https://www.mail-archive.com/server-dev@james.apache.org/msg71462.html

A good long term objective for the PMC is to drop RabbitMQ in
favor of pulsar (third parties could package their own components using
RabbitMQ if they wishes...)

This means:

• Solve the bugs that were found during the Pulsar MailQueue review
• Pulsar MailQueue need to allow listing blobs in order to be
  deduplication friendly.
• Provide an event bus based on Pulsar
• Provide a task manager based on Pulsar
• Package a distributed server backed by pulsar, deprecate then replace
  the current one.
• (optionally) support mail queue priorities

While contributions would of course be welcomed on this topic, we could
offer it as part of GSOC 2022, and we could co-mentor it with mentors of
the Pulsar community (see [3])

[3] https://lists.apache.org/thread/y9s7f6hmh51ky30l20yx0dlz458gw259

Would such a plan gain traction around here ?

James today provides a command line tool to do administration tasks like creating a domain, listing users, setting quota, etc.
It requires access to JMX port and even if lot of admins are confortable with such tools, to make our user base broader, we probably should expose the same commands in Rest and provide a fancy default web ui.
The task would need some basic skills on frontend tools to design an administration board, knowledge on what REST mean and enough Java understanding to add commands to existing Rest backend.
In the team, we have a strong focus on test (who want a mail server that is not tested enough ?) so we will explain and/or teach the student how to have the right test coverage of the features using modern tools like Cucumber, Selenium, rest-assured, etc.

Why ?

Alternatives like Postfix...

• Do not offer a unified view of the mail queue across nodes
• Requires statefull persistent storage

Given Apache James recent push to adopt a distributed mail queue based on Pulsar supporting delays (JAMES-3687), it starts making sense developing tooling for MX related tooling.

I propose myself to mentor a Gsoc on this topic.

Benefits for the student

At the end of this GSOC you will...

• Have a solid understanding of email relaying and associated mechanics
• Understand James modular architecture (mailet/ matcher / routes)
• Have a hands-on expertise in SQL / NoSQL working with technologies like Cassandra, Redis, JPA...
• Identify fix and solve architecture problems.
• Conduct performance tests and develop an operational mindset

Inventory...

James ships a couple of MX related tools within smtp-hooks/mailets in default packages. It would make sense to me to move those as an extension.

James supports today...

checks agains DNS blacklists. `DNSRBLHandler` or `URIRBLHandler` smtp hook for instance. This can be moved as an extension IMO.

We would need a little performance benchmark to document performance implications of activating DNS-RBL.

Finally as quoted by a gitter guy: it would make more sens to have this done as a MailHook rather as a RcptHook as it would avoid doing the same job again and over again for each recipients. See JAMES-3820 .

Grey listing. There's an existing implementation using JDBC as an underlying storage.

Move it as an extension.

Remove JDBC storage, propose 2 storage possibilities: in-memory for single node, REDIS for a distributed topology.

Some work around whitelist mailets? Move it as an extension, propose JPA, Cassandra, and XML configured implementations ? With a route to manage entries in there for JPA + Cassandra ?

I would expect a student to do his own little audit and come up with extra suggestions!

Beam library developers and Beam users would appreciate this : )

This project involves prototyping a few different solutions, so it will be large.

Background
Apache Traffic Control is a Content Delivery Network (CDN) control plane for large scale content distribution.

Traffic Control currently requires Apache Traffic Server as the underlying cache. Help us expand the scope by integrating with the very popular Varnish Cache.

There are multiple aspects to this project:

• Configuration Generation: Write software to build Varnish configuration files (VCL). This code will be implemented in our Traffic Ops and cache client side utilities, both written in Go.

• Health Monitoring: Implement monitoring of the Varnish cache health and performance. This code will run both in the Traffic Monitor component and within Varnish. Traffic Monitor is written in Go and Varnish is written in C.

• Testing: Adding automated tests for new code

Skills:

• Proficiency in Go is required
• A basic knowledge of HTTP and caching is preferred, but not required for this project.

