A self-adapting latency/power tradeoff model for replicated search engines

For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic

A conceptual framework of control, learn, and knowledge for computer power management

This conceptual paper observes the human inactivity in computer power management and discovers that; the efficiency of the computer power management (CPM)can be achieved by the eligibility of the human inactivity period. This period reduces the efficiency of CPM. This study examines the self-adaptation(SA) and the knowledge repository (KR)concepts, to model the framework of a new approach in computer power management. The essential elements and features from theseconceptswere adapted and applied as a techniqueto a new implementation of CLK-CPM. As a result, this study has proposed a modelof thetheoretical framework and demonstratesit through its conceptual framework for the technique

Energy-QoS Tradeoffs in J2EE Hosting Centers

International audienceNowadays, hosting centres are widely used to host various kinds of applications e.g., web servers or scientific applications. Resource management is a major challenge for most organisations that run these infrastructures. Many studies show that clusters are not used at their full capacity which represents a significant source of waste. Autonomic management systems have been introduced in order to dynamically adapt software infrastructures according to runtime conditions. They provide support to deploy, configure, monitor, and repair applications in such environments. In this paper, we report our experiments in using an autonomic management system to provide resource aware management for a clustered application. We consider a standard replicated server infrastructure in which we dynamically adapt the degree of replication in order to ensure a given QoS while minimising energy consumption

Coordinated Autonomic Managers for Energy Efficient Date Centers

The complexity of today’s data centers has led researchers to investigate ways in which autonomic methods can be used for data center management. Autonomic managers try to monitor and manage resources to ensure that the components they manage are self-configuring, self-optimizing, self-healing and self-protecting (so called “self-*” properties). In this research, we consider autonomic management systems for data centers with a particular focus on making data centers more energy-aware. In particular, we consider a policy based, multi-manager autonomic management systems for energy aware data centers. Our focus is on defining the foundations – the core concepts, entities, relationships and algorithms - for autonomic management systems capable of supporting a range of management configurations. Central to our approach is the notion of a “topology” of autonomic managers that when instantiated can support a range of different configurations of autonomic managers and communication among them. The notion of “policy” is broadened to enable some autonomic managers to have more direct control over the behavior of other managers through changes in policies. The ultimate goal is to create a management framework that would allow the data center administrator to a) define managed objects, their corresponding managers, management system topology, and policies to meet their operation needs and b) rely on the management system to maintain itself automatically. A data center simulator that computes its energy consumption (computing and cooling) at any given time is implemented to evaluate the impact of different management scenarios. The management system is evaluated with different management scenarios in our simulated data center

Low Power Resonant Rotary Global Clock Distribution Network Design

Along with the increasing complexity of the modern very large scale integrated (VLSI) circuit design, the power consumption of the clock distribution network in digital integrated circuits is continuously increasing. In terms of power and clock skew, the resonant clock distribution network has been studied as a promising alternative to the conventional clock distribution network. Resonant clock distribution network, which works based on adiabatic switching principles, provides a complete solution for on-chip clock generation and distribution for low-power and low-skew clock network designs for high-performance synchronous VLSI circuits.This dissertation work aims to develop the global clock distribution network for one kind of resonant clocking technologies: The resonant rotary clocking technology. The following critical aspects are addressed in this work: (1) A novel rotary oscillator array (ROA) topology is proposed to solve the signal rotation direction uniformity problem, in order to support the design of resonant rotary clocking based low-skew clock distribution network; (2) A synchronization scheme is proposed to solve the large scale rotary clocking generation circuit synchronization problem; (3) A low-skew rotary clock distribution network design methodology is proposed with frequency, power and skew optimizations; (4) A resonant rotary clocking based physical design flow is proposed, which can be integrated in the current mainstream IC design flow; (5) A dynamic rotary frequency divider is proposed for dynamic frequency scaling applications. Experimental and theoretical results show: (1) The efficiency of the proposed methodology in the construction of low-skew, low-power resonant rotary clock distribution network. (2) The effectiveness of the dynamic rotary frequency divider in extending the operating frequency range of the low-power resonant rotary based applications.Ph.D., Electrical Engineering -- Drexel University, 201

Allocation et réallocation de services pour les économies d'énergie dans les clusters et les clouds

