2,069 research outputs found
Machine Learning Centered Energy Optimization In Cloud Computing: A Review
The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Cloud computing is a reliable solution to provide distributed computation
power. However, real-time response is still challenging regarding the enormous
amount of data generated by the IoT devices in 5G and 6G networks. Thus,
multi-access edge computing (MEC), which consists of distributing the edge
servers in the proximity of end-users to have low latency besides the higher
processing power, is increasingly becoming a vital factor for the success of
modern applications. This paper addresses the problem of minimizing both, the
network delay, which is the main objective of MEC, and the number of edge
servers to provide a MEC design with minimum cost. This MEC design consists of
edge servers placement and base stations allocation, which makes it a joint
combinatorial optimization problem (COP). Recently, reinforcement learning (RL)
has shown promising results for COPs. However, modeling real-world problems
using RL when the state and action spaces are large still needs investigation.
We propose a novel RL framework with an efficient representation and modeling
of the state space, action space and the penalty function in the design of the
underlying Markov Decision Process (MDP) for solving our problem
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital
healthcare services to clients through remote sensing, continuous monitoring,
and data analytics. Smart eHealth applications sense input data from multiple
modalities, transmit the data to edge and/or cloud nodes, and process the data
with compute intensive machine learning (ML) algorithms. Run-time variations
with continuous stream of noisy input data, unreliable network connection,
computational requirements of ML algorithms, and choice of compute placement
among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth
applications. In this chapter, we present edge-centric techniques for optimized
compute placement, exploration of accuracy-performance trade-offs, and
cross-layered sense-compute co-optimization for ML-driven eHealth applications.
We demonstrate the practical use cases of smart eHealth applications in
everyday settings, through a sensor-edge-cloud framework for an objective pain
assessment case study
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