This is a placeholder for Apache SkyWalking GSOC 2023 ideas, we are currently brainstorming for projects and will update asap. 

There will be at least two projects, one around AIOps algorithms.

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

The ShardingSphere SQL federation engine provides support for complex SQL statements, and it can well support cross-database join queries, subqueries, aggregation queries and other statements. An important part of SQL federation engine is to convert the SQL statement parsed by ShardingSphere into SqlNode, so that Calcite can be used to implement SQL optimization and federated query.

Task

This issue is to solve the MySQL exception that occurs during SQLNodeConverterEngine conversion. The specific case list is as follows.

• select_char
• select_extract
• select_from_dual
• select_from_with_table
• select_group_by_with_having_and_window
• select_not_between_with_single_table
• select_not_in_with_single_table
• select_substring
• select_trim
• select_weight_string
• select_where_with_bit_expr_with_ampersand
• select_where_with_bit_expr_with_caret
• select_where_with_bit_expr_with_div
• select_where_with_bit_expr_with_minus_interval
• select_where_with_bit_expr_with_mod
• select_where_with_bit_expr_with_mod_sign
• select_where_with_bit_expr_with_plus_interval
• select_where_with_bit_expr_with_signed_left_shift
• select_where_with_bit_expr_with_signed_right_shift
• select_where_with_bit_expr_with_vertical_bar
• select_where_with_boolean_primary_with_comparison_subquery
• select_where_with_boolean_primary_with_is
• select_where_with_boolean_primary_with_is_not
• select_where_with_boolean_primary_with_null_safe
• select_where_with_expr_with_and_sign
• select_where_with_expr_with_is
• select_where_with_expr_with_is_not
• select_where_with_expr_with_not
• select_where_with_expr_with_not_sign
• select_where_with_expr_with_or_sign
• select_where_with_expr_with_xor
• select_where_with_predicate_with_in_subquery
• select_where_with_predicate_with_regexp
• select_where_with_predicate_with_sounds_like
• select_where_with_simple_expr_with_collate
• select_where_with_simple_expr_with_match
• select_where_with_simple_expr_with_not
• select_where_with_simple_expr_with_odbc_escape_syntax
• select_where_with_simple_expr_with_row
• select_where_with_simple_expr_with_tilde
• select_where_with_simple_expr_with_variable
• select_window_function
• select_with_assignment_operator
• select_with_assignment_operator_and_keyword
• select_with_case_expression
• select_with_collate_with_marker
• select_with_date_format_function
• select_with_exists_sub_query_with_project
• select_with_function_name
• select_with_json_value_return_type
• select_with_match_against
• select_with_regexp
• select_with_schema_name_in_column_projection
• select_with_schema_name_in_shorthand_projection
• select_with_spatial_function
• select_with_trim_expr
• select_with_trim_expr_from_expr

You need to compare the difference between actual and expected, and then correct the logic in SQLNodeConverterEngine so that actual can be consistent with expected.

After you make changes, remember to add case to SUPPORTED_SQL_CASE_IDS to ensure it can be tested.

 
Notice, these issues can be a good example.
https://github.com/apache/shardingsphere/pull/14492

Relevant Skills

 
1. Master JAVA language

2. Have a basic understanding of Antlr g4 file

3. Be familiar with MySQL and Calcite SqlNode

Targets files

 
SQLNodeConverterEngineIT

https://github.com/apache/shardingsphere/blob/master/test/it/optimizer/src/test/java/org/apache/shardingsphere/test/it/optimize/SQLNodeConverterEngineIT.java 

Mentor

Zhengqiang Duan, PMC of Apache ShardingSphere, duanzhengqiang@apache.org

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

ShardingSphere provides two adapters: ShardingSphere-JDBC and ShardingSphere-Proxy.

Now, ShardingSphere uses logback for logging, but consider the following situations:

• Users may need to switch the logging framework to meet special needs, such as log4j2 can provide better asynchronous performance;
• When using the JDBC adapter, the user application may not use logback, which may cause some conflicts.

