Dilip Kumar Krishnappa

Low-power sub-threshold design of secure physical unclonable functions

The unique and unpredictable nature of silicon enables the use of physical unclonable functions (PUFs) for chip identification and authentication. Since the function of PUFs depends on minute uncontrollable process variations, a low supply voltage can benefit PUFs by providing high sensitivity to variations and low power consumption as well. Motivated by this, we explore the feasibility of sub-threshold arbiter PUFs in 45nm CMOS technology. By modeling process variations and interconnect imbalance effects at the post-layout design level, we optimize the PUF supply voltage for the minimum power-delay product and investigate the trade-offs on PUF uniqueness and reliability. Moreover, we demonstrate that such a design optimization does not compromise the security of PUFs regarding modeling attacks and side-channel analysis attacks. Our final 64-stage sub-threshold PUF design only needs 418 gates and consumes 0.047pJ energy per cycle, which is very promising for low-power wireless sensing and security applications.

Design and Validation of Arbiter-Based PUFs for Sub-45-nm Low-Power Security Applications

Harnessing unique physical properties of integrated circuits to enhance hardware security and IP protection has been extensively explored in recent years. Physical unclonable functions (PUFs) can sense inherent manufacturing variations as chip identifications. To enable the integration of PUFs into low-power and security applications, we study the impacts of process technology and supply voltage scaling on arbiter-based PUF circuit design. A Monte Carlo-based statistical analysis has demonstrated that advanced technologies and reduced supply voltage can improve the PUF uniqueness due to increased delay sensitivity. A linear regression approach has been leveraged to generate PUF delay profile by factoring in device, supply voltage and temperature variations. An accurate SVM-based software modeling analysis is used to verify the PUF additive delay behavior. Finally, postsilicon validation on arbiter-based PUF test chips in 45 nm SOICMOS technology has been correlated to simulation results and the inconsistency has been discussed. The test chips can resist the basic support vector machine attack due to the dynamic circuit effects and the limitation of our delay model.

DASHing YouTube: An analysis of using DASH in YouTube video service

Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard which adaptively streams video based on the link bandwidth between server and client. DASH encoded videos are chunked in small segments and each segment can have different representations. Switching between these representations enables adaptive streaming, which has the potential to reduce bandwidth consumption in cases where a video is not completely watched. In this paper, we present an analysis on the advantages and disadvantages of using DASH as YouTubes video streaming format. To perform this analysis, we make use of a YouTube video trace and analyze the potential reduction in bandwidth consumption by employing DASH in YouTube, based on user watching patterns. Results from our analysis show that by employing DASH with a segment interval of 2 seconds, we can obtain 95% reduction in bandwidth for low quality videos and up to 83% reduction for HD videos in cases where users do not watch videos completely, which is the case for ~ 42% of all video requests in our trace. Considering all videos requested in the trace the overall bandwidth reduction is 40% for low quality videos and 35% for HD videos.

Cache-Centric Video Recommendation: An Approach to Improve the Efficiency of YouTube Caches

In this article, we take advantage of the user behavior of requesting videos from the top of the related list provided by YouTube to improve the performance of YouTube caches. We recommend that local caches reorder the related lists associated with YouTube videos, presenting the cached content above noncached content. We argue that the likelihood that viewers select content from the top of the related list is higher than selection from the bottom, and pushing contents already in the cache to the top of the related list would increase the likelihood of choosing cached content. To verify that the position on the list really is the selection criterion more dominant than the content itself, we conduct a user study with 40 YouTube-using volunteers who were presented with random related lists in their everyday YouTube use. After confirming our assumption, we analyze the benefits of our approach by an investigation that is based on two traces collected from a university campus. Our analysis shows that the proposed reordering approach for related lists would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. This increase in hit rate would lead to reduction in server load and backend bandwidth usage, which in turn reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTubes content distribution system. An analysis of YouTubes recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.

