QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts

Large inter-datacenter transfers are crucial for cloud service efficiency and are increasingly used by organizations that have dedicated wide area networks between datacenters. A recent work uses multicast forwarding trees to reduce the bandwidth needs and improve completion times of point-to-multipoint transfers. Using a single forwarding tree per transfer, however, leads to poor performance because the slowest receiver dictates the completion time for all receivers. Using multiple forwarding trees per transfer alleviates this concern--the average receiver could finish early; however, if done naively, bandwidth usage would also increase and it is apriori unclear how best to partition receivers, how to construct the multiple trees and how to determine the rate and schedule of flows on these trees. This paper presents QuickCast, a first solution to these problems. Using simulations on real-world network topologies, we see that QuickCast can speed up the average receiver's completion time by as much as $10\times$ while only using $1.04\times$ more bandwidth; further, the completion time for all receivers also improves by as much as $1.6\times$ faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018, Honolulu, H

Enabling Richer Insight Into Runtime Executions Of Systems

Systems software of very large scales are being heavily used today in various important scenarios such as online retail, banking, content services, web search and social networks. As the scale of functionality and complexity grows in these software, managing the implementations becomes a considerable challenge for developers, designers and maintainers. Software needs to be constantly monitored and tuned for optimal efficiency and user satisfaction. With large scale, these systems incorporate significant degrees of asynchrony, parallelism and distributed executions, reducing the manageability of software including performance management. Adding to the complexity, developers are under pressure between developing new functionality for customers and maintaining existing programs. This dissertation argues that the manual effort currently required to manage performance of these systems is very high, and can be automated to both reduce the likelihood of problems and quickly fix them once identified. The execution logs from these systems are easily available and provide rich information about the internals at runtime for diagnosis purposes, but the volume of logs is simply too large for today\u27s techniques. Developers hence spend many human hours observing and investigating executions of their systems during development and diagnosis of software, for performance management. This dissertation proposes the application of machine learning techniques to automatically analyze logs from executions, to challenging tasks in different phases of the software lifecycle. It is shown that the careful application of statistical techniques to features extracted from instrumentation, can distill the rich log data into easily comprehensible forms for the developers