A hybrid algorithm to reduce energy consumption management in cloud data centers

There are several physical data centers in cloud environment with hundreds or thousands of computers. Virtualization is the key technology to make cloud computing feasible. It separates virtual machines in a way that each of these so-called virtualized machines can be configured on a number of hosts according to the type of user application. It is also possible to dynamically alter the allocated resources of a virtual machine. Different methods of energy saving in data centers can be divided into three general categories: 1) methods based on load balancing of resources; 2) using hardware facilities for scheduling; 3) considering thermal characteristics of the environment. This paper focuses on load balancing methods as they act dynamically because of their dependence on the current behavior of system. By taking a detailed look on previous methods, we provide a hybrid method which enables us to save energy through finding a suitable configuration for virtual machines placement and considering special features of virtual environments for scheduling and balancing dynamic loads by live migration method

A Genetic Based Resource Management Algorithm Considering Energy Efficiency in Cloud Computing Systems

Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, in addition to high operating costs and huge amounts of electrical power consumption. Insufficient cooling systems and inefficient, causing overheating sources, shortening the life of the machine and too much carbon dioxide is produced. In this paper, we aim to improve system performance; Cloud Computing based on a decrease in migration of among virtual machines (VM), and reduce energy consumption to be able to manage resources to achieve optimal energy efficiency. For this reason, various techniques such as genetic algorithms (GAs), virtual machine migration and ways Dynamic voltage and frequency scaling (DVFS), and resize virtual machines to reduce energy consumption and fault tolerance are used. The main purpose of this article, the allocation of resources with the aim of reducing energy consumption in cloud computing. The results show that reduced energy consumption and hold down the rate of virtual machines breach of contract, reduces migration as well

Simple and effective dynamic provisioning for power-proportional data centers.

数据中心在运转过程中需要消耗大量的电能。但是这其中的很大一部分电能都在工作负荷少时被空闲的服务器消耗了。动态供应技术通过在工作负荷少时，关掉不必要的服务器来节省这一部分的电能。在这篇文章中，我们研究未来工作负荷信息到底能给动态供应带来多大好处。特别地，对于有或没有未来工作负荷信息的两种情况，我们提出了在线的动态供应算法。我们首先发现了离线动态供应的最优解有一个很优美的结构，通过这个优美的结构我们可以以分而治之的方法完全确定离线动态供应的最优解。在这个基础之上我们设计了两个在线算法，它们的竞争比分别为2-α和e/(e - 1 + α),其中α表示标准化的预测未来窗口的长度。在这个预测未来窗口中，未来的工作负荷信息可以精确的获得。一个重要的发现是超出一个完整的预测未来窗口的未来工作负荷信息不会对动态供应的性能有任何提高。我们提出的在线算法是分散的，因此易于实现。最后，我们用真是数据中心的数据测试了我们的在线算法。在设计在线算法的时候，我们利用了未来工作负荷信息。这是因为在很多的现代系统中，短期的未来工作信息可以被精确的估计。我们也测试了我们的算法在有预测噪声时候的性能，结果表明我们的算法在有噪声时，也能很好的工作。我们相信利用未来信息是设计在线算法的一个新的角度。在传统的在线算法设计过程中，我们通常不考虑未来输入信息。在这种情况下，许多在线问题有简单的最优的算法，但是这个最优算法的竞争比却很大。其实未来输入信息在很多在线问题中都能在一定程度上被精确预测，所以我们相信我们可以利用这些未来输入信息去设计竞争比较小的在线算法，这样设计的在线算法具有更多的应用优点，并在理论上也给予我们启发。Energy consumption represents a significant cost in data center operation. A large fraction of the energy however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy by turning of unnecessary servers. In this thesis we explore how much gain knowing future workload information can bring to dynamic pro-visioning. In particular we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the offline dynamic pro-visioning problem which allows us to characterize the optimal solution in a "divide-and-conquer" manner. We then exploit this insight to design two online algorithms with competitive ratios 2 - α and e/ (e - 1+ α), respectively where 0 ≤ α ≤ 1 is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to α =1) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to im-plement. We demonstrate their effectiveness in simulations using real-world traces.When designing online algorithms, we utilize future input information because for many modern systems their short-term future inputs can be predicted by machine learning time-series analysis etc. We also test our algorithms in the presence of prediction errors in future workload information and the results show that our algorithms are robust to prediction errors. We believe that utilizing future information is a new and important degree of freedom in designing online algorithms. In traditional online algo¬rithm design future input information is not taken into account. Many online problems have online algorithms with optimal but large competitive ratios. Since future input information to some extent can be estimated accurately in many problems we believe that we should exploit such information in online algorithm design to achieve better competitive ratio and provide more competitive edge in both practice and theory.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Lu, Tan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.Includes bibliographical references (leaves 76-81).Abstracts also in Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Contributions --- p.4Chapter 1.3 --- Thesis Organization --- p.5Chapter 2 --- Related Work --- p.6Chapter 3 --- Problem Formulation --- p.10Chapter 3.1 --- Settings and Models --- p.10Chapter 3.2 --- Problem Formulation --- p.13Chapter 4 --- Optimal Solution and Offline Algorithm --- p.15Chapter 4.1 --- Structure of Optimal Solution --- p.15Chapter 4.2 --- Intuitions and Observations --- p.17Chapter 4.3 --- Offline Algorithm Achieving the Optimal Solution --- p.18Chapter 5 --- Online Dynamic Provisioning --- p.21Chapter 5.1 --- Dynamic Provisioning without FutureWorkload Information --- p.22Chapter 5.2 --- Dynamic Provisioning with Future Workload Information --- p.23Chapter 5.3 --- Adapting the Algorithms to Work with Discrete-Time Fluid Workload Model --- p.31Chapter 5.4 --- Extending to Case Where Servers Have Setup Time --- p.32Chapter 6 --- Experiments --- p.35Chapter 6.1 --- Settings --- p.35Chapter 6.2 --- Performance of the Proposed Online Algorithms --- p.38Chapter 6.3 --- Impact of Prediction Error --- p.39Chapter 6.4 --- Impact of Peak-to-Mean Ratio (PMR) --- p.40Chapter 6.5 --- Discussion --- p.40Chapter 6.6 --- Additional Experiments --- p.41Chapter 7 --- A New Degree of Freedom for Designing Online Algorithm --- p.44Chapter 7.1 --- The Lost Cow Problem --- p.45Chapter 7.2 --- Secretary Problem without Future Information --- p.47Chapter 7.3 --- Secretary Problem with Future Information --- p.48Chapter 7.4 --- Summary --- p.50Chapter 8 --- Conclusion --- p.51Chapter A --- Proof --- p.54Chapter A.1 --- Proof of Theorem 4.1.1 --- p.54Chapter A.2 --- Proof of Theorem 4.3.1 --- p.57Chapter A.3 --- Least idle vs last empty --- p.60Chapter A.4 --- Proof of Theorem 5.2.2 --- p.61Chapter A.5 --- Proof of Corollary 5.4.1 --- p.70Chapter A.6 --- Proof of Lemma 7.1.1 --- p.72Chapter A.7 --- Proof of Theorem 7.3.1 --- p.74Bibliography --- p.7

