Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center

Cloud computing is growing fast and becoming more and more popular. The computing resources such as CPU and memory are becoming cheaper and the servers grow more and more powerful by the time. This enables clouds to host more virtual machines (VMs) than ever. As a result many modern data centers experience very high internal traffic inside the data centers due to the servers belonging to the same tenants communicating with each other. Since the modern VM deployment tools are not traffic-aware, the VMs with high mutual traffic often end up running far apart in the data center network and have to communicate over unnecessarily long distance. The resulting traffic bottlenecks negatively affect application performance and the network in whole and are posing important challenges for cloud and data center administrators. This thesis investigates how this problem can be resolved by consolidating VMs in clusters in different data center network architectures and deploy the produced clusters on the available server racks in a traffic-aware way. In order to achieve this the paper breaks the problem down in two parts. The VMs are consolidated with a VM clustering algorithm, successfully reducing the total cost of communication with 34 to 85\%, and the resulting clusters are assigned to the server racks with a cluster placement algorithm, which further reduces the total cost of communication with 89 to 99\%. The analysis shows that the optimization is done in a fast and computationally efficient way

Numerical Simulation of Partial Discharge Propagation in Cable Joints using the Finite Difference Time Domain Method

In this second of a series of three papers, the authors investigate partial discharge (PD) detection and propagation in cable joints. The complex nature of cable joints leads to errors when PD analysis is carried out using conventional equivalent circuits. The authors use the finite difference time domain method to determine the transient electromagnetic fields caused by simulated PD in model cable joints

Waveguide Port Approach in EM Simulation of Microwave Antennas

This chapter generalizes a recently proposed MoM-based approach to waveguide port excitation (WPE) problems on arbitrary conducting and composite geometries. This approach combines the canonical aperture coupling approach with the EFIE-PMCHWT formulation for composite structures. Each WPE problem in this approach is divided into equivalent sub-problems for internal and external regions, which are solved using the MoM. Internal WPE problems are solved using waveguide modal expansion in the port plane, while external problems are solved using the equivalence principle to reduce these problems to the systems of algebraic equations for unknown electric and magnetic currents. The developed approach is validated on radiation and coupling problems for coaxial ports by comparing simulated results with those obtained by other approaches and measurements. An excellent agreement between the simulated and measured results is demonstrated. Finally, this approach is applied to practical EMC problems for microwave antennas fed by coaxial ports

PACT-mediated pkr activation acts as a hyperosmotic stress intensity sensor weakening osmoadaptation and enhancing inflammation

The inability of cells to adapt to increased environmental tonicity can lead to inflammatory gene expression and pathogenesis. The Rel family of transcription factors TonEBP and NF-κB p65 play critical roles in the switch from osmoadaptive homeostasis to inflammation, respectively. Here we identified PACT-mediated PKR kinase activation as a marker of the termination of adaptation and initiation of inflammation in Mus musculus embryonic fibroblasts. We found that high stress-induced PACT-PKR activation inhibits the interaction between NF-κB c-Rel and TonEBP essential for the increased expression of TonEBP-dependent osmoprotective genes. This resulted in enhanced formation of TonEBP/NF-κB p65 complexes and enhanced proinflammatory gene expression. These data demonstrate a novel role of c-Rel in the adaptive response to hyperosmotic stress, which is inhibited via a PACT/PKR-dependent dimer redistribution of the Rel family transcription factors. Our results suggest that inhibiting PACT-PKR signaling may prove a novel target for alleviating stress-induced inflammatory diseases

Adaptive translational pausing is a hallmark of the cellular response to severe environmental stress.

