Athula Balachandran

Analyzing the potential benefits of CDN augmentation strategies for internet video workloads

Video viewership over the Internet is rising rapidly, and market predictions suggest that video will comprise over 90\% of Internet traffic in the next few years. At the same time, there have been signs that the Content Delivery Network (CDN) infrastructure is being stressed by ever-increasing amounts of video traffic. To meet these growing demands, the CDN infrastructure must be designed, provisioned and managed appropriately. Federated telco-CDNs and hybrid P2P-CDNs are two content delivery infrastructure designs that have gained significant industry attention recently. We observed several user access patterns that have important implications to these two designs in our unique dataset consisting of 30 million video sessions spanning around two months of video viewership from two large Internet video providers. These include partial interest in content, regional interests, temporal shift in peak load and patterns in evolution of interest. We analyze the impact of our findings on these two designs by performing a large scale measurement study. Surprisingly, we find significant amount of synchronous viewing behavior for Video On Demand (VOD) content, which makes hybrid P2P-CDN approach feasible for VOD and suggest new strategies for CDNs to reduce their infrastructure costs. We also find that federation can significantly reduce telco-CDN provisioning costs by as much as 95%.

CARE: content aware redundancy elimination for challenged networks

This paper presents the design of a novel architecture called CARE (Content-Aware Redundancy Elimination) that enables maximizing the informational value that challenged networks offer their users. We focus on emerging applications for situational awareness in disaster affected regions. Motivated by advances in computer vision algorithms, we propose to incorporate image similarity detection algorithms in the forwarding path of these networks. The purpose is to handle the large generation of redundant content. We outline the many issues involved in such a vision. With a Delay-Tolerant Network (DTN) setup, our simulations demonstrate that CARE can substantially boost the number of unique messages that escape the disaster zone, and it can also deliver them faster. These benefits are achieved despite the energy overhead needed by the similarity detectors.

Memory-efficient groupby-aggregate using compressed buffer trees

The rapid growth of fast analytics systems, that require data processing in memory, makes memory capacity an increasingly-precious resource. This paper introduces a new compressed data structure called a Compressed Buffer Tree (CBT). Using a combination of techniques including buffering, compression, and serialization, CBTs improve the memory efficiency and performance of the GroupBy-Aggregate abstraction that forms the basis of not only batch-processing models like MapReduce, but recent fast analytics systems too. For streaming workloads, aggregation using the CBT uses 21--42% less memory than using Google SparseHash with up to 16% better throughput. The CBT is also compared to batch-mode aggregators in MapReduce runtimes such as Phoenix++ and Metis and consumes 4x and 5x less memory with 1.5--2x and 3--4x more performance respectively.

Online reconfiguration of channel assignment in Multi-Channel Multi-Radio wireless mesh networks

Efficient utilization of Multi Channel-Multi Radio (MC-MR) wireless mesh networks (WMNs) can be achieved only by intelligent channel assignment (CA) and Link Scheduling (LS). Due to the dynamic nature of traffic demand in WMNs, the CA has to be reconfigured whenever traffic demand changes, in order to achieve maximum throughput in the network. The reconfiguration of CA requires channel switching at radios which leads to disruption of ongoing traffic in the network. So, we have to consider this traffic disruption overhead while reconfiguring the network for traffic adaptation. The existing CA algorithms for MC-MR WMNs in the literature do not consider the reconfiguration overhead caused by the channel switching. In this paper, we propose a novel reconfiguration model that considers both network throughput and reconfiguration overhead to quantitatively evaluate a reconfiguration algorithm. Based on the reconfiguration model, we formulate the problem of reconfiguration of CA as a Mixed Integer Linear Program (MILP). We also propose an on-line heuristic algorithm for CA called Demand based State Aware channel Reconfiguration Algorithm (DeSARA) that finds the CA for the current traffic demand by considering the existing CA of the network to minimize the reconfiguration overhead. Through extensive simulations, we show the importance of considering the overhead in reconfiguration of CA, by comparing the performance of DeSARA with a static CA and a fully dynamic CA that does not consider the reconfiguration overhead. We also study the performance of the proposed algorithm with real network traces collected in a campus network to show its practicality.

Demand Based State Aware Channel Reconfiguration Algorithm for Multi-Channel Multi-Radio Wireless Mesh Networks

Efficient utilization of Multi Channel - Multi Radio (MC-MR) Wireless Mesh Networks (WMNs) can be achieved only by intelligent Channel Assignment (CA) and Link Scheduling (LS). Due to the dynamic nature of traffic demand in WMNs, the CA has to be reconfigured whenever traffic demand changes, in order to achieve maximum throughput in the network. The reconfiguration of CA requires channel switching which leads to disruption of ongoing traffic in the network. The existing CA algorithms for MC-MR WMNs in the literature do not consider the channel reconfiguration overhead that occurs due to this channel switching. In this paper, we propose a novel reconfiguration framework that considers both network throughput and reconfiguration overhead to quantitatively evaluate a reconfiguration algorithm. Based on the reconfiguration framework, we propose an online heuristic algorithm for CA called Demand based State Aware channel Reconfiguration Algorithm (DeSARA) that finds the CA for the current traffic demand by considering the existing CA of the network to minimize the reconfiguration overhead. We show through simulations that DeSARA outperforms both static CA and fully dynamic CA in terms of total achieved throughput.

Understanding internet video viewing behavior in the wild

Over the past few years video viewership over the Internet has risen dramatically and market predictions suggest that video will account for more than 50% of the traffic over the Internet in the next few years. Unfortunately, there has been signs that the Content Delivery Network (CDN) infrastructure is being stressed with the increasing video viewership load. Our goal in this paper is to provide a first step towards a principled understanding of how the content delivery infrastructure must be designed and provisioned to handle the increasing workload by analyzing video viewing behaviors and patterns in the wild. We analyze various viewing behaviors using a dataset consisting of over 30 million video sessions spanning two months of viewership from two large Internet video providers. In these preliminary results, we observe viewing patterns that have significant impact on the design of the video delivery infrastructure.

Enhancing video accessibility and availability using information-bound references

Users are often frustrated when they cannot view video links shared via blogs, social networks, and shared bookmark sites on their devices or suffer performance and usability problems when doing so. While other versions of the same content better suited to their device and network constraints may be available on other third-party hosting sites, these remain unusable because users cannot efficiently discover these and verify that these variants match the content publishers original intent. Our vision is to enable consumers to leverage verifiable alternatives from different hosting sites that are best suited to their constraints to deliver a high quality of experience and enable content publishers to reach a wide audience with diverse operating conditions with minimal upfront costs. To this end, we make a case for information-bound references or IBRs that bind references to video content to the underlying information that a publisher wants to convey, decoupled from details such as protocols, hosts, file names, or the underlying bits. This paper addresses key challenges in the design and implementation of IBR generation and resolution mechanisms, and presents an evaluation of the benefits IBRs offer.