云计算资源预测整合：降低运营成本与能源效率

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"通过整合云中的资源预测来降低运营成本" 在当前的信息化时代，云数据中心的运营成本和能源消耗是业界关注的重要议题。随着电力成本的不断攀升和环保意识的增强，如何优化云数据中心的能源效率变得至关重要。这篇研究论文提出了一种利用资源预测来整合云资源的方法，以降低运营成本。首先，文章提到了云数据中心中常见的能源消耗问题，特别是当数据中心内存在大量相同服务器时。为解决这一问题，研究者引入了动态分配活跃服务器、合并不同应用在同一服务器上运行以及调整活跃服务器的CPU速度等策略。这些策略旨在减少不必要的能源浪费，提高硬件资源的利用率。接着，研究人员采用"在线着色装箱"问题来模型化合并问题。这是一个经典的计算机科学问题，用于在有限的资源中有效分配多个实体。他们设计了一种应用感知的近似算法，该算法能够在接近最优解的情况下进行资源分配，具有1.7的渐近逼近比。这意味着算法能够在大部分情况下找到接近最佳的解决方案。然后，论文引入了预测贝叶斯网络模型，这是一种强大的概率建模工具，能够识别和学习数据中心的工作负载模式。通过对未来工作负载的预测，系统可以动态地调整资源配置，以适应预期的需求变化。通过使用真实数据中心的追踪数据进行评估，结果表明该预测算法能够准确估算未来的资源需求。此外，文章还证明了采用这种方法的协调机制不仅能显著节约能源，降低运营成本，且接近最优的离线解决方案，即假设所有未来信息已知的理想情况。同时，这种方法在服务器组件的磨损方面产生的可靠性成本极低，这意味着它在节省成本的同时，不会牺牲系统的稳定性和持久性。通过整合云资源并结合预测算法，该研究提供了一种有效的策略来降低云数据中心的运营成本，同时保持了服务质量和可靠性。这种策略对于应对日益严峻的能源挑战和不断上升的运营支出具有重要的实际意义。

Reducing Operational Costs through Consolidation

with Resource Prediction in the Cloud

Jian Li

, Kai Shuang

, Sen Su

, Qingjia Huang

, Peng Xu

, Xiang Cheng

, and Jie Wang

State Key Lab of Networking and Switching Technology,

Beijing University of Posts and Telecommunications

{upton, shuangk, susen, QJHuang, xupeng, chengxiang}@bupt.edu.cn

University of Massachusetts Lowell

wang@cs.uml.edu

Abstract—How to achieve energy efﬁciency to run a cloud data

center is a major challenge in the era of rising electricity cost and

environmental protection. Various techniques have been devised

to help reduce energy consumption for cloud data centers that

consist of a large number of identical servers, including dynamic

allocation of active servers, consolidating diverse applications to

run on them, and adjusting the CPU speed of an active server.

Leveraging these techniques, we use an Online Coloring Bin

Packing problem to model the consolidation problem and devise

an effective application-aware approximation algorithm to ﬁnd a

near-optimal solution. We show a 1.7 asymptotic approximation

ratio. We then apply a Predictive Bayesian Network model to

identify daily workload patterns and adjust resource provisioning

accordingly. We evaluate our approaches using traces collected

from a real data center and demonstrate that (1) our prediction

algorithm is effective in estimating future demands, (2) our coor-

dinated approaches can provide signiﬁcant savings of energy and

operational costs close to the near-optimal ofﬂine solution,and (3)

our approaches incur little reliability costs in term of wear-and-

tear of server components.

Keywords: Data Center; Energy Efﬁciency; Consolidation;

Forecast-based Resource Provisioning

I. I NTRODUCTION

People today are increasingly relying on cloud services

to conduct business and take care of their household and

entertainment needs. Search engines, web mail, and online

social networks for examples, are common cloud services. To

guarantee performance at large scales, cloud service providers

would operate data centers to take on these IT demands. Rising

electricity cost and concerns over greenhouse gas emissions

amplify the importance of power management of these centers.

Even in 2006, data centers consumed an estimated 61 billion

kilowatt-hours (kWh) of energy, and accounted for 1.5% of

the total U.S. power consumption [1]. A recent study estimates

that operational costs for powering and cooling these centers

will soon exceed acquisition costs [2]. Thus, how to reduce

operational costs for cloud data centers has become a serious

issue.

A number of power management mechanisms have been

used in practice to reduce energy consumption. At the hard-

ware level, the Dynamic Voltage and Frequency Scaling

(DVFS) technology scales processor clock frequencies and

supplies needed voltages. At the software level, consoli-

dating applications to run on a smaller number of servers

can reduce power consumption. In this paper, we present

a new consolidation algorithm to effectively combine het-

erogeneous applications based on correlations between them,

and a forecast-based resource provisioning algorithm to better

allocate needed resources. Our approach is motivated by

observing real data collected in the Web caching project [3].

First, we observe that the increased diversity of applications

is a new trend in the cloud. Second, the total workload in

cloud data centers typically exhibits periodic patterns with

daily, weekly, and seasonal cycles [4]. The ﬁrst observation

motivates consolidation of various types of workloads on a

smaller number of servers. The second observation implies

that adjusting resource provisioning during off-peak periods

can reduce power consumption.

In this context, we ﬁrst formulate the consolidation problem

as an Online Coloring Bin Packing problem (OCBP). This

problem, however, is NP-hard. We then devise an Online

Coloring First-Fit (OCFF) algorithm to approximate OCBP,

which is based on First-Fit bin-packing heuristic for its

simplicity and effectiveness. OCFF automatically packs the

arrival workload into a smaller number of servers. We show

that OCFF provides a 1.7 performance guarantee. Second,

taking advantage of the periodic patterns of workload traces,

we devise a forecast-based resource provisioning algorithm

using the Bayesian Network (BN) model, which enables us

to dynamically allocate proper amount of resources (e.g., the

number of servers and their respective frequencies) and hence

reduce power consumption. Our coordinated approaches run

periodically to account for time-varying behavior. Due to

unforeseen factors such as ﬂash crowds, we reserve sufﬁcient

capacity readily. The main contributions of this paper are

summarized as following:

• We devise an efﬁcient online algorithm for packing het-

erogeneous applications into a fewer number of servers.

We prove its asymptotical worst-case performance upper

bound. We also incorporate a forecast-based resource

provisioning algorithm to minimize the operational costs.

• We carry out extensive numerical experiments and

demonstrate that our approaches can signiﬁcantly reduce

operational costs. We show that using online algorithm

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

DOI 10.1109/CCGrid.2012.50

793

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