1,014 research outputs found
Accelerated Backpressure Algorithm
We develop an Accelerated Back Pressure (ABP) algorithm using Accelerated
Dual Descent (ADD), a distributed approximate Newton-like algorithm that only
uses local information. Our construction is based on writing the backpressure
algorithm as the solution to a network feasibility problem solved via
stochastic dual subgradient descent. We apply stochastic ADD in place of the
stochastic gradient descent algorithm. We prove that the ABP algorithm
guarantees stable queues. Our numerical experiments demonstrate a significant
improvement in convergence rate, especially when the packet arrival statistics
vary over time.Comment: 9 pages, 4 figures. A version of this work with significantly
extended proofs is being submitted for journal publicatio
Fast, Distributed Optimization Strategies for Resource Allocation in Networks
Many challenges in network science and engineering today arise from systems composed of many individual agents interacting over a network. Such problems range from humans interacting with each other in social networks to computers processing and exchanging information over wired or wireless networks. In any application where information is spread out spatially, solutions must address information aggregation in addition to the decision process itself. Intelligently addressing the trade off between information aggregation and decision accuracy is fundamental to finding solutions quickly and accurately. Network optimization challenges such as these have generated a lot of interest in distributed optimization methods. The field of distributed optimization deals with iterative methods which perform calculations using locally available information. Early methods such as subgradient descent suffer very slow convergence rates because the underlying optimization method is a first order method. My work addresses problems in the area of network optimization and control with an emphasis on accelerating the rate of convergence by using a faster underlying optimization method. In the case of convex network flow optimization, the problem is transformed to the dual domain, moving the equality constraints which guarantee flow conservation into the objective. The Newton direction can be computed locally by using a consensus iteration to solve a Poisson equation, but this requires a lot of communication between neighboring nodes. Accelerated Dual Descent (ADD) is an approximate Newton method, which significantly reduces the communication requirement. Defining a stochastic version of the convex network flow problem with edge capacities yields a problem equivalent to the queue stability problem studied in the backpressure literature. Accelerated Backpressure (ABP) is developed to solve the queue stabilization problem. A queue reduction method is introduced by merging ideas from integral control and momentum based optimization
Electron and Ion Acceleration in Relativistic Shocks with Applications to GRB Afterglows
We have modeled the simultaneous first-order Fermi shock acceleration of
protons, electrons, and helium nuclei by relativistic shocks. By parameterizing
the particle diffusion, our steady-state Monte Carlo simulation allows us to
follow particles from particle injection at nonthermal thermal energies to
above PeV energies, including the nonlinear smoothing of the shock structure
due to cosmic-ray (CR) backpressure. We observe the mass-to-charge (A/Z)
enhancement effect believed to occur in efficient Fermi acceleration in
non-relativistic shocks and we parameterize the transfer of ion energy to
electrons seen in particle-in-cell (PIC) simulations. For a given set of
environmental and model parameters, the Monte Carlo simulation determines the
absolute normalization of the particle distributions and the resulting
synchrotron, inverse-Compton, and pion-decay emission in a largely
self-consistent manner. The simulation is flexible and can be readily used with
a wide range of parameters typical of gamma-ray burst (GRB) afterglows. We
describe some preliminary results for photon emission from shocks of different
Lorentz factors and outline how the Monte Carlo simulation can be generalized
and coupled to hydrodynamic simulations of GRB blast waves. We assume Bohm
diffusion for simplicity but emphasize that the nonlinear effects we describe
stem mainly from an extended shock precursor where higher energy particles
diffuse further upstream. Quantitative differences will occur with different
diffusion models, particularly for the maximum CR energy and photon emission,
but these nonlinear effects should be qualitatively similar as long as the
scattering mean free path is an increasing function of momentum.Comment: Accepted for publication in MNRA
Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization
Distributed network optimization has been studied for well over a decade.
