Saeed Ullah

Management of scalable video streaming in information centric networking

Ability of caching the contents is one of the most important feature of an Information Centric Networking (ICN) node. By managing the cache space intelligently we can improve network’s performance and increase users’ Quality of Experience (QoE). Moreover, scalable video streaming in ICN is envisioned to be very beneficial as well as a challenging issue. In this paper, we are proposing a mechanism for cache management and request forwarding policies for scalable video streaming in ICN. Our proposed cache decision policy ensures to cache the base layer of a scalable video near to the users, which is mandatory layer for decoding any SVC encoded video and is needed by all the users with any data-rate budget, and consequently cache the higher layers in the upper nodes in the CCN/ICN within a specific RTT range. Furthermore, our intelligent cache decision cover fairness by considering router’s cache capacity (inside the RTT range) and at the same time giving more priority to the nodes that are nearer to the users. A limited cooperative request forwarding mechanism, which is the part of our proposal, plays a role to improve users’ QoE by providing the popular requested contents quickly. We have intensively simulated our proposed cache management and request forwarding scheme. The simulation results show that our proposed solution outperforms the current cache management schemes and improve the cache utilization. Also our proposed scheme decrease the traffic flowing inside the network by eliminating the request flooding and providing the requested contents from the nearby location to the users. The proposed scheme provides video faster to the users, specially the mandatory base layer is provided very quickly to the users.

Efficient forwarding and popularity based caching for Content Centric Network

In Content Centric Network, caching and forwarding schemes can affect the performance of the whole network. The original caching and forwarding schemes are simple, but these schemes have some drawbacks. Firstly, the original caching scheme stores the same content on several neighboring routers along the request path. This redundant caching does not use the limited storage space available to the routers efficiently. Secondly, the original forwarding scheme in which the data requests are flooded to neighboring routers, also degrades the performance of the network. In this paper, we aim to solve the issues of using cache space of each router efficiently and forwarding the data requests effectively. In this proposal, we divided an Autonomous System (AS) in several groups of routers to cache the popular contents. Routers in a group cooperatively store the data and forward the Interest in order to increase the network performance. To improve the cache hit, the group of routers only store the popular contents and reduce the duplicate content without effecting the redundancy. We used Consistent Hashing to reduce overlapping contents and forward the request efficiently. The content popularity prediction algorithm assists the routers to store the popular contents that pass through them. Finally, we evaluated the performance of our proposed scheme by using a chunk level simulator.

Consistent Hashing Based Cooperative Caching and Forwarding in Content Centric Network

The original Content Centric Network (CCN) employs a simple caching scheme, Leave Copy Everywhere (LCE). However, this scheme is not efficient because cache redundancy reduces the storage capacity of the CCN network. In this paper, to resolve the issue of cache redundancy, we propose a cooperative caching decision and forwarding mechanism which is based on consistent hashing and virtual routers. We divide the Autonomous System (AS) into several groups of routers. The routers in the group cooperatively store the contents (Data) and also forward the requests (Interest) cooperatively in order to increase the caching performance of the CCN network. Finally, we evaluate our proposal by using a chunk-level simulator. The results show that the cache hit ratio of our proposed scheme is better than other proposed schemes.

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.

Delivering Scalable Video Streaming in ICN enabled Long Term Evolution networks

Information Centric Networking (ICN) is envisioned to be the future Internet architecture and mobile access network e.g., Long Term Evolution (LTE), and 5G will be the major access networks. In this paper, we present a cache management and cooperative request forwarding schemes for Scalable Video Streaming (SVS) in Information Centric Networking (ICN) enabled mobile access networks. H.264/SVC encoded video is consisted a mandatory baselayer and multiple optional enhancement layers. Baselayer, which is enough to decode the video, though with the lowest quality, is needed by every user who want to watch the video while enhancement layers are used to improve the video quality. Only a subset of users download enhancement layers of the video. Therefore, caching the baselayer nearer to the users will increase their Quality of Experience. Furthermore, we introduce cooperative request forwarding for the baselayer of video to take more benefits from cache of neighboring base stations. We have intensively simulated our proposed schemes by extending chunk level simulator ccnsim which is developed over Omnet++. Our experimental results show that, cache hit rate can be improved significantly by adopting our proposed caching and Interest forwarding schemes.

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.

Reinforcement Learning Based Data Self-Destruction Scheme for Secured Data Management

As technologies and services that leverage cloud computing have evolved, the number of businesses and individuals who use them are increasing rapidly. In the course of using cloud services, as users store and use data that include personal information, research on privacy protection models to protect sensitive information in the cloud environment is becoming more important. As a solution to this problem, a self-destructing scheme has been proposed that prevents the decryption of encrypted user data after a certain period of time using a Distributed Hash Table (DHT) network. However, the existing self-destructing scheme does not mention how to set the number of key shares and the threshold value considering the environment of the dynamic DHT network. This paper proposes a method to set the parameters to generate the key shares needed for the self-destructing scheme considering the availability and security of data. The proposed method defines state, action, and reward of the reinforcement learning model based on the similarity of the graph, and applies the self-destructing scheme process by updating the parameter based on the reinforcement learning model. Through the proposed technique, key sharing parameters can be set in consideration of data availability and security in dynamic DHT network environments.

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.

Passive packet loss detection in Wi-Fi networks and its effect on HTTP traffic characteristics

In this paper, we analyze the performance of web applications that run on top of TCP over the passively collected data from an operational network based on wireless infrastructure. In particular, we evaluate the impact of 1) TCP loss rates; 2) user interrupts; 3) connection volume and their durations; and 4) variations in TCP throughputs on web applications. Since TCP behavior is dependent on events like packet losses, timeouts, and connection terminations, which affect the behavior of TCP-based web applications. The phenomena is more prominent in wireless networks. Our collected real network traffic traces provides very interesting insight on the TCP behavior showing the effect of TCP throughput on packet losses and user interrupted connections, which in turn also increases unnecessary retransmissions. Our performance evaluation reveal that the longer duration and higher volumes of TCP connections is a function of higher interrupted connections and loss rates. By further studying the statistical properties of TCP connections shows that the loss rate increases with decrease in connection duration, and also leads to frequent termination of TCP connections.