Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale.

Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.

Renewable energy such as solar and wind generation will constitute an important part of the future grid. As the availability of renewable sources may not match the load, energy storage is essential for grid stability. In this paper we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid by also accounting for energy storage. To deal with the fluctuation in both the power supply and demand, we extend and apply stochastic network calculus to analyze the power supply reliability with various renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. In particular, we asses the power supply reliability in terms of the average Fraction of Time that energy is Not-Served (FTNS).

The popularity of delay-sensitive applications has been rapidly evolving in recent years. However, the current Internet is not well designed to support the delivery of such traffic. The SRD (Stochastic Rate-Delay) services proposed in this paper empower a user to choose either a higher transmission rate or a low queuing delay at a congested network link. While average low delay remains guaranteed, the delay constraint is allowed to violate occasionally. The implementation enforces the intended service differentiation via link scheduling and sizing of the buffer. We build a model using stochastic network calculus to analyze the SRD services proposal and shed light on its fundamental characteristics quantitatively.

Remote desktop access is commonly used to remotely access a host in enterprise networks; however it also brings in security problems in supervision and auditing. In this paper, a novel proxy-based security audit system is designed and implemented in order to ensure security supervising and auditing remote desktop access. Our system effectively monitors all the accessing sessions of RDP, VNC, and X-window, and provides replay function by recording all the graphics operations from users. Our performance test result shows that for most of small business, just one proxy server is enough to handle all the routine auditing workload of RDP sessions.

P2P-based video-on-demand (P2P-VoD) streaming service has recently emerged as a new paradigm of Internet application. Unlike streaming live content, P2P-VoD system require each user to contribute a small amount of storage since it has less synchrony in the users sharing video content. At the same time, new mechanisms for peer service scheduling are carefully designed. On briefly describing the architectural design based on the real system deployed by PPLive, we develop a stochastic model that seeks to expose the essential behavior of the PPLive P2P-VoD system. The performance evaluation followed shows that the system design of PPLive P2P-VoD system could guarantee good viewing quality, and sheds insight on the fundamental characteristics and limitations of the system quantitatively. Such theoretical analysis on P2P-VoD could help to improve the viewing quality and make such system more robust and scalable.