Today’s data centers offer tremendous aggregate bandwidth to clusters of tens of thousands of machines. However, because of limited port densities in even the highest-end switches, data center topologies typically consist of multi-rooted trees with many equal-cost paths between any given pair of hosts. Existing IP multipathing protocols usually rely on per-flow static hashing and can cause substantial bandwidth losses due to longterm collisions. In this paper, we present Hedera, a scalable, dynamic flow scheduling system that adaptively schedules a multi-stage switching fabric to efficiently utilize aggregate network resources. We describe our implementation using commodity switches and unmodified hosts, and show that for a simulated 8,192 host data center, Hedera delivers bisection bandwidth that is 96 % of optimal and up to 113 % better than static load-balancing methods. 1

Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment. The purpose of this paper is to review the area of simulation optimization. A critical review of the methods employed and applications developed in this relatively new area are presented and notable successes are highlighted. Simulation optimization software tools are discussed. The intended audience is simulation practitioners and theoreticians as well as beginners in the field of simulation.

We present a functional framework for automated Bayesian and robust mechanism design based on a two-stage game model of strategic interaction between the designer and the mechanism participants, and apply it to several classes of two-player infinite games of incomplete information. At the core of our framework is a black-box optimization algorithm which guides the selection process of candidate mechanisms. Our approach yields optimal or nearly optimal mechanisms in several application domains using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we show that ours is a promising approach to parametric design of indirect mechanisms. 1

We present a functional framework for automated Bayesian and robust mechanism design based on a two-stage game model of strategic interaction between the designer and the mechanism participants, and apply it to several classes of two-player infinite games of incomplete information. Our approach yields optimal or nearly optimal mechanisms in three application domains using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we show that ours is a promising approach to parametric design of indirect mechanisms. 1

In the automotive industry, a new body shop production-line needs to be set up for almost every new car model. Due to the relatively short product life cycles, the planning process of body shops can almost be regarded as continuous. A main problem is to find an efficient layout fulfilling the desired production rate which is characterized by small buffer sizes and optimized cycle times. Often, the optimization of a new body shop is carried out manually, possibly supported by a simulation model to analyze the impact of different cycle times and buffer sizes. In this paper, we present a mathematical formulation and an automated optimization approach for this planning problem. The optimization modules, which have a direct interface to the underlying simulation model, are based on metaheuristics, such as genetic algorithms and simulated annealing. Here, the main task lies in comparing the manual body-in-white configuration with metaheuristic-based optimization approaches. For an evaluation of the potentials of our approach, a case study was carried out in collaboration with a German car manufacturer.

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The main issues characterizing current video applications are their strong requirements for huge storage spaces and their need for timing synchronization. Video data storage is a critical research topic due to the so-called I/O bottleneck problem which affects the quality of service of video applications. This paper introduces a twolevel video data representation model in order to guide video data storage on a tertiary storage subsystem. A simulation model has been developed to evaluate different video placement strategies based on both Constructive and Iterative Improvement approaches. Experimentation has been carried out for the proposed placement approaches as well as for a typical random placement policy which serves as a comparison reference. Iterative Improvement placement has been proven to outperform the other considered video data placementapproaches, in both seek and service times.

1 Introduction and motivation FPGAs are circuits that can be programmed (and reprogrammed) in the field. Logic functions are typically implemented with look-up tables and flip-flops. The routing between the various blocks is also programmable with the ability to connect the output of virtually any logic block with any other. Through synthesis of a hardware-description language like Verilog or VHDL, user logic is mapped to these logic blocks. After this mapping, it is necessary to make decisions as to the physical location of these logic blocks and which routing resources should be dedicated to which nets. To perform this placement and routing in an optimal manner is a proven NP-complete problem [1]. There are several methods for providing solutions that are acceptable to the designer in a tractable amount of time. Some of these include simulated annealing (SA), forcedirected placement, min-cut placement, placement by numerical optimization, and evolutionbased placement[2]. In this paper, we are mostly concerned with SA, though evolutionary techniques will make a brief appearance.

XML data storage is a critical issue due to the so-called I/O bottleneck problem emerged in nowdays computer systems. This paper presents an object-based XML data representation model towards effective XML data placement The proposed representation of XML documents is analysed in a two-level scheme : the external level is based on the structure of a browsing graph whereas the internal level is supported by a tree-like structure. The main contribution of the paper is that it exploits the object data model in order to consider XML data dependencies, access frequencies and constraints. A simulation model has been developed in order to evaluate different XML data placement strategies and the impact of the proposed representation model in the overall storage process. XML data placement is applied on a tertiary storage subsystem by either constructive or iterative placementtechniques. Three popular policies : the Organ-pipe, the Camel and the Simulated Annealing algorithms, have been considered and experiments have been carried out on synthetic workloads of XML data sets. The need of applying an XML data storage policy is apparent as indicated by the resulted improvements in seek and service times. The Simulated Annealing approachhas been proven to outperform the other XML data placement strategies.

Multimedia data storage is a critical issue in large scale applications.