A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

Ugwuanyi, Emeka E.; Iqbal, Muddesar; Dagiuklas, Tasos

doi:10.1109/access.2021.3058769

Cited by 7 publications

(2 citation statements)

References 34 publications

(49 reference statements)

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“…This algorithm is capable of selecting a victim in constant time. Furthermore, in order to adapt effectively to the constantly evolving patterns of user requests, a proactive predictive caching algorithm has been proposed [6]. This algorithm aims to learn cache associations and proactively prefetch cache objects in anticipation of user requests.…”

Section: Related Workmentioning

confidence: 99%

An in-Depth Review on Cache Replacement Strategy in Bigdata Analytics

2023

IRJMETS

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Achieving high system execution performance is challenging due to the variety of application features, the erratic nature of memory resources, and the erratic use of the cache application programming interface (API). Ineffective cache replacement procedures can lead to a variety of performance issues, including lengthy application execution times, low memory use, frequent replacements, and even program execution failures owing to memory outages. Spark is presently using the least recently used (LRU) cache refilling technique. Despite being a popular classical strategy, LRU ignores environmental conditions and workloads. It cannot therefore function well in a variety of situations. The model in this paper introduces a new cache replacement technique called least partition weight (LPW). LPW considers several parameters, such as the partition size, computational cost, and reference count, in evaluating the performance of a system. Once the LPW algorithm was integrated into Spark, it was evaluated against the LRU and other cutting-edge methods. The cache replacement model's functionality is substantially improved by the recommended model with the application of auto data division and weighted tree technique. Therefore, the focus of this review paper is on examining previous research to comprehend the cache replacement process and present an improved version of the catch replacement using LPW in conjunction with parallel computation, which will be described in the paper's next edition.

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Section: Related Workmentioning

confidence: 99%

An in-Depth Review on Cache Replacement Strategy in Bigdata Analytics

2023

IRJMETS

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“…It has been proved in our prior research endeavours [13][10] [14]. Additionally, it has been proved that the MEC platform is prone to a deadlock due to limited resource constraints.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Deadlock Avoidance and Prevention Algorithms for Resource Provisioning in Intelligent Autonomous Transport Systems Over 6G Infrastructure

Ugwuanyi

Iqbal

Dagiuklas

2023

IEEE Trans. Intell. Transport. Syst.

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6G is the future of intelligent connectivity artefacts with Artificial Intelligence (AI) at its backbone. The Multi-Access Edge Computing (MEC) based 6G enabled infrastructure helps in achieving the required zero latency for autonomous Intelligent Transport Systems (ITS) with features of low power consumption, lower end-to-end latency, minimal processing/transmission overheads, higher throughput and reliability. MEC are prone to a deadlock due to the limited amount of available computational resources, resulting in fatal delays in Vehicle-to-Vehicle (V2V), Vehicles to Road Side Unit (RSU) and RSU to ITS communication. The unresolved deadlock may entail higher energy consumption that can adversely affect the Quality of Service (QoS) in terms of safety and reliability with potential threats of causing fatal accidents. Therefore, it is almost imperative to resolve the deadlocks from MEC to comply with the QoS parameters of MEC based autonomous vehicles. The asserted goals can be achieved by employing an intelligent and adaptive deadlock resolution strategy. In this paper, a deadlock-aware, and collaborative edge decision algorithm has been proposed for facilitating the seamless communication of autonomous vehicles over MEC. Additionally, deadlock avoidance and prevention schemes have been evaluated using Bankers resource request avoidance algorithm, wound wait algorithm and wait-die algorithms for resource provisioning in collaborative MEC. Furthermore, the effectiveness of deadlock avoidance and prevention algorithms in real-time scenarios has been analyzed in MEC systems. The metrics used for a comparative analysis in this research include Round-trip time, Queue wait-time and CPU utilization. The proposed algorithm shows promising results when compared with prevalent techniques.

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Collaborative Cache Algorithm of Mobile Edge Computing

Peng

2024

Lecture Notes in Electrical Engineering

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A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

Cited by 7 publications

References 34 publications

An in-Depth Review on Cache Replacement Strategy in Bigdata Analytics

An in-Depth Review on Cache Replacement Strategy in Bigdata Analytics

A Comparative Analysis of Deadlock Avoidance and Prevention Algorithms for Resource Provisioning in Intelligent Autonomous Transport Systems Over 6G Infrastructure

Collaborative Cache Algorithm of Mobile Edge Computing

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