Logarithmic growth dynamics in software networks

In a recent paper, Krapivsky and Redner (Phys. Rev. E, 71 (2005) 036118) proposed a new growing network model with new nodes being attached to a randomly selected node, as well to all ancestors of the target node. The model leads to a sparse graph with an average degree growing logarithmically with the system size. Here we present compeling evidence for software networks being the result of a similar class of growing dynamics. The predicted pattern of network growth, as well as the stationary in- and out-degree distributions are consistent with the model. Our results confirm the view of large-scale software topology being generated through duplication-rewiring mechanisms. Implications of these findings are outlined.Comment: 7 pages, 3 figures, published in Europhysics Letters (2005

Proceedings of IPACK'03 The Pacific Rim/ASME International Electronic Packaging Technical Conference and Exhibition

ABSTRACT Today's data centers are designed for handling heat densities of 1000W/m 2 at the room level. Trends indicate that these heat densities will exceed 3000W/m 2 in the near future. As a result, cooling of data centers has emerged as an area of increasing importance in electronics thermal management. With these high heat loads, data center layout and design cannot rely on intuitive design of air distribution and requires analytical tools to provide the necessary insight to the problem. These tools can also be used to optimize the layout of the room to improve energy efficiency in the data center. In this paper, first an under floor analysis is done to find an optimized layout based on flow distribution through perforated tiles, then a complete Computational Fluid Dynamics (CFD) model of the data center facility is done to check for desired cooling and air flow distribution throughout the room. A robust methodology is proposed which helps for fast, easy, efficient modeling and analysis of data center design. Results are displayed to provide some guidance on the layout and design of data center. The resulting design approach is very simple and well suited for the energy efficient design of complex data centers and server farms

Optimal Eviction Policies for Stochastic Address Traces

The eviction problem for memory hierarchies is studied for the Hidden Markov Reference Model (HMRM) of the memory trace, showing how miss minimization can be naturally formulated in the optimal control setting. In addition to the traditional version assuming a buffer of fixed capacity, a relaxed version is also considered, in which buffer occupancy can vary and its average is constrained. Resorting to multiobjective optimization, viewing occupancy as a cost rather than as a constraint, the optimal eviction policy is obtained by composing solutions for the individual addressable items. This approach is then specialized to the Least Recently Used Stack Model (LRUSM), a type of HMRM often considered for traces, which includes V-1 parameters, where V is the size of the virtual space. A gain optimal policy for any target average occupancy is obtained which (i) is computable in time O(V) from the model parameters, (ii) is optimal also for the fixed capacity case, and (iii) is characterized in terms of priorities, with the name of Least Profit Rate (LPR) policy. An O(log C) upper bound (being C the buffer capacity) is derived for the ratio between the expected miss rate of LPR and that of OPT, the optimal off-line policy; the upper bound is tightened to O(1), under reasonable constraints on the LRUSM parameters. Using the stack-distance framework, an algorithm is developed to compute the number of misses incurred by LPR on a given input trace, simultaneously for all buffer capacities, in time O(log V) per access. Finally, some results are provided for miss minimization over a finite horizon and over an infinite horizon under bias optimality, a criterion more stringent than gain optimality.Comment: 37 pages, 3 figure

RED-BL: Evaluating dynamic workload relocation for data center networks

In this paper, we present RED-BL (Relocate Energy Demand to Better Locations), a framework to minimize the electricity cost for operating data center networks over consecutive intervals of fixed duration. Within each interval, RED-BL provides a mapping of workload to a set of geographically distributed data centers. To this end, RED-BL uses the geographical and temporal variations in electricity prices as exhibited by electrical energy markets. In addition, we incorporate the transition costs associated with a change in workload mapping from one interval to the next, over a planning window comprising multiple such intervals. This results in a sequence of workload mappings that is optimal over the entire planning window, even though the workload mapping in a given interval may not be locally optimal. Our evaluation of RED-BL uses electricity prices from the US markets and workload traces from live Internet applications with millions of users. We find that RED-BL can reduce the electric bill by as much as 45% compared to the case when the workload is uniformly distributed. When compared to existing workload relocation solutions, for a wide range of data center deployment sizes, RED-BL achieves electricity cost savings that are 8.28% higher, on average. This seemingly modest reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window. © 2014 Elsevier B.V. All rights reserved