Projects Current Projects

Please, see the list of LCCN offered projects:

Project Title:

MPEG-DASH Live Streaming in Unstable Environment

Supervisors:
Aviel Glam (Rafael), Itzik Ashkenazi,
Description:
MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) is a vendor independent, international standard ratified in 2012. One of the main benefits of MPEG-DASH is reduction of startup delays and buffering/stalls during the video and continued adaptation to the bandwidth situation of the client. Today, MPEG-DASH is gaining more and more deployments, accelerated by services such as Netflix or Google, which recently switched to this new standard. With these two major sources of internet traffic, 50% of total internet traffic is already MPEG-DASH. The basic idea of MPEG-DASH is as follows: chop the media file into different bitrates or spatial resolutions encoded segments. The segments are provided on a Web server and can be downloaded through HTTP standard compliant GET requests where the HTTP Server serves different qualities, chopped into segments of equal length. Since the client knows its capabilities, received throughput and the context of the user best - the adaptation to the best bitrate or resolution is done on the client side for each segment. In previous semester, we managed to achieve MPEG-DASH live streaming (sub 2 second delay) in an un-stable environment by improving the client’s rate adaptation algorithm. The traffic instability was simulated by Netem tool. In this project we will use MiniNet-WiFi that will emulate WiFi mobile clients and will research the MPEG-DASH client rate adaption in various mobility models
Picture of MPEG-DASH Live Streaming in Unstable Environment Project
 
Project Title:

Intelligent End-To-End Traffic Congestion Trouble Shooting – using P4

Supervisors:
Alan Lo (Mellanox), Itzik Ashkenazi
Description:
Programming Protocol-independent Packet Processor (P4) is a high-level language that can be deployed in the future into Software Defined Networks (SDN) and can actually serve as an alternative to OpenFlow that is currently used – due to its flexibility and ability program the data plane and support emerging new protocols. Debugging End-To-End traffic problems and finding the root cause for packets congestion or un-expected high End-To-End latency plays a significant role in network management. Trouble shooting such issues in order to find the root cause, especially in a Data Center where traffic volume is massive, can be a difficult task. In this project we will use Mellanox SN3700 P4-capable Spectrum-2 based switches and implement an online intelligent method, based on postcard telemetry, that will debug end-to-end traffic congestion or high latency events and will pinpoint its root cause.
Picture of Intelligent End-To-End Traffic Congestion Trouble Shooting – using P4 Project
 
Project Title:

Smart Mobile LoRaWAN Gateway

Supervisors:
Aviel Glam (Rafael), Itzik Ashkenazi,
Description:
Low-power WAN (LPWAN) is a wireless wide area network specification that interconnects low-bandwidth, battery-powered sensors with low bit rates over long ranges. To meet the challenges of long range, low power consumption and secure data transmission, the sensors are based on LoRa Technology and on LoRaWAN media access control (MAC) layer protocol that manages communication between LPWAN sensors and the Gateway. Not in all circumstances its possible for an end node sensor to communicate with the outside world. This requires to use mobile gateway utilized on drone. The drone on its flight path can reach the remote location where the sensor device is running and collect its data. The challenge in this solution is to establish a communication link with every sensor node, by being at the correct location at the right sensor duty cycle time.
Picture of Smart Mobile LoRaWAN Gateway Project
 
Project Title:

MPEG-DASH Proxy Live Streaming in Unstable Environment

Supervisors:
Aviel Glam (Rafael), Itzik Ashkenazi,
Description:
MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) is a vendor independent, international standard ratified in 2012. One of the main benefits of MPEG-DASH is reduction of startup delays and buffering/stalls during the video and continued adaptation to the bandwidth situation of the client. Today, MPEG-DASH is gaining more and more deployments, accelerated by services such as Netflix or Google, which recently switched to this new standard. With these two major sources of internet traffic, 50% of total internet traffic is already MPEG-DASH. The basic idea of MPEG-DASH is as follows: chop the media file into different bitrates or spatial resolutions encoded segments. The segments are provided on a Web server and can be downloaded through HTTP standard compliant GET requests where the HTTP Server serves different qualities, chopped into segments of equal length. Since the client knows its capabilities, received throughput and the context of the user best - the adaptation to the best bitrate or resolution is done on the client side for each segment. In certain cases, there is a need for multiple clients to receive the same video stream. Since the client’s bandwidth and connection quality can vary, the challenge is to stream to each client the best possible quality, using MPEG-DASH Proxy, while maintaining live streaming. A greater challenge is to support in addition cases where the server’s network connection is not stable.
Picture of MPEG-DASH Proxy Live Streaming in Unstable Environment Project
 
Project Title:

Malware Detection in NFV Edge Computing - using Machine Learning

Supervisors:
Andrew Sergeev (Adva), Itzik Ashkenazi
Description:
Network Function Virtualization (NFV) is an emerging approach gaining popularity among network providers. NFV takes the physical networking devices commonly used today (switches, routers, load balancers, firewalls, antivirus, storage devices etc.) and visualizes them in the cloud. Edge computing provides these compute and storage resources with adequate networking connectivity close to the devices generating traffic. The benefit is the ability to provide new services with very low latency and avoid the data travel far in the network to reach the server in the cloud. The ADVA FSP-150 proVMe is a Multi-layer demarcation device that is equipped with a compute blade, based on x86 architecture CPUs, for NFV hosting. It is located in the cloud edge at the customer premise or at the cell site. However, along with its flexibility, this approach inherits the vulnerabilities of CPU architecture. It allows an attacker to obtain root privileges and to plant malware. Among such malware is crypto mining that is stealing CPU cycles from a legitimate NFV application. Such malware is hardly detectable either by malware scanner or by a firewall. In this project, we will use Machine Learning tools to investigate the applicability of side-channels Key Performance Indicators (KPIs) needed for malware detection.
Picture of Malware Detection in NFV Edge Computing - using Machine Learning Project
 
Projects:Current ProjectsspPast Projects