Service Mesh Performance
Standardizing Cloud Native Value Measurement
- 11
- Projects
- 4
- Years active
- 1
- In 2024 T3
Projects
112024
4 projectsTerm 3 · Sep-Nov · 1 project
CNCF Project Performance Test Dashboard
LFX Mentorship · Listed in the official cohort
In coordination with CNCF TAG Network, the current performance dashboard at is proposed to be incorporated into CNCF project level-moving criteria in that each CNCF project will be encouraged (mandated?) to incorporate ongoing performance tests into their build and release processes, resulting in ongoing performance analysis of each project.
- Go
- familiarity with HTTP/HTTPS performance testing tools
- Service Mesh
- grpc
- familiarity with containerization technologies
- like Docker would be helpful.
Term 2 · Jun-Aug · 1 project
Service Mesh Performance: Convergence of Network and Graph topologies
LFX Mentorship · Listed in the official cohort
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.
- Go
- familiarity with Service Mesh
- grpc
- docker
- kubernetes
Term 1 · Mar-May · 2 projects
Service Mesh Performance: Adaptive Load Control with Nighthawk
LFX Mentorship · Listed in the official cohort
The adaptive load controller operates by iteratively running optimization routines to ascertain the maximum load a system can sustain. Typically, this maximum load is determined by the system's capacity to handle requests per second (rps). The metrics, such as CPU usage and latency, collected from the system under test (SUT) serve as constraints to assess whether the SUT can sustain the imposed load. However, it lacks comprehensive lifecycle management functionalities. This hinders its adoption and limits its potential for continuous performance monitoring and optimization.
- Go
- familiarity with Service Mesh
- grpc
- docker
- kubernetes
Service Mesh Performance: Distributed Load Testing with Nighthawk
LFX Mentorship · Listed in the official cohort
Many performance benchmarks are limited to single instance load generation (single pod load generator). This limits the amount of traffic that can be generated to the output of the single machine that the benchmark tool runs on in or out of a cluster. Overcoming this limitation would allow for more flexible and robust testing. Distributed load testing in parallel poses a challenge when merging results without losing the precision we need to gain insight into the high tail percentiles. Distributed load testing offers insight into system behaviors that arguably more accurately represent real-world behaviors of services under load as that load comes from any number of sources.
- Go
- familiarity with HTTP/HTTPS performance testing tools
- Service Mesh
- grpc
- familiarity with containerization technologies
- like Docker would be helpful.
2023
2 projectsTerm 2 · Jun-Aug · 1 project
Service Mesh Performance IDE Plugin
LFX Mentorship · Listed in the official cohort
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.
- Cuelang
Term 1 · Mar-May · 1 project
Adaptive Load Controller II
LFX Mentorship · Listed in the official cohort
The adaptive load controller is to execute optimization routines recursivley to determine the maximum load a system can sustain. The maximum load is usually defined by the maximum requests per second (rps) the system can handle. The metrics (CPU usage, latency etc) collected from the system under test are the constraints we provide to judge whether a system under test (SUT) is sustaining the load. A use-case that fits very well is be the ability to use it to run performance tests on a schedule and track the maximum load a system can handle over time. This could give insights to performance improvements or degradations.
- Go
- grpc
- docker
- kubernetes
2022
4 projectsTerm 3 · Sep-Nov · 2 projects
Adaptive Load Controller
LFX Mentorship · Listed in the official cohort
The adaptive load controller is to execute optimization routines recursivley to determine the maximum load a system can sustain. The maximum load is usually defined by the maximum requests per second (rps) the system can handle. The metrics (CPU usage, latency etc) collected from the system under test are the constraints we provide to judge whether a system under test (SUT) is sustaining the load. A use-case that fits very well is be the ability to use it to run performance tests on a schedule and track the maximum load a system can handle over time. This could give insights to performance improvements or degradations.
- Go
- grpc
- docker
- kubernetes
Convergence of Network and Graph topologies
LFX Mentorship · Listed in the official cohort
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.
- cuelang
- Go
- neo4j
Term 0 · 2022 Summer (Jun-Aug) · 1 project
Implementation of MeshMark
LFX Mentorship · Listed in the official cohort
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. Implementation of this new performance index involves integration with Meshery, Prometheus, and more. Recommended Skills: Golang, ReactJS, GitHub Actions
Term 0 · 2022 Spring (Mar-May) · 1 project
Definition of MeshMark
LFX Mentorship · Listed in the official cohort
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.
- Analytics
- Algorithms
- Data Science
- (Golang and/or C++ helpful
- but not necessary)
2021
1 projectTerm 0 · 2021 Fall (Sep-Nov) · 1 project
Definition of MeshMark
LFX Mentorship · Listed in the official cohort
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.
- Analytics
- Algorithms
- Data Science
- (Golang and/or C++ helpful
- but not necessary)
