Bio

Gauri Joshi is a PhD candidate at MIT, advised by Prof. Gregory Wornell. She works on applying probability and coding theory to improve today’s cloud infrastructure. She received an S. M. in EECS from MIT in 2012, for which she received the William Martin memorial award for best thesis in Computer Science at MIT.

Before coming to MIT in 2010, she completed a B.Tech and M. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Bombay. She was awarded the Institute Gold Medal of IIT Bombay, for highest GPA across all majors.

Gauri has received several other awards and honors including the Schlumberger Faculty for the Future fellowship (2012-15) and the Claude E. Shannon Research Assistantship (2015-16). She has had summer internships at Bell Labs (2012) and Google (2013, 14).

Using Redundancy to Reduce Delay in Cloud Systems

Using Redundancy to Reduce Delay in Cloud Systems

It is estimated that by 2018, more than thirty percent of all digital content will be stored and processed on the cloud. The term ‘cloud’ refers to a shared pool of a large number of connected servers, used to host services such as Dropbox, Amazon EC2, Netflix etc. The sharing of resources provides scalability and flexibility to cloud systems, but it also causes randomness in the response time of individual servers, which can result in large and unpredictable delays experienced by users. My research develops techniques to use redundancy to reduce delay, while using the available resources efficiently.

In cloud storage and computing systems, a task (for e.g. searching for a term on Google, or accessing a file from Dropbox) experiences random queuing and service delays at the machine it is assigned to. To reduce the overall latency, we can launch replicas of the task on multiple machines and wait for the earliest copy to finish, albeit at the expense of extra computing and network resources. We develop a fundamental understanding how the randomness in the response time of a server affects latency and cost of computing resources. This helps us find cost-efficient strategies of launching and canceling redundant tasks to minimize latency.

Achieving low latency is even more challenging in streaming services such as Netflix and Youtube because they require fast, in-order playback of packets. Another focus of my research is to develop erasure codes to transmit redundant combinations of packets, and minimize the number of interruptions in playback