CloudCast: Cloud computing for short-term mobile weather forecasts

Since todays weather forecasts only cover large regions every few hours, their use in severe weather is limited. In this paper, we present CloudCast, an application that provides short-term weather forecasts depending on users current location. Since severe weather is rare, CloudCast leverages pay-as-you-go cloud platforms to eliminate dedicated computing infrastructure. CloudCast has two components: 1) an architecture linking weather radars to cloud resources, and 2) a Nowcasting algorithm for generating accurate short-term weather forecasts. We study CloudCasts design space, which requires significant data staging to the cloud. Our results indicate that serial transfers achieve tolerable throughput, while parallel transfers represent a bottleneck for real-time mobile Nowcasting. We also analyze forecast accuracy and show high accuracy for ten minutes in the future. Finally, we execute CloudCast live using an on-campus radar, and show that it delivers a 15-minute Nowcast to a mobile client in less than 2 minutes after data sampling started.

CloudCast: Cloud Computing for Short-Term Weather Forecasts

CloudCast provides clients with personalized short-term weather forecasts based on their current location using cloud services, generating accurate forecasts tens of minutes in the future for small areas. Results show that it takes less than 2 minutes from the start of data sampling to deliver a 15-minute forecast to a client.

Future internets escape the simulator

Future Internet testbeds permit experiments not possible in todays public Net or commercial cloud services.

What should you cache?: a global analysis on YouTube related video caching

Following advice from the YouTube recommendation system is one of the ways users browse through the videos offered by YouTube. The system presents related videos based on several factors depending on the current video requested. This related videos list can be used by caching infrastructure to reduce network bandwidth consumption. In this paper, we analyze the differences between user-specific recommendation lists. We perform this analysis on 100s of user nodes from all around the world divided into 4 geographical regions using PlanetLab. Based on our analysis, we find that the related videos differ less in the top half (1-10) of the related video list offered by YouTube compared to the bottom half (11-20). Based on our analysis, we suggest that, caching or prefetching of the Top 10 of the related videos is advantageous over a period of time than caching the whole list offered by YouTube.

Network capabilities of cloud services for a real time scientific application

Dedicating high-end servers for executing scientific applications that run intermittently, such as severe weather detection or generalized weather forecasting, wastes resources. While the Infrastructure-as-a-Service (IaaS) model used by todays cloud platforms is well-suited for the bursty computational demands of these applications, it is unclear if the network capabilities of todays cloud platforms are sufficient. In this paper, we analyze the networking capabilities of multiple commercial (Amazons EC2 and Rackspace) and research (GENICloud and ExoGENI cloud) platforms in the context of a Nowcasting application, a forecasting algorithm for highly accurate, near-term, e.g., 5-20 minutes, weather predictions. The application has both computational and network requirements. While it executes rarely, whenever severe weather approaches, it benefits from an IaaS model; However, since its results are time-critical, enough bandwidth must be available to transmit radar data to cloud platforms before it becomes stale. We conduct network capacity measurements between radar sites and cloud platforms throughout the country. Our results indicate that ExoGENI cloud performs the best for both serial and parallel data transfer with an average throughput of 110.22 Mbps and 17.2 Mbps, respectively. We also found that the cloud services perform better in the distributed data transfer case, where a subset of nodes transmit data in parallel to a cloud instance. Ultimately, we conclude that commercial and research clouds are capable of providing sufficient bandwidth for our real-time Nowcasting application.

Planet YouTube: Global, measurement-based performance analysis of viewer;'s experience watching user generated videos

User experience is a very important aspect of user-generated video streaming service such as YouTube. In this paper, we perform a global study of user experience for YouTube videos using PlanetLab nodes from all over the world. We analyze the number of pauses, accumulative pause time, rate at which videos were downloaded to clients, and how the YouTube infrastructure impact the viewers experience. The data for this analysis was generated by an automated tool from the traces captured during the PlanetLab-based measurement. Results from this analysis show that on average there are about 2.5 pauses per video. Interestingly, we found that on average 25% of the videos with pauses have an accumulative pause time greater than 15 seconds. This shows that not only the number of pauses but also the total length of pauses has to be analyzed to investigate the user experience of watching videos offered by YouTube. In addition, our results show that there is an inverse relationship between the download rates of the videos and the number of pauses encountered.