Added instrumentation to measure worker idle time before it receives its first request. 
Also added flags related to JIT deployment

Support is only for non-conditional chains.

Moving metrics handling to Prometheus from swagger stats.

Better support for different runtimes added

Conditional chain support added for Explicit chains. This introduces probabilistic chain support to Xanadu.

Added support to save long term metrics to couchDB

Support added for explicit function chains via a map file. 
Static map support added - map provided during deployment added
TODO: add support for dynamic maps during request

Metrics module added for periodic metrics push to log channel

Support added for multi-function deployment

Added different dispatcher side instrumentation. More to be added.

Added support for speculative deployment of function chains. If function MLE path is present then deploy those parts of the path which are  not already running to avoid cascading cold starts.

The probability model generated so far is generative in nature, in the sense that it tries to model the true probability distribution of the incoming requests. After a large number of requests have been received we can expect to have a reasonable MLE model of the probability distribution. We will then use the viterbi algorithm to detect the most likely path to be taken by a request. This can be used to preallocate the resources in the functions chain span to reduce cascading cold starts.
Path detection has been implemented.
TODO: resource pre-allocation