数据中心电费最小化的负载调度策略

为了降低电费成本,一些数据中心使用绿色能源供电。然而,负载的波动性和电价的时间差异性给数据中心电费成本带来了挑战。针对上述问题,提出一种低成本的负载调度算法,使得数据中心电费最小化。首先,建立电力耗费模型;然后,将电费最小化问题形式化为一个多目标约束的优化问题;最后,求解该优化问题得到相应的负载调度策略。实验结果表明:该算法可以在保证负载性能的前提下,有效降低数据中心的电费成本。     

基于多层虚拟拓扑节能的SDN数据中心网络流量调度算法

针对“富连接”数据中心网络在低负载时能源利用率较低的问题,提出一种节能的多层虚拟拓扑流量调度算法(EMV-SDN)。建立节能流量调度问题的整形线性规划(Integral Linear Programing,ILP)优化数学模型,使得在承载所有网络负载的前提下,网络能源消耗最小。提出节能的多层虚拟拓扑流量调度算法来求解数学优化模型,得到数据流的节能调度方案。通过休眠高层的虚拟拓扑和交换机端口实现节能,降低网络能源消耗。实验结果表明,在网络能耗和数据流平均完成时间等方面,EMV-SDN算法均优于ECMP(Equal-Cost Multi-Path Routing)以及Dijkstra最短路径算法。     

一种改进的智能电网能耗调度算法

为了提高电网的运行效率,提出一种新的实时能耗调度算法,通过考虑负载不确定性来实现每个用户的电费最小化.我们将负载调度描述为一个优化问题.为了降低计算复杂度,提出一种近似动态规划算法,以解决电器运行的调度问题.在研究问题时,考虑了必须运行和可控运行在内的不同电器.与大部分当前需求侧管理算法假设完全知晓用户用电需求不同,算法只需知道将来部分需求的估计即可.仿真结果表明,能量调度算法既降低了用户用电支出,又提高了负载需求的峰均比,为用户和电力公司带来收益.     

基于蚁群算法的SDN数据中心网络大象流调度研究

针对传统方法调度大象流时容易造成数据中心网络拥塞和负载不均衡等问题,提出一种基于蚁群算法的SDN（software defined network）数据中心网络流量调度算法ACO-SDN。对大象流调度问题建立整型线性规划ILP（integral linear programing）模型,优化目标为最小化最大链路利用率。通过重定义蚁群算法的参数和操作求解ILP模型,得到大象流重路由的最优路径。实验结果表明,与ECMP（equal-cost multi-path routing）和GFF（global first fit）流量调度算法相比,ACO-SDN算法降低了网络最大链路利用率,有效地提高了网络对分带宽。     

考虑源荷互动的主动配电网两层优化调度

针对主动配电网中清洁能源消纳率低、负荷侧资源调度不足的问题,提出了一种包含负荷层和主动配网层的两层优化调度模型。负荷层首先根据负荷参与调度的形式不同分类建模,然后通过源荷协调互动调整柔性负荷用电时序及清洁能源的出力;主动配网层则依据分时电价优化系统的综合运行成本,提出禁忌-细胞膜优化算法对模型求解。通过算例对比分析了分层、分类负荷前后三种方案下优化调度的结果,证明了提出的优化调度模型在配网系统经济运行的前提下,可以有效提高清洁能源的消纳率,进一步降低负荷峰谷差,同时验证了算法的有效性。     

面向移动云计算的自适应虚拟机调度算法

随着移动云计算的快速发展和应用普及,如何对移动云中心资源进行有效管理同时又降低能耗、确保资源高可用是目前移动云计算数据中心的热点问题之一.本文从CPU、内存、网络带宽和磁盘四个维度,建立了基于多目标优化的虚拟机调度模型VMSM-EUN(Virtual Machine Scheduling Model based on Energy consumption,Utility and minimum Number of servers),将最小化数据中心能耗、最大化数据中心效用以及最小化服务器数量作为调度目标.设计了基于改进粒子群的自适应参数调整的虚拟机调度算法VMSA-IPSO(Virtual Machine Scheduling Algorithm based on Improved Particle Swarm Optimization)来求解该模型.最后通过仿真实验验证了本文提出的调度算法的可行性与有效性.对比实验结果表明,本文设计的基于改进粒子群的自适应虚拟机调度算法在进行虚拟机调度时,能在降低能耗的同时提高数据中心效用.     

