11,057 research outputs found
A Framework for QoS-aware Execution of Workflows over the Cloud
The Cloud Computing paradigm is providing system architects with a new
powerful tool for building scalable applications. Clouds allow allocation of
resources on a "pay-as-you-go" model, so that additional resources can be
requested during peak loads and released after that. However, this flexibility
asks for appropriate dynamic reconfiguration strategies. In this paper we
describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for
executing workflows involving Web Services hosted in a Cloud environment. SAVER
allows execution of arbitrary workflows subject to response time constraints.
SAVER uses a passive monitor to identify workload fluctuations based on the
observed system response time. The information collected by the monitor is used
by a planner component to identify the minimum number of instances of each Web
Service which should be allocated in order to satisfy the response time
constraint. SAVER uses a simple Queueing Network (QN) model to identify the
optimal resource allocation. Specifically, the QN model is used to identify
bottlenecks, and predict the system performance as Cloud resources are
allocated or released. The parameters used to evaluate the model are those
collected by the monitor, which means that SAVER does not require any
particular knowledge of the Web Services and workflows being executed. Our
approach has been validated through numerical simulations, whose results are
reported in this paper
A Stochastic Resource-Sharing Network for Electric Vehicle Charging
We consider a distribution grid used to charge electric vehicles such that
voltage drops stay bounded. We model this as a class of resource-sharing
networks, known as bandwidth-sharing networks in the communication network
literature. We focus on resource-sharing networks that are driven by a class of
greedy control rules that can be implemented in a decentralized fashion. For a
large number of such control rules, we can characterize the performance of the
system by a fluid approximation. This leads to a set of dynamic equations that
take into account the stochastic behavior of EVs. We show that the invariant
point of these equations is unique and can be computed by solving a specific
ACOPF problem, which admits an exact convex relaxation. We illustrate our
findings with a case study using the SCE 47-bus network and several special
cases that allow for explicit computations.Comment: 13 pages, 8 figure
Traffic Driven Resource Allocation in Heterogenous Wireless Networks
Most work on wireless network resource allocation use physical layer
performance such as sum rate and outage probability as the figure of merit.
These metrics may not reflect the true user QoS in future heterogenous networks
(HetNets) with many small cells, due to large traffic variations in overlapping
cells with complicated interference conditions. This paper studies the spectrum
allocation problem in HetNets using the average packet sojourn time as the
performance metric. To be specific, in a HetNet with base terminal stations
(BTS's), we determine the optimal partition of the spectrum into possible
spectrum sharing combinations. We use an interactive queueing model to
characterize the flow level performance, where the service rates are decided by
the spectrum partition. The spectrum allocation problem is formulated using a
conservative approximation, which makes the optimization problem convex. We
prove that in the optimal solution the spectrum is divided into at most
pieces. A numerical algorithm is provided to solve the spectrum allocation
problem on a slow timescale with aggregate traffic and service information.
Simulation results show that the proposed solution achieves significant gains
compared to both orthogonal and full spectrum reuse allocations with moderate
to heavy traffic.Comment: 6 pages, 5 figures IEEE GLOBECOM 2014 (accepted for publication
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Closed queueing networks under congestion: non-bottleneck independence and bottleneck convergence
We analyze the behavior of closed product-form queueing networks when the
number of customers grows to infinity and remains proportionate on each route
(or class). First, we focus on the stationary behavior and prove the conjecture
that the stationary distribution at non-bottleneck queues converges weakly to
the stationary distribution of an ergodic, open product-form queueing network.
This open network is obtained by replacing bottleneck queues with per-route
Poissonian sources whose rates are determined by the solution of a strictly
concave optimization problem. Then, we focus on the transient behavior of the
network and use fluid limits to prove that the amount of fluid, or customers,
on each route eventually concentrates on the bottleneck queues only, and that
the long-term proportions of fluid in each route and in each queue solve the
dual of the concave optimization problem that determines the throughputs of the
previous open network.Comment: 22 page
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