Orchestrating model to improve utilization of IaaS environment for sustainable revenue

Badshah, Afzal; Ghani, Anwar; Siddiqui, Isma Farah; Daud, Ali; Zubair, Muhammad; Mehmood, Zafar

doi:10.1016/j.seta.2023.103228

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Uploading every vehicle's data to the cloud servers is necessary. However, every vehicle may generate one TB of data per day, which is impossible for the existing system to transport, process, and store [33]. As shown in Figure 4, the proposed system aims to process/store the VBD near the road infrastructure to minimize the delay and cost and laterally transfer this data to the cloud in off-peak hours.…”

Section: Proposed Modelmentioning

confidence: 99%

Network Resources Optimization through Regional Computing forVehicular Big Data

Khan,

Badshah,

Alharbi

et al. 2024

Preprint

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The transportation system is shifting toward an Intelligent Transportation System (ITS). Research shows that a singleAutonomous Vehicle (AV) can generate up to 1 Terabyte (TB) datadaily. Data processing occurs in real-time at the vehicle edge to facilitate navigation. However, some of this data is sent to the cloudfor real-time navigation support for other AVs. Even slight delayscan have significant consequences for these vehicles. All AV datais also necessary in the cloud for navigation and training purposes.Unfortunately, the massive volume of Vehicular Big Data (VBD)congests public networks, making real-time communication unfeasible. This study proposes Regional Computing (RC) as a solution,enabling real-time updates and navigation for AVs. Compared toedge servers, RC offers sufficient resources and considerably reduceddelays and costs compared to Cloud Computing (CC). RC substantially reduces communication delay, cost, and network congestion.

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Section: Proposed Modelmentioning

confidence: 99%

Network Resources Optimization through Regional Computing forVehicular Big Data

Khan,

Badshah,

Alharbi

et al. 2024

Preprint

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“…The IEEE 2302-2021 Standard for Intercloud Interoperability and Federation (SIIF) highlights the importance of establishing common mechanisms and APIs to enable resource sharing among different stakeholders, fostering the testing of complex technologies in a federated environment [2]. The need for more secured heterogeneous cloud services from federated clouds and the demand for high efficiency in performing tasks on multiple clouds are increasing nowadays, especially in cases that require high confidentiality and reliability [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Younis,

Jambi,

Eassa

et al. 2024

Systems

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Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.

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“…Key components of IaaS scheduling, including resource allocation, load balancing, scaling, and policy enforcement, will be explored. The discussion will also encompass the trade-offs and considerations that system administrators, cloud service providers, and application developers must address in their scheduling decisions [4]. Figure 1 defines the IaaS basic cloud system.…”

Section: Introductionmentioning

confidence: 99%