Dynamically adjusting the number of virtual machines (VMs) assigned to a cloud application to keep up with load changes and interference from other uses typically requires detailed application knowledge and an ability to know the future, neither of which are readily available to infrastructure service providers or application owners. The result is that systems need to be over-provisioned (costly), or risk missing their performance Service Level Objectives (SLOs) and have to pay penalties (also costly). AGILE deals with both issues: it uses wavelets to provide a medium-term resource demand prediction with enough lead time to start up new application server instances before performance falls short, and it uses dynamic VM cloning to reduce application startup times. Tests using RUBiS and Google cluster traces show that AGILE can predict varying resource demands over the medium-term with up to 3.42× better true positive rate and 0.34× the false positive rate than existing schemes. Given a target SLO violation rate, AGILE can efficiently handle dynamic application workloads, reducing both penalties and user dissatisfaction.

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AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.

Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey

Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery of best workload---resource pair based on application requirements of cloud users is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters-based resource provisioning technique is therefore required for efficient provisioning of resources. This research depicts a broad methodical literature analysis of cloud resource provisioning in general and cloud resource identification in specific. The existing research is categorized generally into various groups in the area of cloud resource provisioning. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource management, resource provisioning, resource provisioning evolution, different types of resource provisioning mechanisms and their comparisons, benefits and open issues are described. This research work also highlights the previous research, current status and future directions of resource provisioning and management in cloud computing.

Cloud resource provisioning: survey, status and future research directions

Q-aware

A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances

As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining an importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting QoS metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared with statically applied forecasting methods, for example, in an exemplary scenario on average by 37%. In a case study, between 55 and 75% of the violations of a given service level objective can be prevented by applying proactive resource provisioning based on the forecast results of our implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Self-adaptive workload classification and forecasting for proactive resource provisioning

As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.

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Next generation IT infrastructures are highly driven by virtualization technology. The latter enables flexible and efficient resource sharing allowing to improve system agility and reduce costs for IT services. Due to the sharing of resources and the increasing requirements of modern applications on I/O processing, the performance of storage systems is becoming a crucial factor. In particular, when migrating or consolidating different applications the impact on their performance behavior is often an open question. Performance modeling approaches help to answer such questions, a prerequisite, however, is to find an appropriate workload characterization that is both easy to obtain from applications as well as sufficient to capture the important characteristics of the application. In this paper, we present an automated workload characterization approach that extracts a workload model to represent the main aspects of I/O-intensive applications using relevant workload parameters, e.g., request size, read-write ratio, in virtualized environments. Once extracted, workload models can be used to emulate the workload performance behavior in real-world scenarios like migration and consolidation scenarios. We demonstrate our approach in the context of two case studies of representative system environments. We present an in-depth evaluation of our workload characterization approach showing its effectiveness in workload migration and consolidation scenarios. We use an IBM System z equipped with an IBM DS8700 and a Sun Fire system as state-of-the-art virtualized environments. Overall, the evaluation of our workload characterization approach shows promising results to capture the relevant factors of I/O-intensive applications.

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Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments

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Erich Amrehn

Papers

Self-adaptive workload classification and forecasting for proactive resource provisioning

Self-adaptive workload classification and forecasting for proactive resource provisioning

Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments

Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments