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| 2. |
- Bauer, André, et al.
(författare)
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Chameleon : A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field
- 2019
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Ingår i: IEEE Transactions on Parallel and Distributed Systems. - : IEEE Computer Society. - 1045-9219 .- 1558-2183. ; 30:4, s. 800-813
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Tidskriftsartikel (refereegranskat)abstract
- Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalers in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalers one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time.
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| 3. |
- Eismann, Simon, et al.
(författare)
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The State of Serverless Applications: Collection, Characterization, and Community Consensus
- 2022
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Ingår i: IEEE Transactions on Software Engineering. - 0098-5589 .- 1939-3520. ; 48:10, s. 4152-4166
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Tidskriftsartikel (refereegranskat)abstract
- Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.
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| 5. |
- Ilyushkin, Alexey, et al.
(författare)
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An Experimental Performance Evaluation of Autoscalers for Complex Workflows
- 2018
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Ingår i: ACM Transactions on Modeling and Performance Evaluation of Computing Systems. - : Association for Computing Machinery (ACM). - 2376-3639 .- 2376-3647. ; 3:2
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Tidskriftsartikel (refereegranskat)abstract
- 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 quality of service 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 application model workflows, a popular 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 we 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.
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| 6. |
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| 7. |
- Ilyushkin, Alexey, et al.
(författare)
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An Experimental Performance Evaluation of Autoscaling Algorithms for Complex Workflows
- 2017
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Ingår i: ICPE '17 Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering. - New York, NY, USA : ACM. - 9781450344043 ; , s. 75-86
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Konferensbidrag (refereegranskat)abstract
- 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 experimentalperformance 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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| 8. |
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| 9. |
- Kimovski, Dragi, et al.
(författare)
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The Seventh Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf-2024)
- 2024
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Ingår i: COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024. - : ASSOC COMPUTING MACHINERY. - 9798400704451 ; , s. 163-164
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Konferensbidrag (refereegranskat)abstract
- It gives us immense pleasure to extend a warm welcome to you for the 2024 edition of the Workshop on Hot Topics in Cloud Computing Performance - HotCloudPerf 2024. Cloud computing represents one of the most significant transformations in the realm of IT infrastructure and usage. The adoption of global services within public clouds is on the rise, and the immensely lucrative global cloud market already sustains over 1 million IT-related jobs. However, optimizing the performance and efficiency of the IT services provided by both public and private clouds remains a considerable challenge. Emerging architectures, techniques, and real-world systems entail interactions with the computing continuum, serverless operation, everything as a service, complex workflows, auto-scaling and -tiering, etc. The extent to which traditional performance engineering, software engineering, and system design and analysis tools can contribute to understanding and engineering these emerging technologies is uncertain. The community requires practical tools and robust methodologies to address the hot topics in cloud computing performance effectively.
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