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
Consider many management policies each creating lots of OpenFlow rules such as Accept/Deny rules for access control. How can we pack them all in the switches on/off the shortest path with limited resources? There are three challenges: 1) rules can overlap and separating them may change their semantics, so we partition overlapping rules to decouple them. 2) In packing partitions on switches, we must put partitions with similar rules on the same switch to save resources. We solved this as a special version of bin-packing problem by an approximation algorithm with provable bound. Then we extend it to the case that partition similarities depend on the switch resource model (hardware vs. software switch) and 3) Forwarding flows imposes traffic overhead. For example, dropping a flow on ToR has one hop overhead comparing to drop it at server. We minimize traffic overhead using an online greedy algorithm. We evaluated the system using large scale simulation and a prototype using OVS and showed that vCRIB could find many non-trivial placements with less than 10% traffic overhead.
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.