Why doesn't the log facade suffice? Because ShardingSphere provides users with clustered logging configurations (such as changing the log level online), this requires dynamic construction of logger, which cannot be achieved with only the log facade.

Task

1. Design and implement logging SPI to support multiple logging frameworks (such as logback and log4j2)
2. Allow users to choose which logging framework to use through the logging rule

Relevant Skills

1. Master JAVA language

2. Basic knowledge of logback and log4j2

3. Maven

Mentor

Longtao Jiang, Committer of Apache ShardingSphere, jianglongtao@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

ShardingSphere has designed its own metadata database to simulate metadata queries that support various databases.

More details:

https://github.com/apache/shardingsphere/issues/21268
https://github.com/apache/shardingsphere/issues/22052

Task

• Support PostgreSQL And openGauss `\d tableName`
• Support PostgreSQL And openGauss `\d+`
• Support PostgreSQL And openGauss `\d+ tableName`
• Support PostgreSQL And openGauss `l`
• Support query for MySQL metadata `TABLES`
• Support query for MySQL metadata `COLUMNS`
• Support query for MySQL metadata `schemata`
• Support query for MySQL metadata `ENGINES`
• Support query for MySQL metadata `FILES`
• Support query for MySQL metadata `VIEWS`

Notice, these issues can be a good example.

https://github.com/apache/shardingsphere/pull/22053
https://github.com/apache/shardingsphere/pull/22057/
https://github.com/apache/shardingsphere/pull/22166/
https://github.com/apache/shardingsphere/pull/22182

Relevant Skills

•  Master JAVA language
•  Have a basic understanding of Zookeeper
•  Be familiar with MySQL/Postgres SQLs 

Mentor

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apache.org

Zhengqiang Duan, PMC of Apache ShardingSphere, duanzhengqiang@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org/
Github: https://github.com/apache/shardingsphere 

Background

There is a proposal about new CRD Cluster and ComputeNode as belows:

• WIP: [New Feature] Introduce new CRD Cluster #167
• [Feat] Introduce new CRD as ComputeNode for better usability #166

Currently we try to promote ComputeNode as major CRD to represent a special ShardingSphere Proxy deployment. And plan to use Cluster indicating a special ShardingSphere Proxy cluster.

Task

This issue is to enhance ComputeNode reconciliation availability. The specific case list is as follows.

•  Add IT test case for Deployment spec volume
•  Add IT test case for Deployment spec template init containers
•  Add IT test case for Deployment spec template spec containers
•  Add IT test case for Deployment spec volume mounts
•  Add IT test case for Deployment spec container ports
•  Add IT test case for Deployment spec container image tag
•  Add IT test case for Service spec ports
•  Add IT test case for ConfigMap data serverconfig
•  Add IT test case for ConfigMap data logback
   
  Notice, these issues can be a good example.
• chore: add more Ginkgo tests for ComputeNode #203

Relevant Skills

1. Master Go language, Ginkgo test framework
2. Have a basic understanding of Apache ShardingSphere Concepts
3. Be familiar with Kubernetes Operator, kubebuilder framework

Targets files

ComputeNode IT - https://github.com/apache/shardingsphere-on-cloud/blob/main/shardingsphere-operator/pkg/reconcile/computenode/compute_node_test.go

Mentor

Liyao Miao, Committer of Apache ShardingSphere,  miaoliyao@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

The community just added CDC (change data capture) feature recently. Change feed will be published in created network connection after logging in, then it could be consumed.

Since Kafka is popular distributed event streaming platform, it's useful to import change feed into Kafka for later processing.

Task

1. Familiar with ShardingSphere CDC client usage, create publication and subscribe change feed.
2. Familiar with Kafka connector development, develop source connector, integrate with ShardingSphere CDC. Persist change feed to Kafka topics properly.

