Service Mesh Performance

Create MeshMark provides a universal performance index to gauge your mesh’s efficiency against deployments in other organizations’ environments. MeshMark functions as a service mesh performance index (a scale) to provide people the ability to weigh the value of their service mesh versus the overhead of their service mesh and assess whether they are getting out of the mesh what they are “paying” for in it. Work with maintainers from Layer5, Intel, Red Hat, and HashiCorp on researching cloud native infrastructure performance. Internship involves: machine learning, adaptive algorithms, running and analyzing performance statistics.

Description: Create MeshMark provides a universal performance index to gauge your mesh’s efficiency against deployments in other organizations’ environments. MeshMark functions as a service mesh performance index (a scale) to provide people the ability to weigh the value of their service mesh versus the overhead of their service mesh and assess whether they are getting out of the mesh what they are “paying” for in it. Work with maintainers from Layer5, Intel, Red Hat, and HashiCorp on researching cloud native infrastructure performance. Internship involves: machine learning, adaptive algorithms, running and analyzing performance statistics.

The objective of this project is to develop IDE plugins that can enhance the developer experience while working with Service Mesh Performance Performance Profiles. The proposed plugins will leverage technologies such as golang and cuelang to provide features such as syntax highlighting, auto-completion, validation, and rendering previews for Service Mesh Performance profile and model definitions. - Expected outcome: - 1. Release VS Code Extension - 2. Syntax Highlighting and Auto-completion: The plugin can fetch SMP Model definitions such as cloud-native components and their relationships. This information can be used to provide syntax highlighting and auto-completion for these definitions in the JSON files, making it easier for developers to write error-free code. - 3. Validation and Reference: For Meshery MeshModel definitions such as cloud-native components and their relationships, the plugin can use the CUE language to provide validation for the CUE input and preview the rendering result. The plugin can also fetch the SMP Model schemas and display them in the IDE for reference.

Opens the door to leveraging algorithms in the areas of Centrality, Community Detection, Pathfinding, Topological Link Prediction, etc. Bringing to bear advances made in Machine Learning / AI / recommendation systems, fraud detection could really help to derive meaning and comprehension for future tools. Another example is how ML + graph approaches are used to find and determine the optimal molecular structure of atoms such that desired physical properties are targeted. This approach could be applied to the problem of workload sizing and estimation for service mesh operators and would-be adopters. Expected outcome: - Use Neo4j's ability to create graph projections, which copy a subgraph to RAM so that algorithms can be efficiently run.

Use Neo4j's ability to create graph projections, which copy a subgraph to RAM so that algorithms can be efficiently run. This opens the door to leveraging algorithms in the areas of Centrality, Community Detection, Pathfinding, Topological Link Prediction, etc. Bringing to bear advances made in Machine Learning / AI / recommendation systems, fraud detection could really help to derive meaning and comprehension for future tools. Another example is how ML + graph approaches are used to find and determine the optimal molecular structure of atoms such that desired physical properties are targeted. This approach could be applied to the problem of workload sizing and estimation for service mesh operators and would-be adopters.