Monitoring and Proactive Management of QoS Levels in Pervasive
Applications
- URL: http://arxiv.org/abs/2206.05478v1
- Date: Sat, 11 Jun 2022 09:27:47 GMT
- Title: Monitoring and Proactive Management of QoS Levels in Pervasive
Applications
- Authors: Georgios Boulougaris, Kostas Kolomvatsos
- Abstract summary: Edge Computing (EC) provides multiple computation and analytics capabilities close to data sources.
The expectation of ensuring high levels of execution imposes strict requirements for innovative management approaches.
We elaborate a distributed and intelligent decision-making approach for tasks scheduling.
We propose that nodes continuously monitor levels and systematically evaluate the probability of violating them to proactively decide some tasks to be offloaded to peer nodes or Cloud.
- Score: 9.289846887298852
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The advent of Edge Computing (EC) as a promising paradigm that provides
multiple computation and analytics capabilities close to data sources opens new
pathways for novel applications. Nonetheless, the limited computational
capabilities of EC nodes and the expectation of ensuring high levels of QoS
during tasks execution impose strict requirements for innovative management
approaches. Motivated by the need of maintaining a minimum level of QoS during
EC nodes functioning, we elaborate a distributed and intelligent
decision-making approach for tasks scheduling. Our aim is to enhance the
behavior of EC nodes making them capable of securing high QoS levels. We
propose that nodes continuously monitor QoS levels and systematically evaluate
the probability of violating them to proactively decide some tasks to be
offloaded to peer nodes or Cloud. We present, describe and evaluate the
proposed scheme through multiple experimental scenarios revealing its
performance and the benefits of the envisioned monitoring mechanism when
serving processing requests in very dynamic environments like the EC.
Related papers
- Adaptive User-Centric Entanglement Routing in Quantum Data Networks [5.421492821020181]
Distributed quantum computing (DQC) holds immense promise in harnessing the potential of quantum computing by interconnecting multiple small quantum computers (QCs) through a quantum data network (QDN)
establishing long-distance quantum entanglement between two QCs for quantum teleportation within the QDN is a critical aspect, and it involves entanglement routing.
Existing approaches have mainly focused on optimizing entanglement performance for current entanglement connection (EC) requests.
We present a novel user-centric entanglement routing problem that spans an extended period to maximize entanglement success rate while adhering to the user's budget constraint.
arXiv Detail & Related papers (2024-04-13T17:20:00Z) - Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets)
Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
arXiv Detail & Related papers (2023-11-05T12:03:58Z) - Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud
Computing [73.7522199491117]
Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources.
The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning.
We propose a resource allocation model to provision quantum computing and networking resources.
arXiv Detail & Related papers (2023-07-25T00:38:46Z) - Scaling Limits of Quantum Repeater Networks [62.75241407271626]
Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing.
Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability.
In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
arXiv Detail & Related papers (2023-05-15T14:57:01Z) - Differentially Private Deep Q-Learning for Pattern Privacy Preservation
in MEC Offloading [76.0572817182483]
attackers may eavesdrop on the offloading decisions to infer the edge server's (ES's) queue information and users' usage patterns.
We propose an offloading strategy which jointly minimizes the latency, ES's energy consumption, and task dropping rate, while preserving pattern privacy (PP)
We develop a Differential Privacy Deep Q-learning based Offloading (DP-DQO) algorithm to solve this problem while addressing the PP issue by injecting noise into the generated offloading decisions.
arXiv Detail & Related papers (2023-02-09T12:50:18Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - A Reinforcement Learning Framework for PQoS in a Teleoperated Driving
Scenario [18.54699818319184]
We propose the design of a new entity, implemented at the RAN-level, that implements PQoS functionalities.
Specifically, we focus on the design of the reward function of the learning agent, able to convert estimates into appropriate countermeasures if requirements are not satisfied.
We demonstrate via ns-3 simulations that our approach achieves the best trade-off in terms of Quality of Experience (QoE) performance of end users in a teledriving-like scenario.
arXiv Detail & Related papers (2022-02-04T02:59:16Z) - QoS-Aware Power Minimization of Distributed Many-Core Servers using
Transfer Q-Learning [8.123268089072523]
This paper presents a runtime-aware controller using horizontal scaling (node allocation) and vertical scaling (resource allocation within nodes)
A horizontal scaling determines the number of active nodes based on workload demands and the required scalable according to a set of rules.
Then, it is coupled with vertical scaling using transfer Q-learning, which tunes power/performance based on workload profile using dynamic voltage/frequency scaling (DVFS)
When combined, these methods allow to reduce the exploration time and violations when compared to model-free Q-learning.
arXiv Detail & Related papers (2021-02-02T06:47:58Z) - Proactive Tasks Management based on a Deep Learning Model [9.289846887298852]
We propose an intelligent, proactive tasks management model based on the demand.
We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network.
We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.
arXiv Detail & Related papers (2020-07-25T05:28:14Z) - RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment
and Passive Beamforming Design [116.88396201197533]
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS)
The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency.
A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset.
A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
arXiv Detail & Related papers (2020-01-28T14:37:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.