虚拟化数据中心全局能耗优化调度方法

虚拟化数据中心的制冷和供电设备能耗比重大且浪费严重,但当前虚拟化能耗优化的研究仅考虑IT设备能耗,针对该问题,通过对数据中心能耗逻辑的研究,提出一种虚拟化数据中心全局能耗优化调度方法。该方法通过感知数据中心负载和热分布状况,依据虚拟化调度规则生成动态调度策略,并对虚拟设备组的制冷供电设备进行同步调度,减少数据中心冗余制冷和设备空载损耗,以此最小化数据中心能耗。实验结果表明,该调度方法可节省制冷设备近26％的冗余制冷,并提升供电设备8％左右的供电效率,提高数据中心的能耗有效性,降低整体能耗。     

融合遗传和粒子群算法的云工作流调度算法

针对云工作流调度问题,提出一种融合遗传算法和粒子群优化算法的工作流调度负载均衡算法.充分利用多元启发式方法融合的优势,避免遗传算法的收敛过慢和粒子群算法易于陷入局部最优的缺陷,有效将工作流任务映射至虚拟机资源,实现全局工作流执行跨度最小化和虚拟机分配的负载均衡.以算例详细说明算法实现思路,在现实科学工作流条件下进行仿真测试,验证算法性能.与几种单一元启发式调度方法相比,验证该算法拥有更高执行效率和负载均衡度.     

一种基于任务全局迁移的静态调度算法

任务调度是分布实时系统中的一个关键问题.TDS等典型算法在优化条件下可得到该问题调度长度上的最优解.但是TDS等算法在节点分配时存在节点选择范围和节点执行时间范围的局限,无法最小化算法所需处理器数目.任务全局迁移调度算法GTT（global task-transferring）在保证调度长度最优的前提下,从全局范围内选择并调度任务节点,有效利用了处理器,可最小化调度所需处理器数目.优化条件下对各种算法的调度实验表明,GTT算法在加速比和效率上比TDS等同类算法有显著提高.GTT算法的时间复杂度是O（d ｜ V｜^ 2）.这里｜V｜是DAG图中的节点数,d是图中各节点入度或出度的最大值.     

基于预测及蚁群算法的云计算资源调度策略

研究云计算资源调度问题,针对目前静态的网格资源调度算法只考虑任务完成时间最小化,导致了不能满足动态的云计算资源调度要求。为了适应云计算的动态性和实时性,解决云计算资源调度问题,降低数据中心用电量,提出一种基于预测及蚁群算法的云计算资源调度策略。当数据中心利用率较低时运行改进蚁群算法来合理调度虚拟机至宿主机,通过动态趋势预测算法预测数据中心负载来智能开关宿主机。仿真结果表明,采用预测及蚁群算法进行的云计算资源调度策略,保证了云计算的实时性,并有效减少数据中心用电量。     

