Anselme Ndikumana

Network-assisted congestion control for information centric networking

Internet has grown very rapidly in the last couple of decades and still growing because of the expansion and utilization of various services and applications. Consequently, demand of delay and throughput sensitive services, like audio/video is also increasing. Information Centric Networking (ICN) is proposed as an architecture for the future Internet to meet the modern users and application requirements. In ICN users send requests (Interest Packets) for the Data they need. Interest packet is assigned a lifetime, which greatly affects the Quality of Experience (QoE) because user needs to resend the Interest, when the lifetime expires. Interest lifetime may be expired because of congestion, or Interest lifetime is shorter than the network delay, etc. Waiting for the expiration of an Interest lifetime to resend it is merely appropriate for best effort traffic, rather than services which require high throughput and are delay sensitive. In this paper, we propose Network-Assisted Congestion Control mechanism in ICN, which detects the congestion before it happens, and provides notification to downstream node. On reception of the notification, downstream node continuously reduces the traffic rate. However, when the downstream node fails to adjust the sending rate, the same procedure continues, until the sending node reduces the traffic rate through adjusting its congestion window. We have intensively evaluated our proposal by comparing it with similar proposal using ndnSIM. The experimental results show that our proposal achieves up to 59 percent performance improvement over other proposal in the literature.

Scalable aggregation-based packet forwarding in Content Centric Networking

Content Centric Networking (CCN) is one of the Future Internet architectures that aims at improving content distribution and retrieval, where content is requested by name rather than IP address. Each content is partitioned into small units, namely chunks. In CCN, the consumer sends Interest packet in order to get a chunk of Data. Upon successful reception of the requested chunk, the consumer sends the next Interest packet. This policy of send Interest, wait for the Data to reach and generate next Interest makes the situation worst in case of large sized content. The result is uplink underutilization. To overcome the above highlighted issue, we propose Interest forwarding in CCN, which is based on packet aggregation through combining multiple chunk requests in one Interest packet, and content name aggregation. The Interest packet aggregation will reduce the number of Interest packets need to be sent in the network and downsize Pending Interest Table (PIT), while organizing content based on aggregated name reduces Content Store (CS) entries. The simulation results show that our proposal achieves high performance with throughput improvement, reduced number of Interest packets in network, PIT and CS entries over other existing proposal in the literature.

Collaborative cache allocation and computation offloading in mobile edge computing

Mobile data traffic is increasing astronomically. This enormous extent was not only caused by the increasing in the number of mobile devices, but also the growing number of applications running on clouds. Most of these mobile devices have limited resources for computing, they are relying on cloud computing, which is inadequate to realize millisecond-scale latency in the 5G network. To deal with this issue, Mobile Edge Computing (MEC) has been introduced to supplement cloud computing by pushing Computing, Caching, Communication, and Control (4C) to the edges. However, the proposed MEC server at each base station is not enough to deal with 4C, when it operates independently, and without any collaboration with other MEC servers. This results in increasing the delay and making backhaul to continue suffering from huge data exchange between end-users and remote clouds. To address this challenge, we propose collaborative cache allocation and computation offloading, where the MEC servers collaborate for executing computation tasks and data caching. We formulate an optimization problem that aims at maximizing the resource utilization. The simulation results show that our proposal is easy to be implemented in production network.