However, we still do not have a good idea of how to design schemes that can
simultaneously provide good performance across the dimensions of utility
optimality, convergence speed, and delay. To address these challenges, in this
paper, we propose a new algorithmic framework with all these metrics
approaching optimality. The salient features of our new algorithm are
three-fold: (i) fast convergence: it converges with only
iterations that is the fastest speed among all the existing algorithms; (ii)
low delay: it guarantees optimal utility with finite queue length; (iii) simple
implementation: the control variables of this algorithm are based on virtual
queues that do not require maintaining per-flow information. The new technique
builds on a kind of inexact Uzawa method in the Alternating Directional Method
of Multiplier, and provides a new theoretical path to prove global and linear
convergence rate of such a method without requiring the full rank assumption of
the constraint matrix
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QoS-aware mechanisms for improving cost-efficiency of datacenters
Warehouse Scale Computers (WSCs) promise high cost-efficiency by amortizing power, cooling, and management overheads. WSCs today host a large variety of jobs with two broad performance requirements categories: latency-critical (LC) and best-effort (BE). Ideally, to fully utilize all hardware resources, WSC operators can simply fill all the nodes with computing jobs. Unfortunately, because colocated jobs contend for shared resources, systems with high loads often experience performance degradation, which negatively impacts the Quality of Service (QoS) for LC jobs. In fact, service providers usually over-provision resources to avoid any interference with LC jobs, leading to significant resource inefficiencies. In this dissertation, I explore opportunities across different system-abstraction layers to improve the cost-efficiency of dataceters by increasing resource utilization of WSCs with little or no impact on the performance of LC jobs. The dissertation has three main components. First, I explore opportunities to improve the throughput of multicore systems by reducing the performance variation of LC jobs. The main insight is that by reshaping the latency distribution curve, performance headroom of LC jobs can be effectively converted to improved BE throughput. I develop, implement, and evaluate a runtime system that achieves this goal with existing hardware. I leverage the cache partitioning, per-core frequency scaling, and thread masking of server processors. Evaluation results show the proposed solution enables 30% higher system throughput compared to solutions proposed in prior works while maintaining at least as good QoS for LC jobs. Second, I study resource contention in near-future heterogeneous memory architectures (HMA). This study is motivated by recent developments in non-volatile memory (NVM) technologies, which enable higher storage density at the cost of same performance. To understand the performance and QoS impact of HMAs, I design and implement a performance emulator in the Linux kernel that runs unmodified workloads with high accuracy, low overhead, and complete transparency. I further propose and evaluate multiple data and resource management QoS mechanisms, such as locality-aware page admission, occupancy management, and write buffer jailing. Third, I focus on accelerated machine learning (ML) systems. By profiling the performance of production workloads and accelerators, I show that accelerated ML tasks are highly sensitive to main memory interference due to fine-grained interaction between CPU and accelerator tasks. As a result, memory resource contention can significantly decreases the performance and efficiency gains of accelerators. I propose a runtime system that leverages existing hardware capabilities and show 17% higher system efficiency compared to previous approaches. This study further exposes opportunities for future processor architecturesElectrical and Computer Engineerin
Novel Redundant Sensor Fault Detection and Accommodation Algorithm for an Air-breathing Combustion System and its Real-time Implementation
Failure of sensors used to provide a feedback signal in control system can cause serious deterioration in performance of system, and even instability may be observed. Based on knowledge of aircraft engine systems, the main cause of fault in such air-breathing combustion systems (ACS) with no rotating parts is due to the pressure sensors. Fast online detection of faults before the error grows very large and accommodation is critical to the success of the mission. However, at the same time, it is necessary to avoid false alarms. Hence, early detection of small magnitude faults with acceptable reliability is very challenging, especially in the presence of sensor noise, unknown engine-to-engine variation and deterioration and modeling uncertainty. This paper discusses the novel fault detection and accommodation (FDA) algorithm based on analytical redundancy based technique for ACS.Defence Science Journal, 2010, 60(1), pp.61-75, DOI:http://dx.doi.org/10.14429/dsj.60.10
Electron and ion acceleration in relativistic shocks with applications to GRB afterglows
We have modelled the simultaneous first-order Fermi shock acceleration of protons, electrons, and helium nuclei by relativistic shocks. By parametrizing the particle diffusion, our steady-state Monte Carlo simulation allows us to follow particles from particle injection at non-relativistic thermal energies to above PeV energies, including the non-linear smoothing of the shock structure due to cosmic ray (CR) backpressure. We observe the mass-to-charge (A/Z) enhancement effect believed to occur in efficient Fermi acceleration in non-relativistic shocks and we parametrize the transfer of ion energy to electrons seen in particle-in-cell (PIC) simulations. For a given set of environmental and model parameters, the Monte Carlo simulation determines the absolute normalization of the particle distributions and the resulting synchrotron, inverse Compton, and pion-decay emission in a largely self-consistent manner. The simulation is flexible and can be readily used with a wide range of parameters typical of γ-ray burst (GRB) afterglows. We describe some preliminary results for photon emission from shocks of different Lorentz factors and outline how the Monte Carlo simulation can be generalized and coupled to hydrodynamic simulations of GRB blast waves. We assume Bohm diffusion for simplicity but emphasize that the non-linear effects we describe stem mainly from an extended shock precursor where higher energy particles diffuse further upstream. Quantitative differences will occur with different diffusion models, particularly for the maximum CR energy and photon emission, but these non-linear effects should be qualitatively similar as long as the scattering mean-free path is an increasing function of momentu
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