Modern web services rely on a network of distributed cache servers to efficiently deliver content to users. Load imbalance among cache servers can substantially degrade content delivery performance. Due to the skewed and dynamic nature of real-world workloads, cache servers that serve viral content experience higher load as compared to other cache servers. We propose a novel distributed load balancing protocol called Meezan to address the load imbalance among cache servers. Meezan replicates popular objects to mitigate skewness and adjusts hash space boundaries in response to load dynamics in a novel way. Our theoretical analysis shows that Meezan achieves near perfect load balancing for a wide range of operating parameters. Our trace driven simulations shows that Meezan reduces load imbalance by up to 52% as compared to prior solutions.

The economic aspects of peering and transit interconnections between ISPs have been extensively studied in prior literature. Prior research primarily focuses on the economic issues associated with establishing peering and transit connectivity among ISPs to model interconnection strategies. Performance analysis, on the other hand, while understood intuitively, has not been empirically quantified and incorporated in such models. To fill this gap, we conduct a large scale measurement based performance comparison of peering and transit interconnection strategies. We use JavaScript to conduct application layer latency measurements between 510K clients in 900 access ISPs and multi-homed CDN servers located at 33 IXPs around the world. Overall, we find that peering paths outperformed transit paths for 91% Autonomous Systems (ASes) in our data. Peering paths have smaller propagation delays as compared to transit paths for more than 95% ASes. Peering paths outperform transit paths in terms of propagation delay due to shorter path lengths. Peering paths also have smaller queueing delays as compared to transit paths for more than 50% ASes.

Fueled by increasing network bandwidth and decreasing costs, the popularity of over-the-top large-scale live video streaming has dramatically increased over the last few years. In this paper, we present a measurement study of adaptive bitrate video streaming for a large-scale live event. Using server-side logs from a commercial content delivery network, we study live video delivery for the annual Academy Awards event that was streamed by hundreds of thousands of viewers in the United States. We analyze the relationship between Quality-of-Experience (QoE) and user engagement. We first study the impact of buffering, average bitrate, and bitrate fluctuations on user engagement. To account for interdependencies among QoE metrics and other confounding factors, we use quasi-experiments to quantify the causal impact of different QoE metrics on user engagement. We further design and implement a Principal Component Analysis (PCA) based technique to detect live video QoE impairments in real-time. We then use Hampel filters to detect QoE impairments and report 92% accuracy with 20% improvement in true positive rate as compared to baselines. Our approach allows content providers to detect and mitigate QoE impairments on the fly instead of relying on post-hoc analysis.

Large-scale cloud networks are constantly driven by the need for improved performance in communication between datacenters. Indeed, such back-office communication makes up a large fraction of traffic in many cloud environments. This communication often occurs frequently, carrying control messages, coordination and load balancing information, and customer data. However, ensuring such inter-datacenter traffic is delivered efficiently requires optimizing connections over large physical distances, which is non-trivial. Worse still, many large cloud networks are subject to complex configuration and administrative restrictions, limiting the types of solutions that can be implemented. In this paper, we propose improving the efficiency of datacenter to datacenter communication by learning the congestion level of links in between. We then use this knowledge to inform new connections made between the relevant datacenters, allowing us to eliminate the overhead associated with traditional slow-start processes in new connections. We further present Riptide, a tool which implements this approach. We present the design and implementation details of Riptide, showing that it can be easily executed on modern Linux servers deployed in the real world. We further demonstrate that it successfully reduces total transfer times in a production global-scale content delivery network (CDN), providing up to a 30% decrease in tail latency. We further show that Riptide is simple to deploy and easy to maintain within a complex existing network.

Fair exchange between two parties can be defined as an instance of exchange such that either both parties obtain what they expected or neither one does. Protocols that facilitate such transactions are known as “fair exchange protocols”. We analyze one such protocol by Micali that demonstrates fair contract signing, where two parties exchange their commitments over an already negotiated contract. In this journal we show that Micali’s protocol is not completely fair and demonstrate the possibilities for one party cheating by obtaining the other party’s commitment and not offer theirs. A revised version of this protocol by Bao provides superior fairness by handling the above mentioned weakness but fails to handle the possibility of a replay attack. Our proposed protocol improves upon Bao’s protocol by addressing the weakness that leads to a replay attack. We also demonstrate a software implementation of our system which provides fair contract signing along with properties like user authentication achieved through the use of a fingerprint based authentication system and features like confidentiality, data-integrity and non-repudiation through implementation of hybrid cryptography and digital signatures algorithms based on Elliptic Curve Cryptography.