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.

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