343 research outputs found
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
We formulate computation offloading as a decentralized decision-making
problem with autonomous agents. We design an interaction mechanism that
incentivizes agents to align private and system goals by balancing between
competition and cooperation. The mechanism provably has Nash equilibria with
optimal resource allocation in the static case. For a dynamic environment, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, and a reward signal that reduces
information need to a great extent. Empirical results confirm that through
learning, agents significantly improve both system and individual performance,
e.g., 40% offloading failure rate reduction, 32% communication overhead
reduction, up to 38% computation resource savings in low contention, 18%
utilization increase with reduced load variation in high contention, and
improvement in fairness. Results also confirm the algorithm's good convergence
and generalization property in significantly different environments
Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems
The ever-increasing growth in the number of connected smart devices and
various Internet of Things (IoT) verticals is leading to a crucial challenge of
handling massive amount of raw data generated from distributed IoT systems and
providing real-time feedback to the end-users. Although existing
cloud-computing paradigm has an enormous amount of virtual computing power and
storage capacity, it is not suitable for latency-sensitive applications and
distributed systems due to the involved latency and its centralized mode of
operation. To this end, edge/fog computing has recently emerged as the next
generation of computing systems for extending cloud-computing functions to the
edges of the network. Despite several benefits of edge computing such as
geo-distribution, mobility support and location awareness, various
communication and computing related challenges need to be addressed in
realizing edge computing technologies for future IoT systems. In this regard,
this paper provides a holistic view on the current issues and effective
solutions by classifying the emerging technologies in regard to the joint
coordination of radio and computing resources, system optimization and
intelligent resource management. Furthermore, an optimization framework for
edge-IoT systems is proposed to enhance various performance metrics such as
throughput, delay, resource utilization and energy consumption. Finally, a
Machine Learning (ML) based case study is presented along with some numerical
results to illustrate the significance of edge computing.Comment: 16 pages, 4 figures, 2 tables, submitted to IEEE Wireless
Communications Magazin
Towards More Efficient 5G Networks via Dynamic Traffic Scheduling
Department of Electrical EngineeringThe 5G communications adopt various advanced technologies such as mobile edge computing and unlicensed band operations, to meet the goal of 5G services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). Specifically, by placing the cloud resources at the edge of the radio access network, so-called mobile edge cloud, mobile devices can be served with lower latency compared to traditional remote-cloud based services. In addition, by utilizing unlicensed spectrum, 5G can mitigate the scarce spectrum resources problem thus leading to realize higher throughput services.
To enhance user-experienced service quality, however, aforementioned approaches should be more fine-tuned by considering various network performance metrics altogether. For instance, the mechanisms for mobile edge computing, e.g., computation offloading to the edge cloud, should not be optimized in a specific metric's perspective like latency, since actual user satisfaction comes from multi-domain factors including latency, throughput, monetary cost, etc. Moreover, blindly combining unlicensed spectrum resources with licensed ones does not always guarantee the performance enhancement, since it is crucial for unlicensed band operations to achieve peaceful but efficient coexistence with other competing technologies (e.g., Wi-Fi).
This dissertation proposes a focused resource management framework for more efficient 5G network operations as follows. First, Quality-of-Experience is adopted to quantify user satisfaction in mobile edge computing, and the optimal transmission scheduling algorithm is derived to maximize user QoE in computation offloading scenarios. Next, regarding unlicensed band operations, two efficient mechanisms are introduced to improve the coexistence performance between LTE-LAA and Wi-Fi networks. In particular, we develop a dynamic energy-detection thresholding algorithm for LTE-LAA so that LTE-LAA devices can detect Wi-Fi frames in a lightweight way. In addition, we propose AI-based network configuration for an LTE-LAA network with which an LTE-LAA operator can fine-tune its coexistence parameters (e.g., CAA threshold) to better protect coexisting Wi-Fi while achieving enhanced performance than the legacy LTE-LAA in the standards. Via extensive evaluations using computer simulations and a USRP-based testbed, we have verified that the proposed framework can enhance the efficiency of 5G.clos
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