Proactive software rejuvenation solution for web enviroments on virtualized platforms

The availability of the Information Technologies for everything, from everywhere, at all times is a growing requirement. We use information Technologies from common and social tasks to critical tasks like managing nuclear power plants or even the International Space Station (ISS). However, the availability of IT infrastructures is still a huge challenge nowadays. In a quick look around news, we can find reports of corporate outage, affecting millions of users and impacting on the revenue and image of the companies. It is well known that, currently, computer system outages are more often due to software faults, than hardware faults. Several studies have reported that one of the causes of unplanned software outages is the software aging phenomenon. This term refers to the accumulation of errors, usually causing resource contention, during long running application executions, like web applications, which normally cause applications/systems to hang or crash. Gradual performance degradation could also accompany software aging phenomena. The software aging phenomena are often related to memory bloating/ leaks, unterminated threads, data corruption, unreleased file-locks or overruns. We can find several examples of software aging in the industry. The work presented in this thesis aims to offer a proactive and predictive software rejuvenation solution for Internet Services against software aging caused by resource exhaustion. To this end, we first present a threshold based proactive rejuvenation to avoid the consequences of software aging. This first approach has some limitations, but the most important of them it is the need to know a priori the resource or resources involved in the crash and the critical condition values. Moreover, we need some expertise to fix the threshold value to trigger the rejuvenation action. Due to these limitations, we have evaluated the use of Machine Learning to overcome the weaknesses of our first approach to obtain a proactive and predictive solution. Finally, the current and increasing tendency to use virtualization technologies to improve the resource utilization has made traditional data centers turn into virtualized data centers or platforms. We have used a Mathematical Programming approach to virtual machine allocation and migration to optimize the resources, accepting as many services as possible on the platform while at the same time, guaranteeing the availability (via our software rejuvenation proposal) of the services deployed against the software aging phenomena. The thesis is supported by an exhaustive experimental evaluation that proves the effectiveness and feasibility of our proposals for current systems

Sistemas organizativos para la asignación dinámica de recursos computacionales en entornos distribuidos

[ES]Cloud Computing, el conocido paradigma computacional, está emergiendo en los últimos años con gran fuerza. Este paradigma incluye un novedoso modelo de comercialización basado en el pago por uso que ha cambiado radicalmente el modelo de negocio en Internet, lo que ha permitido que las empresas y usuarios individuales puedan alquilar los recursos computacionales que necesitan en cada momento. Este nuevo modelo computacional también ha derivado en que el modelo de producción de estos recursos computacionales evolucione hasta una aproximación cercana al modelo de producción just-in-time, en el que sólo se consumen los recursos necesarios para la producción de los servicios en función de la demanda existente en cada momento, hablándose dentro de este ámbito de elasticidad en los servicios ofertados. Para que esto sea posible, no cabe duda, que una gran cantidad de tecnologías subyacentes han tenido que madurar para dar como resultado un nicho tecnológico con la capacidad para variar los recursos asociados a cada servicio en función de la demanda. Sin embargo, pese a los indudables avances que se han producido a nivel tecnológico, todavía hoy existe una gran capacidad de mejora de estos sistemas. En este sentido, en el marco de esta tesis doctoral se propone el uso de los sistemas multiagente y, especialmente, aquellos basados en modelos organizativos para el control y monitorización de un sistema Cloud Computing. Gracias a esta aproximación, una de las primeras en este campo de investigación, será posible incluir en las plataformas Cloud de nueva generación características derivadas de la Inteligencia Artificial, como son la autonomía, la proactividad y, también, la capacidad de aprendizaje. Para ello se propone un modelo único en su concepción, que permite dotar a la organización de agentes inteligentes con capacidades auto-adaptativas en tiempo de ejecución para entornos abiertos, altamente dinámicos en los que, además, existe un cierto grado de incertidumbre. Así gracias a este modelo, el sistema es capaz de variar los recursos computacionales asociados a cada servicio producido en función de la demanda existe por parte de los usuarios, mediante la auto-adaptación dinámica del propio sistema en su conjunto