Relevant Skills

1. Java language

2. Basic knowledge of CDC and Kafka

3. Maven

References

• https://github.com/apache/shardingsphere/issues/22500
• https://kafka.apache.org/documentation/#connect_development
• https://github.com/apache/kafka/tree/trunk/connect/file/src
• https://github.com/confluentinc/kafka-connect-jdbc

Mentor

Hongsheng Zhong, PMC of Apache ShardingSphere, zhonghongsheng@apache.org

Apache RocketMQ

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Page: https://rocketmq.apache.org
Github: https://github.com/apache/rocketmq

Background

RocketMQ 5.0 client has been released recently, we need to integrate it with Spring.

Task

1. Familiar with RocketMQ 5.0 java client usage, you could see more details from https://github.com/apache/rocketmq-clients/tree/master/java and https://rocketmq.apache.org/docs/quickStart/01quickstart
2. Integrate with Spring.

Relevant Skills

1. Java language
2. Basic knowledge of RocketMQ 5.0
3. Spring

Mentor

Yangkun Ai, PMC of Apache RocketMQ, aaronai@apache.org

Placeholder for tasks that could be undertaken in this year's GSoC.

Ideas:

• Design an updated summary statistics API for use with Java 8 streams based on the summary statistic implementations in the Commons Math stat.descriptive package including moments, rank and summary sub-packages.

Add implementations of extended precision floating point numbers.

An extended precision floating point number is a series of floating-point numbers that are non-overlapping such that:

double-double (a, b):
            |a| > |b|
            a == a + b

Common representations are double-double and quad-double (see for example David Bailey's paper on a quad-double library: QD).

Many computations in the Commons Numbers and Statistics libraries use extended precision computations where the accumulated error of a double would lead to complete cancellation of all significant bits; or create intermediate overflow of integer values.

This project would formalise the code underlying these use cases with a generic library applicable for use in the case where the result is expected to be a finite value and using Java's BigDecimal and/or BigInteger negatively impacts performance.

An example would be the average of long values where the intermediate sum overflows or the conversion to a double loses bits:

            long[] values = {Long.MAX_VALUE, Long.MAX_VALUE};
            System.out.println(Arrays.stream(values).average().getAsDouble()); System.out.println(Arrays.stream(values).mapToObj(BigDecimal::valueOf)
            .reduce(BigDecimal.ZERO, BigDecimal::add)
            .divide(BigDecimal.valueOf(values.length)).doubleValue());
            long[] values2 = {Long.MAX_VALUE, Long.MIN_VALUE};
            System.out.println(Arrays.stream(values2).asDoubleStream().average().getAsDouble()); System.out.println(Arrays.stream(values2).mapToObj(BigDecimal::valueOf)
               .reduce(BigDecimal.ZERO, BigDecimal::add)
            .divide(BigDecimal.valueOf(values2.length)).doubleValue());
            

Outputs:

-1.0
            9.223372036854776E18
            0.0
            -0.5

Placeholder for tasks that could be undertaken in this year's GSoC.

Ideas (extracted from the "dev" ML):

1. Redesign and modularize the "ml" package
   -> main goal: enable multi-thread usage.
2. Abstract the linear algebra utilities
   -> main goal: allow switching to alternative implementations.
3. Redesign and modularize the "random" package
   -> main goal: general support of low-discrepancy sequences.
4. Refactor and modularize the "special" package
   -> main goals: ensure accuracy and performance and better API,
   add other functions.
5. Upgrade the test suite to Junit 5
   -> additional goal: collect a list of "odd" expectations.

Other suggestions welcome, as well as

• delineating additional and/or intermediate goals,
• signalling potential pitfalls and/or alternative approaches to the intended goal(s).

A placeholder ticket, to link other issues and organize tasks related to the 1.0 release of Commons Imaging.

The 1.0 release of Commons Imaging has been postponed several times. Now we have a more clear idea of what's necessary for the 1.0 (see issues with fixVersion 1.0 and 1.0-alpha3, and other open issues), and the tasks are interesting as it involves both basic and advanced programming for tasks such as organize how test images are loaded, or work on performance improvements at byte level and following image format specifications.

