160,569 research outputs found
ANFIS Modeling of Dynamic Load Balancing in LTE
Modelling of ill-defined or unpredictable systems can be very challenging. Most models have relied on
conventional mathematical models which does not adequately track some of the multifaceted challenges
of such a system. Load balancing, which is a self-optimization operation of Self-Organizing Networks
(SON), aims at ensuring an equitable distribution of users in the network. This translates into better user
satisfaction and a more efficient use of network resources. Several methods for load balancing have been
proposed. While some of them have a very buoyant theoretical basis, they are not practical. Furthermore,
most of the techniques proposed the use of an iterative algorithm, which in itself is not computationally
efficient as it does not take the unpredictable fluctuation of network load into consideration. This chapter
proposes the use of soft computing, precisely Adaptive Neuro-Fuzzy Inference System (ANFIS) model,
for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of
neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost
effective and closer to human intuition. Three key load parameters (number of satisfied user in the net-
work, virtual load of the serving eNodeB, and the overall state of the target eNodeB) are used to adjust
the hysteresis value for load balancing
PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning
With the increase in the complexity of chip designs, VLSI physical design has
become a time-consuming task, which is an iterative design process. Power
planning is that part of the floorplanning in VLSI physical design where power
grid networks are designed in order to provide adequate power to all the
underlying functional blocks. Power planning also requires multiple iterative
steps to create the power grid network while satisfying the allowed worst-case
IR drop and Electromigration (EM) margin. For the first time, this paper
introduces Deep learning (DL)-based framework to approximately predict the
initial design of the power grid network, considering different reliability
constraints. The proposed framework reduces many iterative design steps and
speeds up the total design cycle. Neural Network-based multi-target regression
technique is used to create the DL model. Feature extraction is done, and the
training dataset is generated from the floorplans of some of the power grid
designs extracted from the IBM processor. The DL model is trained using the
generated dataset. The proposed DL-based framework is validated using a new set
of power grid specifications (obtained by perturbing the designs used in the
training phase). The results show that the predicted power grid design is
closer to the original design with minimal prediction error (~2%). The proposed
DL-based approach also improves the design cycle time with a speedup of ~6X for
standard power grid benchmarks.Comment: Published in proceedings of IEEE/ACM Design, Automation and Test in
Europe Conference (DATE) 2020, 6 page
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
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