[logseq-plugin-git:commit] 2025-06-05T08:36:10.944Z

pages/hls__STAF_2024_paper_8_1704401003970_0.md

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+file:: [STAF_2024_paper_8_1704401003970_0.pdf](../assets/STAF_2024_paper_8_1704401003970_0.pdf)
+file-path:: ../assets/STAF_2024_paper_8_1704401003970_0.pdf
+
+- Container orchestration tools assist in deploying, scaling, and managing containers, permitting alterations to the execution platform (environment) at runtime.
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+- self-adaptation capabilities. 
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+- appears to be underexplored
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+- nvestigating how container orchestration can augment MDE techniques for the effective design, implementation, and maintenance of adaptive cloud applications
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+- approach and toolchain for automatically generating and deploying a fully containerized distributed application from a component-and-connector model and leveraging both model- and platform-level dynamic adaptation and failure recovery capabilities to allow the application to respond to changes to the requirements or failures at runtime.
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+- A recent survey (Weyns et al. 2023) finds that large parts of industry already make significant use of self-adaptation to, e.g., increase system utility and decrease costs via auto-scaling, auto-tuning, or monitoring.
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+- Kubernetes 
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+- AWS Elastic Cloud
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+- RedHat OpenShift,
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+- DynaTrace
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+- lack of design guidelines
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+- need to support different system views
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+- increasing complexity
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+- integrate adaptation capabilities involving different artifacts and technologies along an MDEfor-cloud-computing toolchain.
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+- Model-level descriptions of system behavior
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+- Model-level descriptions of system structure
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+- Platform-level descriptions of computing system topology
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+- Platform-level descriptions of computing system resources
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+- is parameter adaptation: 
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+- Container orchestration platforms such as Kubernetes allow runtime changes to, e.g., the number of computing nodes and the way software components are assigned to these nodes. Dynamic redeployment can help reduce communication latency and improve system responsiveness
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+- we describe our work on facilitating the design and implementation of self-adaptive, containerized cloud applications through MDE, component-and-connector (C&C) architectures, the actor model, and the effective use of model- and platform-level adaptation capabilities.
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+- executable C&C model
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+- enerates and deploys a Kubernetes-based, distributed cloud application capable of autonomously recovering from node failures and adapting to changing requirements through runtime modifications to the model or the Kubernetes platform
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+- Cloud computing offers on-demand availability of system resources such as data storage or computing powe
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+- Cloud providers can give customers varying degrees of access and control of the underlying infrastructur
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+- Orchestration platforms such as Kubernetes facilitate, e.g., provisioning, deploying, scaling, and networking of containers across multiple nodes.
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+- Containerization and orchestration offer diverse adaptation capabilities, including redeployment, platform modification, and resource management
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+- our work aims to offer SaaS-type encapsulation of cloud platform resources and use container orchestration to automatically ensure their effective use.
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+- Elasticity (Mell & Grance 2011; Herbst et al. 2013) refers to the ability of the system to dynamically adapt by provisioning or de-provisioning resources in response to changes in workload, aiming to closely match available resources to the present demand at any specific point in time.
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+- Cloud-native applications leverage cloud platforms for elasticity, load balancing, and on-demand resource provisioning
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+- elasticity gives rise to enhancing resource efficiency as well, by optimizing performance under variable workloads, scaling up for peak loads and down during reduced activity
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+- EUREMA (
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+- xecutable runtime megamodels for developing adaptation engines through the design, execution, and adaptation of feedback loops
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+- Apart from executable runtime models, EUREMA uses DSL and megamodeling to provide an integrated view of several models and their relationships.
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+- PLASMA (Tajalli et al. 2010) adapts to changing requirements by generating plans based on user-provided goals and component specifications
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+- FUSION (Elkhodary et al. 2010) is a feature-oriented self-adaptive system that aims to find a different set of features to meet goals in case of violations. 
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+- DCL (Nakagawa et al. 2012) uses control loops to collect and analyze data, make decisions, and take action. 
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+- integrate adaptation at different levels: at the model-level by changing the way the structure and behavior of the system are described in the model
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+- at the platformlevel through dynamic redeployment,
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+- topology changes
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+- resource configuration enabled by existing container orchestration techniques and tools.
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+- container failure recovery techniques to make applications more resilient
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+- stateful behavior
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+- To the best of our knowledge, none of the existing approaches offer this combination of features
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+- existing MDE approach and toolchain
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+- adapt this MDE approach and toolchain for containerized applications so that the failure recovery capabilities of existing container management platforms are leveraged
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+- RQ3
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+- constructing a probability distribution for each route segment ahead based on multiple observations
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+- balance the proximity to the ground with the risk of being destroyed by threats
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+- electronic countermeasures (ECM)
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+- surveillance
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+- attack
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+- runtime changes to these mission types should be possible
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+- We will use the following metrics to evaluate the performance of different sets of simulation runs
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+- ls-type:: annotation
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+  1. Average destruction position (ADP):
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+  2. Average number of targets found (ANTF)
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+  3. Average mission success factor (AMSF)
+- strategy corresponds to a particular setting of some of the parameters that influence the behavior of the UAV
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+- altitude at which the UAVs fly, the formation that they fly in, and whether or not ECM 
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+- flying the UAVs at high altitudes, in a tight formation, and with ECM turned on gives rise to a conservative strategy suitable for surveillance-type missions where the long-term survival of the UAVs is paramount
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+- For attack-type missions, an aggressive strategy can be used in which UAVs fly low, in loose formation, and with ECM turned off.
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+- balanced strategy sits between these two extremes. 
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+- To support runtime changes to the mission type, our exemplar allows for strategies to be changed at runtime
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+- containerization
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+- description of executable component-and-connector
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+- model-level runtime information (Step 2) and model transformation (Step 3)
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+- xecutable code for the modeled system
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+- the model provided by the user supports adaptation
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+- an Adaptation Manager component
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+- In the second step, the user identifies the runtime information necessary to assess system performance (and prior adaptation steps), determine the need for adaptation, and select the most suitable adaptation steps
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+- t the use of the MAPE-K reference architecture (Kephart & Chess 2003) or suitable variants (Porter et al. 2020) to structure is recommended
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+- Approach and Prototype Toolchain
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+- asks the user to develop suitable monitors for this information,
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+- source model into a target model as directed by transformation rules,
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+- to facilitate the construction and integration of monitors 
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+- Figure 8 
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+- Section 3, we have used our approach and prototype to create and deploy several cloud applications that all exhibit different kinds of self-adapting behavior
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+- [:span]
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+- Figure 10 Sequence Diagram of UAV Simulation
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