L'informatique dans les nuages (cloud computing) est devenu durant les dernières années un paradigme dominant dans le paysage informatique. Son principe est de fournir des services décentralisés à la demande. La demande croissante pour ce type de service amène les fournisseurs de clouds à augmenter la taille de leurs infrastructures à tel point que les consommations d'énergie ainsi que les coûts associés deviennent très importants. Chaque fournisseur de service cloud doit répondre à des demandes différentes. C'est pourquoi au cours de cette thèse, nous nous sommes intéressés à la gestion des ressources efficace en énergie dans les clouds. Nous avons tout d'abord modélisé et étudié le problème de l'allocation de ressources initiale en fonction des services, en calculant des solutions approchées via des heuristiques, puis en les comparant à la solution optimale. Nous avons ensuite étendu notre modèle de ressources pour nous permettre d'avoir une solution plus globale, en y intégrant de l'hétérogénéité entre les machines et des infrastructures de refroidissement. Nous avons enfin validé notre modèle par simulation. Les services doivent faire face à différentes phases de charge, ainsi qu'à des pics d'utilisation. C'est pourquoi, nous avons étendu le modèle d'allocation de ressources pour y intégrer la dynamicité des requêtes et de l'utilisation des ressources. Nous avons mis en œuvre une infrastructure de cloud simulée, visant à contrôler l'exécution des différents services ainsi que le placement de ceux-ci. Ainsi notre approche permet de réduire la consommation d'énergie globale de l'infrastructure, ainsi que de limiter autant que possible les dégradations de performance.Cloud computing has become over the last years an important paradigm in the computing landscape. Its principle is to provide decentralized services and allows client to consume resources on a pay-as-you-go model. The increasing need for this type of service brings the service providers to increase the size of their infrastructures, to the extent that energy consumptions as well as operating costs are becoming important. Each cloud service provider has to provide for different types of requests. Infrastructure manager then have to host all the types of services together. That's why during this thesis, we tackled energy efficient resource management in the clouds. In order to do so, we first modeled and studied the initial service allocation problem, by computing approximated solutions given by heuristics, then comparing it to the optimal solution computed with a linear program solver. We then extended the model of resources to allow us to have a more global approach, by integrating the inherent heterogeneity of clusters and the cooling infrastructures. We then validated our model via simulation. Usually, the services must face different stages of workload, as well as utilization spikes. That's why we extended the model to include dynamicity of requests and resource usage, as well as the concept of powering on or off servers, or the cost of migrating a service from one host to another. We implemented a simulated cloud infrastructure, aiming at controlling the execution of the services as well as their placement. Thus, our approach enables the reduction of the global energy consumption of the infrastructure, and limits as much as possible degrading the performances

Improved self-management of datacenter systems applying machine learning

Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability

Modelling the assimilation and value of sensor information systems in data centres

Sensor Information Systems (SIS) refer to any IS that utilises sensor(s) that are directly or indirectly connected to other sensors or sensor networks in order to automate, inform and/or transform a given task or process or appliance. SIS are promoted as one of the best practices to overcome critical data centres issues such as inefficiency of Information Technology (IT) infrastructure usage, rising cost of operations, and the consumption and efficiency of energy. A review of the sensor, IS, and data centre literature shows that there is a dearth of theory driven empirical research on the utilisation of SIS in data centres, the factors that explain variations in applying SIS in data centres and the value of SIS use to data centres. The aim of this study is therefore to address the gap in the current literature and answer research questions. The research was conducted through a mixed method approach consisting of a literature review, exploratory case studies (pilot study) and large scale survey. Drawing from several theories of innovation adoption and value, and the five exploratory case studies, an integrative theoretical framework, which we call as TOIN (Technology, Organisation, Institutional and Natural Environment), was proposed to investigate the factors that explain the variation in the assimilation of SIS and the impact of SIS use on data centre’s operational and environmental performance. A series of hypotheses are developed by linking the TOIN factors to SIS assimilation and value in a two order-based model. The TOIN framework is tested using Partial Least Squares (PLS) path modelling and data collected from a global survey of 205 data centres. The findings indicate that SIS compatibility, perceived SIS risk, green IT orientation, and normative pressure directly influence the level of SIS usage among data centres. In addition, normative pressure, energy pressure, and natural environmental pressure indirectly affect the assimilation of SIS through influencing the organizational conditions for SIS use. These results are mostly sensitive to differences in data centre characteristics including age and type of data centre. Further, the test of the second order model show that the level of actual usage as well as the level of SIS mangers’ knowledge affect the operational and environmental performance of data centre operations including the facility, cooling and power, and computing platforms. The research represents one of the first studies on the use and value of SIS in general and in the context of data centre environment in particular. It makes an original contribution by proposing and validating the TOIN framework which can be used as a theoretical foundation for future and related studies. It also contributes original knowledge regarding how data centres are using SIS to tackle some of the operational, economic and environmental challenges. Thus, the research adds to the body of knowledge on intelligent systems, infrastructure management, green IS and energy informatics. Furthermore, the research extends the current innovation theories by incorporating the natural environment to study the technology use and value and shows the significance of natural environment considerations on organizations’ activities

Autonomic power and performance management for computing systems

Abstract—With the increased complexity of platforms, the growing demand of applications and data centers ’ servers sprawl, power consumption is reaching unsustainable limits. The need to improved power management is becoming essential for many reasons including reduced power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents a theoretical framework and methodology for autonomic power and performance management in e-business data centers. We optimize for power and performance (performance/watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach minimizing power while meeting performance constraints. Our experimental results show around 72 % savings in power as compared to static power management techniques and 69.8 % additional savings with both global and local optimizations.