Foehn warming distributions in nonlinear and linear flow regimes: a focus on the Antarctic Peninsula

The structure of lee-side warming during foehn events is investigated as a function of cross-barrier flow regime linearity. Two contrasting cases of westerly flow over the Antarctic Peninsula (AP) are considered – one highly nonlinear, the other relatively linear. Westerly flow impinging on the AP provides one of the best natural laboratories in the world for the study of foehn, owing to its maritime setting and the Larsen C Ice Shelf (LCIS) providing an expansive, homogeneous and smooth surface on its east side. Numerical simulations with the Met Office Unified Model (at 1.5 km grid size) and aircraft observations are utilized. In case A, relatively weak southwesterly cross-Peninsula flow and an elevated upwind inversion dictate a highly nonlinear foehn event, with mountain wave breaking observed. The consequent strongly accelerated downslope flow leads to high-amplitude warming and ice-shelf melt in the immediate lee of the AP. However this foehn warming diminishes rapidly downwind due to upward ascent of the foehn flow via a hydraulic jump. In case C, strong northwesterly winds dictate a relatively linear flow regime. There is no hydraulic jump and strong foehn winds are able to flow at low levels across the entire ice shelf, mechanically mixing the near-surface flow, preventing the development of a strong surface inversion and delivering large fluxes of sensible heat to the ice shelf. Consequently, in case C ice-melt rates are considerably greater over the LCIS as a whole than in case A. Our results imply that although nonlinear foehn events cause intense warming in the immediate lee of mountains, linear foehn events will commonly cause more extensive lee-side warming and, over an ice surface, higher melt rates. This has major implications for the AP, where recent east-coast warming has led to the collapse of two ice shelves immediately north of the LCIS

Stochastic Analysis of Power-Aware Scheduling

Energy consumption in a computer system can be reduced by dynamic speed scaling, which adapts the processing speed to the current load. This paper studies the optimal way to adjust speed to balance mean response time and mean energy consumption, when jobs arrive as a Poisson process and processor sharing scheduling is used. Both bounds and asymptotics for the optimal speeds are provided. Interestingly, a simple scheme that halts when the system is idle and uses a static rate while the system is busy provides nearly the same performance as the optimal dynamic speed scaling. However, dynamic speed scaling which allocates a higher speed when more jobs are present significantly improves robustness to bursty traffic and mis-estimation of workload parameters

Power-Aware Speed Scaling in Processor Sharing Systems

Energy use of computer communication systems has quickly become a vital design consideration. One effective method for reducing energy consumption is dynamic speed scaling, which adapts the processing speed to the current load. This paper studies how to optimally scale speed to balance mean response time and mean energy consumption under processor sharing scheduling. Both bounds and asymptotics for the optimal speed scaling scheme are provided. These results show that a simple scheme that halts when the system is idle and uses a static rate while the system is busy provides nearly the same performance as the optimal dynamic speed scaling. However, the results also highlight that dynamic speed scaling provides at least one key benefit - significantly improved robustness to bursty traffic and mis-estimation of workload parameters

Improving the fairness of FAST TCP to new flows

It has been observed that FAST TCP, and the related protocol TCP Vegas, suffer unfairness when many flows arrive at a single bottleneck link, without intervening departures. We show that the effect is even more marked if a new flow arrives when existing flows share bandwidth fairly, and propose a simple method to ameliorate this effect

Active Queue Management for Fair Resource Allocation in Wireless Networks

This paper investigates the interaction between end-to-end flow control and MAC-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading, and a scheduler allocates the channel based on channel quality,but subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport layer flow control of TCP New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users

Online Algorithms for Geographical Load Balancing

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective