optimal beamforming and transmission power vector. The algorithm is distributed and uses only local interference measurements. In an uplink transmission scenario, it is shown how base assignment can be incorporated in addition to beamforming and power control, such that a globally optimum solution is obtained. The network capacity and the saving in mobile power are evaluated through numerical study     

Energy‐efficient power control for uplink spectrum‐sharing heterogeneous networks

This article considers energy‐efficient power control schemes for interference management in uplink spectrum‐sharing heterogeneous networks that maximize the energy efficiency of users, protect the macro base station, and support users with QoS consideration. In the first scenario, we define the objective function as the weighted sum of the energy efficiencies and develop an efficient global optimization algorithm with global linear and local quadratic rate of convergence to solve the considered problem. To ensure fairness among individual user equipments (UEs) in terms of energy efficiency, we consider the max‐min problem, where the objective is defined as the weighted minimum of the energy efficiencies, and a fractional programming theory and the dual decomposition method are jointly used to solve the problem and investigate an iterative algorithm. As by‐products, we further discuss the global energy efficiency problem and consider near‐optimal schemes. Numerical examples are provided to demonstrate significant improvements of the proposed algorithms over existing interference management schemes.     

Provisioning multimedia wireless networks for better QoS: RRM strategies for 3G W-CDMA

Third-generation mobile communication systems will bring a wide range of new services with different quality of service requirements and will open the ability to exploit radio resource management functions to guarantee a certain target QoS, to maintain the planned coverage area and to offer a high-capacity while using the radio resources in an efficient way. RRM functions impact the overall system efficiency and the operator infrastructure cost, so they will definitively play an important role in a mature 3G scenario. In order to provide some insight into radio resource management (RRM) strategies implementation, a range of representative case-studies with several innovative algorithms are presented and supported by simulation results in a realistic UMTS Terrestrial Radio Access Network scenario as devised in the 3GGP standardization forum. In particular, a decentralized uplink transmission rate selection algorithm in the short term, a congestion control mechanism to cope with overload situations, and downlink scheduling for layered streaming video packets are proposed.     

移动Ad Hoc云中任务卸载的联合优化策略

为了降低移动Ad Hoc云中客户端卸载计算密集型任务过程中产生的计算能耗、传输能耗和任务时延,该文提出了一种联合优化算法。该算法首先基于计算能耗、通信能耗及任务时延进行建模;然后进行预估计,以选择更优的代理终端,并由此降低总的系统能耗与任务时延。仿真结果表明,相对于传统云算法,该算法在系统能耗和任务时延两方面均有显著提升。     

Instruction Scheduling for Low Power

Reducing energy consumption has become an important issue in designing hardware and software systems in recent years. Although low power hardware components are critical for reducing energy consumption, the switching activity, which is the main source of dynamic power dissipation in electronic systems, is largely determined by the software running on these systems.In this paper, we present and evaluate several instruction scheduling algorithms that reorder a given sequence of instructions taking into account the energy considerations. We first compare a performance-oriented scheduling technique with three energy-oriented instruction scheduling algorithms from both performance (execution cycles of the resulting schedules) and energy consumption points of view. Then, we propose three scheduling algorithms that consider energy and performance at the same time. Our experimentation with these scheduling techniques shows that the best scheduling from the performance perspective is not necessarily the best scheduling from the energy perspective. Further, scheduling techniques that consider both energy and performance simultaneously are found to be desirable, that is, these techniques are quite successful in reducing energy consumption and their performance (in terms of execution cycles) is comparable to that of a pure performance-oriented scheduling. We also illuminate the inherent approximations and difficulties in building energy models for enabling energy-aware instruction scheduling and explore alternative options using cycle-accurate energy simulator. The simulation results show that the energy-oriented scheduling reduces energy consumption by up to 30% compared to the performance-oriented scheduling.     

Topology-transparent time division multiple access broadcast scheduling in multihop packet radio networks

Many topology-dependent transmission scheduling algorithms have been proposed to minimize the time-division multiple-access frame length in multihop packet radio networks (MPRNs), in which changes of the topology inevitably require recomputation of the schedules. The need for constant adaptation of schedules-to-mobile topology entails significant problems, especially in highly dynamic mobile environments. Hence, topology-transparent scheduling algorithms have been proposed, which utilize Galois field theory and Latin squares theory. We discuss the topology-transparent broadcast scheduling design for MPRNs. For single-channel networks, we propose the modified Galois field design (MGD) and the Latin square design (LSD) for topology-transparent broadcast scheduling. The MGD obtains much smaller minimum frame length (MFL) than the existing scheme while the LSD can even achieve possible performance gain when compared with the MGD, under certain conditions. Moreover, the inner relationship between scheduling designs based on different theories is revealed and proved, which provides valuable insight. For topology-transparent broadcast scheduling in multichannel networks, in which little research has been done, the proposed multichannel Galois field design (MCGD) can reduce the MFL approximately M times, as compared with the MGD when M channels are available. Numerical results show that the proposed algorithms outperform existing algorithms in achieving a smaller MFL.     

移动网格任务调度算法研究

针对移动网格中同一网格域的任务调度问题进行研究。考虑Min-Min算法的负载不均衡和移动终端的能量受限因素,改进一种结合移动终端能量受限和Min-Min算法的EnergyMin-Min算法(即E-mm算法)进行任务调度,来提高任务执行成功率并优化系统的负载性能。通过仿真验证分析,改进后的E-mm算法相对于Min-Min算法不仅能满足上述要求和提高资源利用率,而且系统负载均衡效果得到明显改善。     

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.