Kaiyang Liu

Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing

Nowadays, in order to deal with the increasingly complex applications on mobile devices, mobile cloud offloading techniques have been studied extensively to meet the ever-increasing energy requirements. In this study, an offloading decision method is investigated to minimize the energy consumption of mobile device with an acceptable time delay and communication quality. In general, mobile devices can execute a sequence of tasks in parallel. In the proposed offloading decision method, only parts of the tasks are offloaded for task characteristics to save the energy of multi-devices. The issue of the offloading decision is formulated as an NP-hard 0-1 nonlinear integer programming problem with time deadline and transmission error rate constraints. Through decision-variable relaxation from the integer to the real domain, this problem can be transformed as a continuous convex optimization. Based on Lagrange duality and the Karush-Kuhn-Tucker condition, a solution with coupled terms is derived to determine the priority of tasks for offloading. Then, an iterative decoupling algorithm with high efficiency is proposed to obtain near-optimal offloading decisions for energy saving. Simulation results demonstrate that considerable energy can be saved via the proposed method in various mobile cloud scenarios. A task offloading decision method is proposed among multi-devices for energy saving.The problem is formalized as a 0-1 nonlinear integer programming problem.An iterative decoupling algorithm that combines with decision-variable relaxation and convex optimization is proposed for near-optimal decisions.

Dynamic resource reservation via broker federation in cloud service: A fine-grained heuristic-based approach

In cloud computing, Infrastructure-as-a-Service (IaaS) cloud providers can offer two types of purchasing plans for cloud users, including on-demand plan and reservation plan. Generally reservation price is cheaper than on-demand price, while reservation plan may cause highly underutilized capacity problem. How to joint optimize the service cost and the resource utilization for clouds is a critical issue. To address this issue, a novel steady broker federation is developed to coordinate service demands in this paper. And the optimal reservation problem can be formulated as a nonlinear integer programming model. Then a fine-grained heuristic algorithm is proposed to reduce its computational complexity and obtain quasi-optimal solutions. Numerical simulations driven by large-scale Parallel Workloads Archive demonstrate that the proposed approach can save considerable costs for cloud users and improves the resource utilization for IaaS cloud providers.

A LiFePO4 battery management system for heavy-haul train electrically controlled pneumatic brake system application

In an electrically controlled pneumatic(ECP) brake system, The locomotives of heavy-haul train communicate with cars and supply power for them through the lonworks bus. To guarantee the proper level of uninterrupted power supply for each car, as well as reliable communication between them, a dedicated LiFePO4 battery management system(BMS) is proposed in this paper. Compared with the conventional BMS, several improvements were made. To begin with, a carefully designed impedance network is integrated for reliable communication. Then, a synchronous rectifier buck converter is used for charger design, which can improve the charging efficiency of LiFePO4 battery. Moreover, To avoid overcharging and discharging caused by cell imbalance, a passive cell balancing algorithm is adopted. Finally, an improved battery impendence based coulomb counting method is proposed to report the accurate state of charge(SOC) of battery to locomotives. The experimental results show the battery charger has the peak efficiency of 97.5%, and the SOC estimation and cell balancing have satisfactory performances.

A Combinatorial Optimization for Energy-Efficient Mobile Cloud Offloading over Cellular Networks

Recently, mobile cloud offloading is a promising technique to deal with the increasingly complex applications on mobile devices, meeting the ever- increasing energy requirements. However, cloud offloading with multiple mobile devices may cause considerable mutual interference, which may result in intolerable time delay and more energy consumption. In this paper, a novel offloading decision method is investigated to minimize the total energy consumption of mobile devices over cellular networks. Generally, mobile devices can execute a sequence of tasks in parallel with different characteristics, i.e., communication- intensive and computation-intensive. And recent advances show that only computation-intensive tasks are applicable to be offloaded for energy saving. The offloading decision issue is formulated as a NP- hard combinatorial optimization problem with the time deadline and communication quality constraints. Combining the problem linearization method and decision variables mapping from integer to the real domain, a rapid and efficient iterative approximation method is proposed, helping the cloud controller to select the best tasks for offloading aiming at minimizing the total energy consumption. Numerical simulation demonstrates that considerable energy can be saved with the proposed task offloading method in mobile cloud scenarios.

A Game-Theoretical Approach for Spectrum Efficiency Improvement in Cloud-RAN

As tremendous mobile devices access to the Internet in the future, the cells which can provide high data rate and more capacity are expected to be deployed. Specifically, in the next generation of mobile communication 5G, cloud computing is supposed to be applied to radio access network. In cloud radio access network (Cloud-RAN), the traditional base station is divided into two parts, that is, remote radio heads (RRHs) and base band units (BBUs). RRHs are geographically distributed and densely deployed, so as to achieve high data rate and low latency. However, the ultradense deployment inevitably deteriorates spectrum efficiency due to the severer intercell interference among RRHs. In this paper, the downlink spectrum efficiency can be improved through the cooperative transmission based on forming the coalitions of RRHs. We formulate the problem as a coalition formation game in partition form. In the process of coalition formation, each RRH can join or leave one coalition to maximize its own individual utility while taking into account the coalition utility at the same time. Moreover, the convergence and stability of the resulting coalition structure are studied. The numeric simulation result demonstrates that the proposed approach based on coalition formation game is superior to the noncooperative method in terms of the aggregate coalition utility.

A differentiated pricing mechanism for idle resources allocation in reservation clouds

In cloud computing, Infrastructure-as-a-Service (IaaS) cloud providers can offer two types of renting plans for Software-as-a-Service (SaaS) clouds, including on-demand plan and reservation plan. The SaaS cloud provider prefers reservation plan for its cheaper price. However, it may cause highly unused capacity without the knowledge of future demands. Therefore, how to improve the utilization of the idle reserved resources is a critical issue. To address this issue, a novel leasing mechanism based on the non-cooperative game is investigated to reallocate the redundant resources. Then to further increase the profit SaaS clouds, a differentiated pricing scheme is proposed. Numerical simulation results indicate that the proposed scheme can improve the profit of SaaS cloud provider and reduce the cost of end users.

An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing

Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel become critical issues. In this paper, the problem of the offloading decision is formulated as a 0-1 nonlinear integer programming problem under the constraints of channel interference threshold and the time deadline. Through the classification and priority determination for the mobile devices, a reverse auction-based offloading method is proposed to solve this optimization problem for energy efficiency improvement. The proposed algorithm not only achieves the task offloading decision but also gives the facility of resource allocation. In the energy efficiency performance aspects, simulation results show the superiority of the proposed scheme.

Energy Optimization by Flow Routing Algorithm in Data Center Network Satisfying Deadline Requirement

Recently, the data center networks consume a large amount of energy, therefore, the energy saving is becoming increasingly important. Most previous works consolidate flows into as few switches as possible, which is not suitable for network-limited flows which require huge bandwidth to finish traffic transmission within deadline. To solve the challenge of energy-efficient path and deadline satisfied for network-limited flows, in this paper, we formulate the routing problem as a 0-1 integer programming problem, and propose a two-phase energy-efficient flow routing algorithm. Both energy saving and link utilization maximization are considered. Simulation results show that our algorithm achieves better performance in terms of energy saving, link utilization, as well as the active switch ratio.