In this paper we present system-wide analysis and performance tuning of a SPECweb SSL server. We focus on the banking workload of SPECweb because this workload is 100% SSL-enabled and thus designed to evaluate the SSL web server performance and capacity. In order to gain insight into the unique characteristics of a SPECweb banking server, we conduct extensive experiments and present module and function-level anatomy data. Based on our results, we propose methods for tuning up the server performance, alleviating some of the bottlenecks. One of our main results is that the SPECweb banking workload may not always be a realistic environment for measuring the capacity of an SSL server, due to the fact that it is burdened with overheads which are not related to cryptography and can be removed.

End-to-end communication encryption is considered necessary for protecting the privacy of user data in the Internet. Only a small fraction of all Internet traffic, however, is protected today. The primary reason for this neglect is economic, mainly security protocol speed and cost. In this paper we argue that recent advances in the implementation of cryptographic algorithms can make general purpose processors capable of encrypting packets at line rates. This implies that the Internet can be gradually transformed to an information delivery infrastructure where all traffic is encrypted and authenticated. We justify our claim by presenting technologies that accelerate end-to-end encryption and authentication by a factor of 6 and a high performance TLS 1.2 protocol implementation that takes advantage of these innovations. Our implementation is available in the public domain for experimentation.

In this paper we measured and analyzed the workload on Yahoo! Video, the 2nd largest U.S. video sharing site, to understand its nature and the impact on online video data center design. We discovered interesting statistical properties on both static and temporal dimensions of the workload; they include file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we extended our understanding on the measurement data with a virtual data center design assuming the same workload as measured, which reveals results regarding the impact of workload arrival distribution, service level agreements (SLAs) and workload scheduling schemes on the design and operations of such large-scale video distribution systems.

In this paper we measured and analyzed the workload on Yahoo! Video, the 2nd largest U.S. video sharing site, to understand its nature and the impact on online video data center design. We discovered interesting statistical properties on both static and temporal dimensions of the workload including file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we further extended our understanding on the measurement data with a virtual design on the workload and capacity management components of a data center assuming the same workload as measured, which reveals key results regarding the impact of Service Level Agreements (SLAs) and workload scheduling schemes on the design and operations of such large-scale video distribution systems.

The Shortest Remaining Processing Time (SRPT) scheduling disciplineis optimal and its superior performance, compared with the policies that do not use the knowledge of job sizes, can be quantified using mean-value analysis as well as our new a symptotic distribution allimits for the relatively smaller heavy-tailed jobs. However, the main difficulty in implementing SRPT in large practical systems, e.g., Web servers, is that its complexity grows with the number of jobs in the queue. Hence, in order to lower the complexity, it is natural to approximate SRPT by grouping the arrivals into a fixed (small) number of classes containing jobs of approximately equal size and then serve the classes of smaller jobs with higher priorities. In this paper, we design a novel adaptive grouping mechanism based on relative size comparison of a newly arriving job to the preceding m arrivals. Specifically, if the newly arriving job is smallerthan k and larger than m-k of the previous m jobs, it isrouted into class k. The excellent performance of this mechanism,even for a small number of classes m+1, is demonstrated using both the asymptotic queueing analysis under heavy tails and extensive simulations. We also discuss refinements of the comparison grouping mechanism that improve the accuracy of job classification at the expense of a small additional complexity.