Maziyar Nazari

Implemented a proxy server for both REST and gRPC calls, intercepting REST and gRPC request/responses to generatestubs for Mocking purpose
Software Engineer Intern at Data Manager Setup team, C360 product

As a research assistant, I started working on a project called Toccoa. Generally speaking, this project is about cloud-scale packet-Level network analytics in software.

Another project which I have been working on roughly for a year, is called Distributed Container. Essentially, this is a manager implemented alongside with K8s which monitors containers deployed on physical hosts, gathers info about them and make decisions like setting some knobs/resource limits/etc dynamically in runtime to achieve higher resource utilization while preserving throughput

Teaching Assistant of the following courses:

Computer Networking

Operating Systems & Operating Systems Lab

Computer Architecture

Introduction to Computing Systems and C Programming

Artificial Intelligence

Efficient resource management in cloud computing research is a crucial problem because over-provisioning resources increases costs for cloud providers and cloud customers; under-provisioning resources increases the application latency, and it may violate service level agreement, which eventually makes cloud providers lose their customers and income. As a result, researchers have been striving to develop optimal resource management in the cloud computing environments in different ways, such as container placement, job scheduling and multi-resource scheduling. Machine learning techniques are extensively used in this area. In this paper, we present a comprehensive survey on the projects which leveraged machine learning techniques for resource management solutions in the cloud computing environment. At the end, we provide a comparison between these works. Furthermore, we propose some future directions that willguide researchers to advance this field

Containers are a popular mechanism used among application developers when deploying their systems on cloud platforms. Both developers and cloud providers are constantly looking to simplify container management, provisioning, and monitoring. In this paper, we present a container management layer that sits beside a container orchestrator that runs, what we call, Elastic Containers. Each elastic container contains multiple subcontainers that are connected to a centralized Global Cloud Manager (GCM). The GCM gathers subcontainer resource utilization information directly from inside each kernel running the subcontainers. The GCM then tries to efficiently and optimally distribute resources between the application subcontainers residing on a distributed environment.

Abstract: With the increasing need for more reactive services, and theneed to process large amounts of IoT data, edge clouds are emerging to enable applications to be run close to the users and/or devices. Following the trend in hyperscale clouds, applications are trending toward a microservices architecture where the application is decomposed into smaller pieces that can each run in its own container and communicate with each other over a network through well defined APIs. This improves the development effort and deployability, but also introduces inefficiencies in communication. In this paper, we rethink the communication model, and introduce the ability tocreate shared memory channels between containers supporting both a pub/sub model and streaming model. Our approachis not only applicable to the edge clouds but also beneficial incore cloud environments. Local communication is made more efficient, and remote communication is efficiently supported through synchronizing shared memory regions via RDMA.

Build and deploy virtual machine live migration in cloud environment, using OpenStack, NFS, VSphere