The tasks are not too hard to follow, as normally there are example images that need to work with Imaging, as well as other libraries in C, C++, Rust, PHP, etc., that process these images correctly. Our goal with this issue is to a) improve our docs, b) improve our tests, c) fix possible security issues, d) get the parsers in Commons Imaging ready for the 1.0 release.

Assigning the label for GSoC 2023, and full time. Although it would be possible to work on a smaller set of tasks for 1.0 as a part time too.

As a development framework that is closely related to users, Dubbo may have a huge impact on users if any problems occur during the iteration process. Therefore, Dubbo needs a complete set of automated regression testing tools.
At present, Dubbo already has a set of testing tools based on docker-compose, but this set of tools cannot test the compatibility in the kubernetes environment. At the same time, we also need a more reliable test case construction system to ensure that the test cases are sufficiently complete.

As a development framework closely related to users, Dubbo provides many functional features (such as configuring timeouts, retries, etc.). We hope that a tool can be given to users to scan which features are used, which features are deprecated, which ones will be deprecated in the future, and so on. Based on this tool, we can provide users with a better migration solution.
Suggestion: You can consider based on static code scanning or javaagent implementation.

Dubbo supports the communication mode based on the gRPC protocol through Triple. For this reason, Dubbo has developed a compiling plug-in for proto files based on jprotoc. Due to the activeness of jprotoc, currently Dubbo compiler cannot run well on the latest protobuf version. Therefore, we need to consider implementing a new compiler with reference to gRPC.

Dubbo is a development framework that is closely related to users, and many usages by users may cause exceptions handled by Dubbo. Usually, in this case, users can only judge through logs. We hope to provide an i18n localized log output tool to provide users with a more friendly log troubleshooting experience.

As more and more projects start to develop based on Gradle and profit from Gradle, Dubbo also hopes to migrate to the Gradle project. This task requires you to transform the dubbo project[1] into a gradle project.

 [1] https://github.com/apache/dubbo

Dubbo Admin is a console of Dubbo. Today, Dubbo's observability is becoming more and more powerful. We need to directly observe some indicators of Dubbo on Dubbo Admin, and even put forward suggestions for users to improve problems.

Background

Dubbo currently supports the rest protocol based on http1, and the triple protocol based on http2, but currently the two protocols based on the http protocol are implemented independently, and at the same time, they cannot replace the underlying implementation, and their respective implementation costs are relatively high.

Target

In order to reduce maintenance costs, we hope to be able to abstract http. The underlying implementation of the target implementation of http has nothing to do with the protocol, and we hope that different protocols can reuse related implementations.

Background

At present, the abstraction of connection by client in different protocols in Dubbo is not perfect. For example, there is a big discrepancy between the client abstraction of connection in dubbo and triple protocols. As a result, the enhancement of connection-related functions in the client is more complicated, and the implementation cannot be reused. At the same time, the client also needs to implement a lot of repetitive code when extending the protocol.

Target

Reduce the complexity of the client part when extending the protocol, and increase the reuse of connection-related modules.

Background

Dubbo currently supports protobuf as a serialization method. Protobuf relies on proto (Idl) for code generation, but currently lacks tools for managing Idl files. For example, for java users, proto files are used for each compilation. It is more troublesome, and everyone is used to using jar packages for dependencies.

Target

Implement an Idl management and control platform, support idl files to automatically generate dependency packages in various languages, and push them to relevant dependency warehouses

Our Commons components use Commons Skin, a skin, or theme, for Apache Maven Site.

Our skin uses Bootstrap 2.x, but Bootstrap is already at 5.x release, and we are missing several improvements (UIUX, accessibility, browser compatibility) and JS/CSS bugs fixed over the years.

Work happening on Apache Maven Skins. Maybe we could adapt/use that one?

https://issues.apache.org/jira/browse/MSKINS-97

The Airavata Django Portal [1] allows users to create, execute and monitor computational experiments. However, when a user wants to then post-process or visualize the output of that computational experiment they must then download the output files and run tools that they may have on their computer or other systems. By integrating with JupyterHub the Django Portal can give users an environment in which they can explore the experiment's output data and gain insights.

