I am a Computer Engineering PhD student in USC under supervision of Ramesh Govindan and Minlan Yu in NSL. I have B.Sc. and M.Sc. degree in Information Technology Engineering from Sharif University of Technology (Tehran, Iran).
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The high level goal of the project is to develop a monitoring module in the network hypervisor based on software defined networking paradigm that help the cloud datacenter operators monitor the network just as a single switch. One important usecase for this module is detecting the heavy hitters and hierarchical heavy hitters. Now imagine that every tenant wants this service. How can we allocate networking resources to keep all measurement tasks accurate using allocation and admission control schemes. Challenges include estimating accuracy of tasks without a ground-truth, admission control without knowing the size of a task beforehand, and per-switch resource allocation in a scalable way.
vCRIB: A virtualized Rule Information Base
Cloud operators increasingly need more and more fine-grained rules to better control individual network flows for various traffic management policies. In this paper, we explore automated rule management in the context of a system called vCRIB (a virtual Cloud Rule Information Base), which provides the abstraction of a centralized rule repository. The challenge in our approach is the design of algorithms that automatically off-load rule processing to overcome resource constraints on hypervisors and/or switches, while minimizing redirection traffic overhead and responding to system dynamics. vCRIB contains novel algorithms for finding feasible rule placements and adapting traffic overhead induced by rule placement in the face of traffic changes and VM migration. We demonstrate that vCRIB can find feasible rule placements with less than 10% traffic overhead even in cases where the traffic-optimal rule placement may be infeasible with respect to hypervisor CPU or memory constraints.
MRM: A service market for Map-Reduce
Data centers are a bunch of computers which are connected through an interconnection network usually offering computing, storage, and … services. On the other hand, the cloud computing model defines a novel role for mobile, social, and health related computing using the reliability and efficiency of these data centers. As a result of vast application of cloud computing, jobs on these datacenters can have various types with different desired requirements. For example some jobs are deadline driven while others just only need some computation resources. Duration and the amount of computation needed for each job is not available before running the job while they are important factors in defining the service model to guarantee the quality of service. In this project, first we propose a way to predict job duration in map-reduce framework and then use that to define a deadline-driven service market.