9,169 research outputs found
Dynamic algorithms for multicast with intra-session network coding
The problem of multiple multicast sessions with
intra-session network coding in time-varying networks is considered.
The network-layer capacity region of input rates that can be
stably supported is established. Dynamic algorithms for multicast
routing, network coding, power allocation, session scheduling, and
rate allocation across correlated sources, which achieve stability
for rates within the capacity region, are presented. This work
builds on the back-pressure approach introduced by Tassiulas
et al., extending it to network coding and correlated sources. In
the proposed algorithms, decisions on routing, network coding,
and scheduling between different sessions at a node are made
locally at each node based on virtual queues for different sinks.
For correlated sources, the sinks locally determine and control
transmission rates across the sources. The proposed approach
yields a completely distributed algorithm for wired networks.
In the wireless case, power control among different transmitters
is centralized while routing, network coding, and scheduling
between different sessions at a given node are distributed
Lattice QCD Thermodynamics on the Grid
We describe how we have used simultaneously nodes of the
EGEE Grid, accumulating ca. 300 CPU-years in 2-3 months, to determine an
important property of Quantum Chromodynamics. We explain how Grid resources
were exploited efficiently and with ease, using user-level overlay based on
Ganga and DIANE tools above standard Grid software stack. Application-specific
scheduling and resource selection based on simple but powerful heuristics
allowed to improve efficiency of the processing to obtain desired scientific
results by a specified deadline. This is also a demonstration of combined use
of supercomputers, to calculate the initial state of the QCD system, and Grids,
to perform the subsequent massively distributed simulations. The QCD simulation
was performed on a lattice. Keeping the strange quark mass at
its physical value, we reduced the masses of the up and down quarks until,
under an increase of temperature, the system underwent a second-order phase
transition to a quark-gluon plasma. Then we measured the response of this
system to an increase in the quark density. We find that the transition is
smoothened rather than sharpened. If confirmed on a finer lattice, this finding
makes it unlikely for ongoing experimental searches to find a QCD critical
point at small chemical potential
On-line scheduling with delivery time on a single batch machine
AbstractWe consider a single batch machine on-line scheduling problem with jobs arriving over time. A batch processing machine can handle up to B jobs simultaneously as a batch, and the processing time for a batch is equal to the longest processing time among the jobs in it. Each job becomes available at its arrival time, which is not known in advance, and its characteristics, such as processing time and delivery time, become known at its arrival. Once the processing of a job is completed we deliver it to the destination. The objective is to minimize the time by which all jobs have been delivered. In this paper, we deal with two variants: the unbound model where B is sufficiently large and the bounded model where B is finite. We provide on-line algorithms with competitive ratio 2 for the unbounded model and with competitive ratio 3 for the bounded model. For when each job has the same processing time, we provide on-line algorithms with competitive ratios (5+1)/2, and these results are the best possible
The Integration of Process Planning and Shop Floor Scheduling in Small Batch Part Manufacturing
In this paper we explore possibilities to cut manufacturing leadtimes and to improve delivery performance in a small batch part manufacturing shop by integrating process planning and shop floor scheduling. Using a set of initial process plans (one for each order in the shop), we exploit a resource decomposition procedure to determine schedules to determine schedules which minimize the maximum lateness, given these process plans. If the resulting schedule is still unsatisfactory, a critical path analysis is performed to select jobs as candidates for alternative process plans. In this way, an excellent due date performance can be achieved, with a minimum of process planning and scheduling effort
Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing
Time-of-Use (TOU) electricity pricing provides an opportunity for industrial
users to cut electricity costs. Although many methods for Economic Load
Dispatch (ELD) under TOU pricing in continuous industrial processing have been
proposed, there are still difficulties in batch-type processing since power
load units are not directly adjustable and nonlinearly depend on production
planning and scheduling. In this paper, for hot rolling, a typical batch-type
and energy intensive process in steel industry, a production scheduling
optimization model for ELD is proposed under TOU pricing, in which the
objective is to minimize electricity costs while considering penalties caused
by jumps between adjacent slabs. A NSGA-II based multi-objective production
scheduling algorithm is developed to obtain Pareto-optimal solutions, and then
TOPSIS based multi-criteria decision-making is performed to recommend an
optimal solution to facilitate filed operation. Experimental results and
analyses show that the proposed method cuts electricity costs in production,
especially in case of allowance for penalty score increase in a certain range.
Further analyses show that the proposed method has effect on peak load
regulation of power grid.Comment: 13 pages, 6 figures, 4 table
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
- …