The main requirements are:

• from the Django Portal a user can click a button and navigate to a JupyterHub instance that the user is immediately logged into using single sign on
• the user can save the Jupyter notebook and later retrieve it
• the user's files are available within the context of the running Jupyter instance
  • ideally users can also generate new outputs in the Jupyter instance and have them saved back in their portal data storage
• users can share their notebooks with other portal users
• (bonus) portal admins can suggest notebooks to use with specific applications so that with one click a user can open an experiment in a provided notebook
• users can manage their notebooks and can, for example, clone a notebook

[1] https://github.com/apache/airavata-django-portal

Integrate Apache Superset (https://superset.apache.org/) to visualize Airavata Catalogs (https://github.com/apache/airavata/tree/master/modules/registry) 

• Examples like this and stack overflow threads seem to indicate it is possible - https://medium.com/@s.akashb/apache-superset-integration-with-keycloak-a302840c290c 

• Integrate with Custos 
• We can start out by directly interfacing with MariaDB, but need to explore if we can write a superset DB Driver following the Hive example - https://github.com/apache/superset/blob/0409b12a55e893d88f6e992a7df247841a2da8f0/superset/db_engines/hive.py

1. UI Framework 
   1. To host the jupyter environment we will need to envolop the notebooks in a user interface and connect it with Apache Airavata services 
   2. Leverage Airavata communications from within the Django Portal - https://github.com/apache/airavata-django-portal 
   3. Explore if the platform is better to be developed as VSCode extensions leveraging jupyter extensions like - https://github.com/Microsoft/vscode-jupyter
   4. Alternatively, explore developing a standalone native application using ElectronJS
2. Draft up a platform architecture - Airavata based infrastructure with functionality similar to collab. 
3. Authenticate with Airavata Custos Framework - https://github.com/apache/airavata-custos 
4. Extend Notebook filesystem using the virtual file system approaching integration with Airavata based storage and catalog
5. Make the notebooks registered with Airavata app catalog and experiment catalog. 

Advanced Possibilities:

Explore Multi-tenanted JupyterHub 

• Can K8 namespace isolation accomplish?
• Make deployment of Jupyter support as part of the default core
• Data and the user-level tenancy can be assumed, how to make sure infrastructure can isolate them, like not one gateway crashing a hosting environment.

1. How to leverage computational resources jupypter hub

Gateway admins need period reports for various reporting and planning. 

Features Include:

• Compute resources across that had at least one job submitted during the period <start date - End date>
• User groups created within a given period and how many users are in those and with permission levels and also number of jobs each user have submitted.
• List applications and number of jobs for each applications for a given period and group them by job status.
• Number of users that at least submitted a single job for the period <start date - End date>
• Total number of Unique Users
• User Registration Trends
• Number of experiments for a given period <Start date - End date> grouped by the experiment status
• The total cpu-hours used by a users, sorted, quarterly, plotted over a period of time
• The total cpu-hours consumed by application, sorted, quarterly, plotted over a period of time

As discussed on the architecture mailing list [1] and summarized at [2], Airavata will need to develop a metascheduler. In the short term, a user request (demeler, gobert) is to have airavata throttle jobs to resources. In the future more informed scheduling strategies needs to be integrated. Hopefully, the actual scheduling algorithms can be borrowed from third party implementations.

[1] - http://markmail.org/message/tdae5y3togyq4duv
[2] - https://cwiki.apache.org/confluence/display/AIRAVATA/Airavata+Metascheduler

Complete all transports in MFT

• Currently SCP, S3 is known to work
• Others need effort to optimize, test, and declare readiness
• Develop a complete a fully functional MFT Command-line interface
• Have a feature-complete Python SDK
• A minimum implementation will be prvoided, students need to complete it and test it. 

Custos does not have the capabilities to efficiently backup and restore a live instance. This is essential for high available services. 

Using SEAGrid Rich Client as an example, develop a native application based on electronJS to mimic Airavata Django Portal.

Reference example - https://github.com/SciGaP/seagrid-rich-client