11,189 research outputs found
Non-Cooperative Scheduling of Multiple Bag-of-Task Applications
Multiple applications that execute concurrently on heterogeneous platforms
compete for CPU and network resources. In this paper we analyze the behavior of
non-cooperative schedulers using the optimal strategy that maximize their
efficiency while fairness is ensured at a system level ignoring applications
characteristics. We limit our study to simple single-level master-worker
platforms and to the case where each scheduler is in charge of a single
application consisting of a large number of independent tasks. The tasks of a
given application all have the same computation and communication requirements,
but these requirements can vary from one application to another. In this
context, we assume that each scheduler aims at maximizing its throughput. We
give closed-form formula of the equilibrium reached by such a system and study
its performance. We characterize the situations where this Nash equilibrium is
optimal (in the Pareto sense) and show that even though no catastrophic
situation (Braess-like paradox) can occur, such an equilibrium can be
arbitrarily bad for any classical performance measure
Power Allocation for Conventional and Buffer-Aided Link Adaptive Relaying Systems with Energy Harvesting Nodes
Energy harvesting (EH) nodes can play an important role in cooperative
communication systems which do not have a continuous power supply. In this
paper, we consider the optimization of conventional and buffer-aided link
adaptive EH relaying systems, where an EH source communicates with the
destination via an EH decode-and-forward relay. In conventional relaying,
source and relay transmit signals in consecutive time slots whereas in
buffer-aided link adaptive relaying, the state of the source-relay and
relay-destination channels determines whether the source or the relay is
selected for transmission. Our objective is to maximize the system throughput
over a finite number of transmission time slots for both relaying protocols. In
case of conventional relaying, we propose an offline and several online joint
source and relay transmit power allocation schemes. For offline power
allocation, we formulate an optimization problem which can be solved optimally.
For the online case, we propose a dynamic programming (DP) approach to compute
the optimal online transmit power. To alleviate the complexity inherent to DP,
we also propose several suboptimal online power allocation schemes. For
buffer-aided link adaptive relaying, we show that the joint offline
optimization of the source and relay transmit powers along with the link
selection results in a mixed integer non-linear program which we solve
optimally using the spatial branch-and-bound method. We also propose an
efficient online power allocation scheme and a naive online power allocation
scheme for buffer-aided link adaptive relaying. Our results show that link
adaptive relaying provides performance improvement over conventional relaying
at the expense of a higher computational complexity.Comment: Submitted to IEEE Transactions on Wireless Communication
Timely-Throughput Optimal Coded Computing over Cloud Networks
In modern distributed computing systems, unpredictable and unreliable
infrastructures result in high variability of computing resources. Meanwhile,
there is significantly increasing demand for timely and event-driven services
with deadline constraints. Motivated by measurements over Amazon EC2 clusters,
we consider a two-state Markov model for variability of computing speed in
cloud networks. In this model, each worker can be either in a good state or a
bad state in terms of the computation speed, and the transition between these
states is modeled as a Markov chain which is unknown to the scheduler. We then
consider a Coded Computing framework, in which the data is possibly encoded and
stored at the worker nodes in order to provide robustness against nodes that
may be in a bad state. With timely computation requests submitted to the system
with computation deadlines, our goal is to design the optimal computation-load
allocation scheme and the optimal data encoding scheme that maximize the timely
computation throughput (i.e, the average number of computation tasks that are
accomplished before their deadline). Our main result is the development of a
dynamic computation strategy called Lagrange Estimate-and Allocate (LEA)
strategy, which achieves the optimal timely computation throughput. It is shown
that compared to the static allocation strategy, LEA increases the timely
computation throughput by 1.4X - 17.5X in various scenarios via simulations and
by 1.27X - 6.5X in experiments over Amazon EC2 clustersComment: to appear in MobiHoc 201
Coded Adaptive Linear Precoded Discrete Multitone Over PLC Channel
Discrete multitone modulation (DMT) systems exploit the capabilities of
orthogonal subcarriers to cope efficiently with narrowband interference, high
frequency attenuations and multipath fadings with the help of simple
equalization filters. Adaptive linear precoded discrete multitone (LP-DMT)
system is based on classical DMT, combined with a linear precoding component.
In this paper, we investigate the bit and energy allocation algorithm of an
adaptive LP-DMT system taking into account the channel coding scheme. A coded
adaptive LPDMT system is presented in the power line communication (PLC)
context with a loading algorithm which accommodates the channel coding gains in
bit and energy calculations. The performance of a concatenated channel coding
scheme, consisting of an inner Wei's 4-dimensional 16-states trellis code and
an outer Reed-Solomon code, in combination with the proposed algorithm is
analyzed. Theoretical coding gains are derived and simulation results are
presented for a fixed target bit error rate in a multicarrier scenario under
power spectral density constraint. Using a multipath model of PLC channel, it
is shown that the proposed coded adaptive LP-DMT system performs better than
coded DMT and can achieve higher throughput for PLC applications
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