Raw imaging and gel dat

A SPICE Model for IGBTs and Power MOSFETs Focusing on EMI/EMC in High-Voltage Systems

We describe two models of Power Transistors (IGBT, MOSFET); both were successfully used for the analysis of electromagnetic interference (EMI) and electromagnetic compatibility (EMC) while modeling high-voltage systems (PFC, DC/DC, inverter, etc.). The first semi-mathematical–behavioral insulated-gate bipolar transistor (IGBT) model introduces nonlinear negative feedback generated in the semiconductor’s p+ and n+ layers, which are located near the metal contact of the IGBT emitter, to better describe the dynamic characteristics of the transistor. A simplified model of the metal–oxide-semiconductor field-effect transistor (MOSFET) in the IGBT is used to simplify this IGBT model. The second simpler behavioral model could be used to model both IGBTs and MOSFETs. Model parameters are obtained from datasheets and then adjusted using results from a single measurement test. Modeling results are compared with measured turn-on and turn-off waveforms for different types of IGBTs. To check the validation of the models, a brushless DC electric motor test setup with an inverter was created. Despite the simplicity of the presented models, a comparison of model predictions with hardware measurements revealed that the model accurately forecasted switch transients and aided EMI–EMC investigations

Achieving Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center Using Learning Automata

Cloud Computing (CC) is becoming increasingly pertinent and popular. A natural consequence of this is that many modern-day data centers experience very high internal traffic within the data centers themselves. The VMs with high mutual traffic often end up being far apart in the data center network, forcing them to communicate over unnecessarily long distances. The consequent traffic bottlenecks negatively affect both the performance of the application and the network in its entirety, posing nontrivial challenges for the administrators of these cloudbased data centers. The problem can, quite naturally, be compartmentalized into two phases which follow each other. First of all, the VMs are consolidated with a VM clustering algorithm, and this is achieved by utilizing the toolbox involving Learning Automata (LA). By mapping the clustering problem onto the Graph Partitioning (GP) problem, our LAbased solution successfully reduces the total communication cost by amounts that range between 34% to 85%. Thereafter, the resulting clusters are assigned to the server racks using a cluster placement algorithm that involves a completely different intelligent strategy, i.e., one that invokes Simulated Annealing (SA). This phase further reduces the total cost of communication by amounts that range between 89% to 99%. The analysis and results for different models and topologies demonstrate that the optimization is done in a fast and computationally-efficient way. Indeed, as far as we know, this paper pioneers the application of LA in the traffic-aware consolidation of virtual machines in data centers, and also pioneers a strategy which serializes the tools in LA and SA to optimize CC

On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata

Unlike the computational mechanisms of the past many decades, that involved individual (extremely powerful) computers or clusters of machines, Cloud Computing (CC) is becoming increasingly pertinent and popular. Computing resources such as CPU and storage are becoming cheaper, and the servers themselves are becoming more powerful. This enables clouds to host more Virtual Machines (VMs). A natural consequence of this is that many modern-day data centers experience very high internal traffic within the data centers themselves. This is, of course, due to the occurrence of servers that belong to the same tenant, communicating between themselves. The problem is accentuated when the VM deployment tools are not traffic-aware. In such cases, the VMs with high mutual traffic often end up being far apart in the data center network, forcing them to communicate over unnecessarily long distances. The consequent traffic bottlenecks negatively affect both the performance of the application and the network in its entirety, posing non-trivial challenges for the administrators of these cloud-based data centers. The problem, and consequently the solution, can, quite naturally, be compartmentalized into two phases which follow each other. In the first, the task is to consolidate VMs into clusters, where those that commu nicate with each other fall into the same cluster. The second phase assigns these clusters onto the available server racks. Both of these phases must be executed in a traffic-aware manner. This paper provides efficient intelligent solutions for both these phases. First of all, the VMs are consolidated with a VM clustering algorithm, and this is achieved by utilizing the toolbox involving Learning Automata (LA). By mapping the clustering problem onto the Graph Partitioning (GP) problem, our LA-based solution successfully reduces the total communication cost by amounts that range between 34% to 85%. Thereafter, the resulting clusters are assigned to the server racks using a cluster placement algorithm that involves a completely different intelligent strategy, i.e., one that invokes Simulated Annealing (SA). This phase further reduces the total cost of communication by amounts that range between 89% to 99%. The analysis and results for different models and topologies demonstrate that the optimization is done in a fast and computationally-efficient way