A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers

Internet Data Center (IDC) is one of important emerging cyber-physical systems. To guarantee the quality of service for their worldwide users, large Internet service providers usually operate multiple geographically distributed IDCs. The enormous power consumption of these data centers may lead to both huge electricity bills and considerable carbon emissions. To mitigate these problems, on-site renewable energy plants are emerging in recent years. Since the renewable energy is intermittent, greening geographical load balancing (GGLB for short) has been proposed to reduce both the electricity bills and carbon emissions by following the renewables. However, GGLB is not able to well deal with the wildly fluctuating wind power when applied into IDCs with on-site wind energy plants. It may either fail to minimize the total electricity bills or incur the costly frequent on–off switching of servers. In order to minimize the total electricity bills of geographically distributed IDCs with on-site wind energy plants, we formulate the total electricity bills minimization problem and propose a novel two-time-scale load balancing framework TLB to solve it. First, TLB models the runtime cooling efficiency for each IDC. Then it predicts the future fine-grained (e.g., 10-min) on-site wind power output at the beginning of each scheduling period (e.g., an hour). After that, TLB transforms the primal optimization problem into a typical mixed-integer linear programming problem and solves it to finally obtain the optimal scheduling policy including the open server number as well as the request routing policy. It is worth noting that the open server number of each IDC will remain the same during each scheduling period. As an application instance of TLB, we also design a two-time-scale load balancing algorithm TLB-ARMA for our experimental scenario. Evaluation results based on real-life traces show that TLB-ARMA is able to reduce the total electricity bills by as much as 12.58 % compared with the hourly executed GGLB without incurring the costly repeated on–off switching of servers.     

A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid

This paper proposes a Stackelberg game approach to maximize the profit of the electricity retailer (utility company) and minimize the payment bills of its customers. The electricity retailer determines the retail price through the proposed smart energy pricing scheme to optimally adjust the real-time pricing with the aim to maximize its profit. The price information is sent to the customers through a smart meter. According to the announced price, the customers can automatically manage the energy use of appliances in the households by the proposed optimal electricity consumption scheduling system with the aim to minimize their electricity bills. We model the interactions between the retailer and its electricity customers as a 1-leader, N-follower Stackelberg game. At the leader’s side, i.e., for the retailer, we adopt genetic algorithms to maximize its profit while at the followers’ side, i.e., for customers, we develop an analytical solution to the linear programming problem to minimize their bills. Simulation results show that the proposed approach is beneficial for both the customers and the retailer.     

Formally modeling and analyzing cost‐aware job scheduling for cloud data center

With the rapid development of cloud computing, many distributed data centers have been deployed. This means larger energy consumption requirements from the data center. How to reduce the cost of data center has received significant attention recently. Although there are several efforts in studying energy consumption of the data center, very few have considered modeling and analyzing cost‐aware job scheduling for the cloud data center. To address this emerging problem, we propose a systematic approach that considers both basic elements and their relationships in cloud data center. First, we present a formal language to describe the cloud data center, and a job scheduling net is proposed to formally model the basic elements such as user request, Web portal, data center, and server. Second, we minimize the total cost of the cloud data center by considering the multidimensional resource and local electricity price on the basis of the state space of constructed model. The dynamic job scheduling algorithm and its specific execution steps are proposed based on the alternating direction method of multipliers algorithm. Third, the operational semantics and related theories of Petri nets for establishing the correctness of our proposed method are presented. Finally, a series of simulations are performed to illustrate that the proposed method can guarantee the correct behavior of job scheduling in the cloud data center while meeting the required cost.     

Reducing the expenses of geo-distributed data centers with portable containerized modules

In order to maximize their profits, big IT companies need to reduce the operating expenses (OpEx) and capital expenses (CapEx) of their geo-distributed data centers. To reduce OpEx, recent studies have proposed algorithms that dynamically dispatch workload among different data center sites, to exploit the variability in local electricity price and green energy availability. However, the amount of OpEx savings achieved by workload dispatching is often limited by the local computing capacity. To reduce CapEx, data centers try to minimize the needed increases in size based on estimated workload growth. However, workload overestimation would result in a high CapEx, while underestimation would hurt business increases.In this paper, we propose ExContainer, a novel strategy that leverages portable containerized modules to minimize both OpEx and CapEx for geo-distributed data centers. With ExContainer, containers are allocated monthly to different sites and workload is dispatched hourly, to minimize total OpEx. To cut CapEx, ExContainer finds the best trade-off between the degree of capacity expansion and the number of needed new containers to handle the workload peak. We evaluate ExContainer with real-world traces. Our experimental results show that compared to state-of-the-art solutions, ExContainer allows significant reduction in both OpEx and CapEx.     

Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers

Job scheduling in data centers can be considered from a cyber–physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber–physical, spatio-temporal (i.e. start time and servers assigned), thermal-aware job scheduling algorithms that minimize the energy consumption of the data center under performance constraints (i.e. deadlines). Savings are possible by being able to temporally “spread” the workload, assign it to energy-efficient computing equipment, and further reduce the heat recirculation and therefore the load on the cooling systems. This paper provides three categories of thermal-aware energy-saving scheduling techniques: (a) FCFS-Backfill-XInt and FCFS-Backfill-LRH, thermal-aware job placement enhancements to the popular first-come first-serve with back-filling (FCFS-backfill) scheduling policy; (b) EDF-LRH, an online earliest deadline first scheduling algorithm with thermal-aware placement; and (c) an offline genetic algorithm for SCheduling to minimize thermal cross-INTerference (SCINT), which is suited for batch scheduling of backlogs. Simulation results, based on real job logs from the ASU Fulton HPC data center, show that the thermal-aware enhancements to FCFS-backfill achieve up to 25% savings compared to FCFS-backfill with first-fit placement, depending on the intensity of the incoming workload, while SCINT achieves up to 60% savings. The performance of EDF-LRH nears that of the offline SCINT for low loads, and it degrades to the performance of FCFS-backfill for high loads. However, EDF-LRH requires milliseconds of operation, which is significantly faster than SCINT, the latter requiring up to hours of runtime depending upon the number and size of submitted jobs. Similarly, FCFS-Backfill-LRH is much faster than FCFS-Backfill-XInt, but it achieves only part of FCFS-Backfill-XInt’s savings.     

A heuristic optimization approach for multi-vehicle and one-cargo green transportation scheduling in shipbuilding

To actively respond to the call for green shipbuilding, block cooperative transportation has been particularly concerned in reducing carbon emission in the shipyard, and hence a “multi-vehicle and one-cargo” (MVOC) green transportation scheduling problem emerges. Aiming to solve this problem effectively and improve transportation efficiency and reduce energy consumption, a bi-objective mathematical model combined routing model with synchronization constraints is proposed to simultaneously minimize non-value-added transportation time cost and total CO₂ emission. A Pareto-based multi-objective Tabu Search (MOTS) algorithm is then designed to solve the model, in which local improvements are developed to generate promising neighboring individuals. Experimental results show that the proposed MOTS algorithm can effectively solve the problem even on a large scale and outperform the classic algorithm of nondominated sorting genetic algorithm-II (NSGA-Ⅱ). It is hoped that this work enables an operation mode with high efficiency and low energy consumption and provides useful insights for flatcar transportation scheduling operators in the shipyard.     

网络状态感知的MapReduce作业洗牌数据传输调度研究

MapReduce作业在洗牌阶段花费大量时间,因此有效的洗牌数据传输调度可以提高MapReduce的性能。数据中心网络中,常有一些周期性的数据流传输。在考虑已知这些周期性数据流传输的情况下,为MapReduce的洗牌数据传输调度问题建立了优化模型,并设计了一个有效的数据传输调度算法。在网络空闲时间段大小相同的情况下,证明了所提算法是近似比为3/2的近似算法。仿真实验结果表明,该算法能够有效地利用网络资源,减少洗牌数据流的调度长度。     

云环境下绿色任务调度策略

针对云计算数据中心的能耗问题,提出了绿色云计算体系理论,设计了绿色云系统架构;基于该架构,将能量作为一种系统资源进行分配,提出了三种绿色任务调度算法分别是STF-OS、LTF-OS和RT-OS算法;对三种绿色任务调度算法可行性做了相关的理论分析,三种算法可以有效地减少能源消耗;通过扩展云计算仿真平台CloudSim实现了模拟实验,结果表明STF-OS算法降低数据中心能耗的能力最优。