47,937 research outputs found
Distributed data mining in grid computing environments
The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper
Hybrid static/dynamic scheduling for already optimized dense matrix factorization
We present the use of a hybrid static/dynamic scheduling strategy of the task
dependency graph for direct methods used in dense numerical linear algebra.
This strategy provides a balance of data locality, load balance, and low
dequeue overhead. We show that the usage of this scheduling in communication
avoiding dense factorization leads to significant performance gains. On a 48
core AMD Opteron NUMA machine, our experiments show that we can achieve up to
64% improvement over a version of CALU that uses fully dynamic scheduling, and
up to 30% improvement over the version of CALU that uses fully static
scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic
scheduling approach is up to 8% faster than the version of CALU that uses a
fully static scheduling or fully dynamic scheduling. Our algorithm leads to
speedups over the corresponding routines for computing LU factorization in well
known libraries. On the 48 core AMD NUMA machine, our best implementation is up
to 110% faster than MKL, while on the 16 core Intel Xeon machine, it is up to
82% faster than MKL. Our approach also shows significant speedups compared with
PLASMA on both of these systems
A Multi-objective Perspective for Operator Scheduling using Fine-grained DVS Architecture
The stringent power budget of fine grained power managed digital integrated
circuits have driven chip designers to optimize power at the cost of area and
delay, which were the traditional cost criteria for circuit optimization. The
emerging scenario motivates us to revisit the classical operator scheduling
problem under the availability of DVFS enabled functional units that can
trade-off cycles with power. We study the design space defined due to this
trade-off and present a branch-and-bound(B/B) algorithm to explore this state
space and report the pareto-optimal front with respect to area and power. The
scheduling also aims at maximum resource sharing and is able to attain
sufficient area and power gains for complex benchmarks when timing constraints
are relaxed by sufficient amount. Experimental results show that the algorithm
that operates without any user constraint(area/power) is able to solve the
problem for most available benchmarks, and the use of power budget or area
budget constraints leads to significant performance gain.Comment: 18 pages, 6 figures, International journal of VLSI design &
Communication Systems (VLSICS
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
Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources
The growing deployment of sensors as part of Internet of Things (IoT) is
generating thousands of event streams. Complex Event Processing (CEP) queries
offer a useful paradigm for rapid decision-making over such data sources. While
often centralized in the Cloud, the deployment of capable edge devices on the
field motivates the need for cooperative event analytics that span Edge and
Cloud computing. Here, we identify a novel problem of query placement on edge
and Cloud resources for dynamically arriving and departing analytic dataflows.
We define this as an optimization problem to minimize the total makespan for
all event analytics, while meeting energy and compute constraints of the
resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for
such dynamic dataflows, and validate them using detailed simulations for 100 -
1000 edge devices and VMs. The results show that our heuristics offer
O(seconds) planning time, give a valid and high quality solution in all cases,
and reduce the number of query migrations. Furthermore, rebalance strategies
when applied in these heuristics have significantly reduced the makespan by
around 20 - 25%.Comment: 11 pages, 7 figure
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