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As data centres continue to grow in size and complexity in order to respond to the increasing demand for computing resources, failures become the norm instead of an exception. To provide dependability at scale, traditional techniques to tolerate faults focus on reactive, redundant schemes. While the former relies on the checkpointing/restart of a job (which could incur significant overhead in a large-scale system), the latter replicates tasks, thus consuming extra resources to achieve higher reliability and availability of computing environments. Proactive fault-tolerance in large systems represents a new trend to avoid, cope with and recover from failures. However, different fault-tolerance schemes provide different levels of computing environment dependability at diverse costs to both providers and consumers. In this paper, two state-of-the-art fault-tolerance techniques are compared in terms of availability of computing environments to cloud consumers and energy costs to cloud providers. The results show that proactive fault-tolerance techniques outperform traditional redundancies in terms of costs to cloud users while providing available computing environments and services to consumers. However, the computing environment dependability provided by proactive fault-tolerance highly depends on failure prediction accuracy.

Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 Spot Instances (SIs) offer spare compute capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that waiting time until requested spots become fulfilled can be incredible high. In this paper, we propose a container migration-based solution to enhance the reliability of virtual cluster computing environments built on top of non-reserved EC2 pricing model instances. We compare the performance of our algorithm by executing different resource provisioning plans for running real-life workflow applications, constrained by user-defined deadline and budget Quality of Service (QoS) parameters. The results show that our solution is able to successfully conclude almost 98% of workflow applications and more than 99% of workflow tasks for on-demand- and spot block-based virtual compute environments. For SI-based virtual compute environments, our solution achieves similar results, completing more than 98% of workflow applications, and over 99% of workflow tasks, for a worse-case scenario.

The success of ever growing warehouse-sized Cloud data centers built to respond to the increasing demand for computing resources depends on the ability to provide reliability and availability at scale. In order to provide dependable and secure systems and services, one needs to implement security controls capable of avoiding, coping and recovering from failures. However, dependability and security of services at all cost is not a solution for Cloud providers. In this paper, two state-of-the-art fault-tolerance techniques are compared in terms of availability of services to consumers, and energy costs to Cloud providers. The results have shown that proactive fault-tolerance technique outperforms traditional redundancy in terms of cost to Cloud users, while providing available compute environments and services to consumers.

Nowadays, cloud data centers play an important role in modern Information Technology (IT) infrastructures, being progressively adopted in different scenarios. The proliferation of cloud has led companies and resource providers to build large warehouse-sized data centers, in an effort to respond to costumers demand for computing resources. Operating with powerful data centers requires a significant amount of electrical power, which translates into more heat to dissipate, possible thermal imbalances, and increased electricity bills. On the other hand, as data centers grow in size and in complexity, failure events become norms instead of exceptions. However, failures contribute to the energy waste as well, since preceding work of terminated tasks is lost. Therefore, today's cloud data centers are faced with the challenge of reducing operational costs through improved energy utilization while provisioning dependable service to customers. This chapter discusses the causes of power and energy consumption in data centers. The advantages brought by cloud computing on the management of data center resources are discussed, and the state of the art on schemes and strategies to improve power and energy efficiency of computing resources is reviewed. A practical case of energy-efficient and service-level agreement (SLA)-based management of resources, which analyzes and discusses the performance of three state-of-the-art scheduling algorithms to improve energy efficiency, is also included. This chapter concludes with a review of open challenges on strategies to improve power and energy efficiency in data centers.

Data Mining (DM) algorithms are able to construct models from available data that can be very useful for both business and science. However, a powerful representation language is required to express the highly complex models that stem from structured data. Multirelational algorithms can then take advantage of this representation for both data and models. The drawback is that for very large or highly complex domains multirelational algorithms may require long running times. This problem can be substantially reduced using parallel implementations. In this chapter, we present a survey on parallel approaches to run Inductive Logic Programming (ILP), a flavor of multirelational algorithms. We also analyze different scheduling approaches for those implementations and describe two applications where the proposed approaches may be very useful.

Virtualization technologies empower construction of flexible computing environments, promising an opportunity for energy and resource cost optimization, while enhancing system availability and achieving high performance. A crucial requirement for effective consolidation is to be able to efficiently utilize system resources for high-availability computing, and energy-efficiency optimization, so as to reduce operational costs and carbon footprints to the environment. In this work, we propose a consolidation technique to improve the performance of energy- and reliability-aware scheduling algorithms. For that, we carefully tune an energy optimization mechanism, which detects energy optimizing opportunities, and executes power- and failure-aware decision making algorithms to readjust virtual-to-physical mappings. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud trace logs. The results indicate that our strategy improves work per Joule ratio in about 9.7%, as well working-efficiency in almost 15.6%, maintaining similar levels of completion jobs.