Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.

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Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing-the most extensive study to date. IEEE Software

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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 runtime. 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. N. R. HERBST ET AL.forecasting method executions on a machine with a Intel Core i7 CPU (2.7 GHz). The forecasting methods make use of only a single core as multi-threading is not yet fully supported by the existing implementations. APPROACH FOR WORKLOAD CLASSIFICATION AND FORECASTINGIn this section, we present a self-adaptive WCF process that can be used for selecting suitable forecasting methods at run-time.Over time, a WIB may change and develop in a way that affects its characteristics, that is, the class of the WIB is not fixed and needs to be updated periodically. Therefore, our classification process must be self-adaptive. Therefore, it must consider changes of the WIB and of given forecasting objectives to adapt the assignment of appropriate forecasting methods to given WIBs.An overview of the WCF process is sketched in Figure 2. Input of the WCF process is a trace of a WIB, a set of forecasting objectives, and possible feedback about the accuracy of the previous forecast. Essentially, we distinguish two phases in this process, the CLASSIFICATION phase and the FORECASTING phase. In the CLASSIFICATION phase, we extract the characteristics of a given WIB trace. Based on the identified characteristics, we use a decision tree (cf. 3) to classify the WIB and to select suitable forecasting methods. The assignment of forecasting methods also defines the class of the WIB. Then, in the FORECASTING phase, we apply the assigned forecasting...

Elasticity is one of the main features of cloud computing allowing customers to scale their resources based on the workload. Many autoscalers have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application based on the workload utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy as there is seldom enough analysis on the performance of the autoscalers in different operating conditions and with different applications. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structures. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the seven policies, we conduct various experiments and compare their performance in both pairwise and group comparisons. We report both individual and aggregated metrics. As many workflows have deadline requirements on the tasks, we study the effect of autoscaling on workflow deadlines. Additionally, we look into the effect of autoscaling on the accounted and hourly-based charged costs, and evaluate performance variability caused by the autoscaler selection for each group of workflow sizes. Our results highlight the trade-offs between the suggested policies, how they can impact meeting the deadlines, and how they perform in different operating conditions, thus enabling a better understanding of the current state-of-the-art. CCS Concepts: •General and reference →Cross-computing tools and techniques; •Networks →Cloud computing; •Computer systems organization →Self-organizing autonomic computing; •Software and its engineering →Virtual machines; ACM Transactions on Modeling and Performance Evaluation of Computing Systems This is a significantly extended and more comprehensive version of our ICPE 2017 article [ 28].

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