| 1. |
- Domaschka, Jörg, et al.
(author)
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Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds
- 2020
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In: Managing Distributed Cloud Applications and Infrastructure. - Cham : Palgrave Macmillan. - 9783030398620 - 9783030398637 ; , s. 1-25
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Book chapter (peer-reviewed)abstract
- The complexity of computing along the cloud-to-edge continuum presents significant challenges to ICT operations and in particular reliable capacity planning and resource provisioning to meet unpredictable, fluctuating, and mobile demand. This chapter presents a high-level conceptual overview of RECAP—an architectural innovation to support reliable capacity provisioning for distributed clouds—and its operational modes and functional building blocks. In addition, the major design concepts informing its design—namely separation of concerns, model-centricism, modular design, and machine learning and artificial intelligence for IT operations—are also discussed.
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| 2. |
- Leznik, Mark, et al.
(author)
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Multivariate Time Series Synthesis Using Generative Adversarial Networks
- 2021
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In: ICPE 2021. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450381949 ; , s. 43-50
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Conference paper (peer-reviewed)abstract
- Collection and analysis of distributed (cloud) computing workloads allows for a deeper understanding of user and system behavior and is necessary for efficient operation of infrastructures and applications. The availability of such workload data is however often limited as most cloud infrastructures are commercially operated and monitoring data is considered proprietary or falls under GPDR regulations. This work investigates the generation of synthetic workloads using Generative Adversarial Networks and addresses a current need for more data and better tools for workload generation. Resource utilization measurements such as the utilization rates of Content Delivery Network (CDN) caches are generated and a comparative evaluation pipeline using descriptive statistics and time-series analysis is developed to assess the statistical similarity of generated and measured workloads. We use CDN data open sourced by us in a data generation pipeline as well as back-end ISP workload data to demonstrate the multivariate synthesis capability of our approach. The work contributes a generation method for multivariate time series workload generation that can provide arbitrary amounts of statistically similar data sets based on small subsets of real data. The presented technique shows promising results, in particular for heterogeneous workloads not too irregular in temporal behavior.
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| 3. |
- Stier, Christian, et al.
(author)
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Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation
- 2018
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In: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering. - New York, NY, USA : ACM Digital Library. ; , s. 184-191
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Conference paper (peer-reviewed)abstract
- Infrastructure as a Service (IaaS) Cloud services allow users to deploy distributed applications in a virtualized environment without having to customize their applications to a specific Platform as a Service (PaaS) stack. It is common practice to host multiple Virtual Machines (VMs) on the same server to save resources. Traditionally, IaaS data center management required manual effort for optimization, e.g. by consolidating VM placement based on changes in usage patterns. Many resource management algorithms and frameworks have been developed to automate this process. Resource management algorithms are typically tested via experimentation or using simulation. The main drawback of both approaches is the high effort required to conduct the testing. Existing Cloud or IaaS simulators require the algorithm engineer to reimplement their algorithm against the simulator's API. Furthermore, the engineer manually needs to define the workload model used for algorithm testing. We propose an approach for the simulative analysis of IaaS Cloud infrastructure that allows algorithm engineers and data center operators to evaluate optimization algorithms without investing additional effort to reimplement them in a simulation environment. By leveraging runtime monitoring data, we automatically construct the simulation models used to test the algorithms. Our validation shows that algorithm tests conducted using our IaaS Cloud simulator match the measured behavior on actual hardware.
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| 4. |
- Östberg, Per-Olov, et al.
(author)
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Application Optimisation : Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications
- 2020
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In: Managing Distributed Cloud Applications and Infrastructure. - Cham : Palgrave Macmillan. - 9783030398620 - 9783030398637 ; , s. 51-68
-
Book chapter (peer-reviewed)abstract
- Optimisation of (the configuration and deployment of) distributed cloud applications is a complex problem that requires understanding factors such as infrastructure and application topologies, workload arrival and propagation patterns, and the predictability and variations of user behaviour. This chapter outlines the RECAP approach to application optimisation and presents its framework for joint modelling of applications, workloads, and the propagation of workloads in applications and networks. The interaction of the models and algorithms developed is described and presented along with the tools that build on them. Contributions in modelling, characterisation, and autoscaling of applications, as well as prediction and generation of workloads, are presented and discussed in the context of optimisation of distributed cloud applications operating in complex heterogeneous resource environments.
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| 5. |
- Östberg, Per-Olov, 1973-, et al.
(author)
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The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation
- 2014
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In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science. - 9781479940936 ; , s. 26-31
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Conference paper (peer-reviewed)abstract
- Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.
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