ReMot Reputation and Resource-Based Model to Estimate the Reliability of the Host Machines in Volunteer Cloud Environment

Alsenani, Yousef; Crosby, Garth V.; Velasco, Tomas; Alahmadi, Abdulrahman

doi:10.1109/ficloud.2018.00017

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…In computing, trust remains one of the main issues in centralized and distributed systems such as cloud, edge [9], [12], [13], volunteer computing [14], [15], and federated learning [16], [17]. This section review number of trust research in volunteer computing and other emerging paradigms, such as volunteer edge computing, edge computing, and federated learning.…”

Section: Related Workmentioning

confidence: 99%

Trust-Aware Scheduling for Edge Computing With Task Dependencies and Unreliable Servers

Alsenani,

Alnori

2023

IEEE Access

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Volunteer Edge Computing (VEC) is a promising solution for addressing the challenge of high round-trip latency in traditional cloud computing systems. Leveraging distributed computing resources reduces latency and improves performance. However, resource management in VEC is challenging, first, due to the uncertain behavior of volunteers, which frequently go offline unexpectedly, and second, since sequences of tasks can be executed on different volunteers, which requires transmitting data from one to another volunteer, which can lead to processing interruptions and network overhead. To address these challenges, we propose a trustaware scheduling procedure that consists of two stages. First, we train a regression model based on lagged data suitable to accurately predict volunteer availability. Second, we assign tasks to volunteers using a metric based on the predicted availability from the first stage. The metric assesses the likelihood that a candidate volunteer can successfully complete a task and the likelihood that nearby nodes are available for successor tasks or as replacements if the processing is not completed. Thereby, we increase the chances of assigning tasks with dependencies to nearby resources, thus reducing long-distance communication and hence latency. We evaluate our approach in a discrete-event simulation using real data from Telecom's base stations. The results demonstrate an average reduction of 43.44% in the number of failed dependent tasks, an increase of 5% in completed tasks, a reduction of 85.68% in failed tasks, a reduction of 47.32%. in delay duration, and a reduction of 3.06% in average execution task, compared to the existing alternative algorithm.

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Section: Related Workmentioning

confidence: 99%

Trust-Aware Scheduling for Edge Computing With Task Dependencies and Unreliable Servers

Alsenani,

Alnori

2023

IEEE Access

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“…Users can peruse V-home BOINC's page, which lists tasks, to acquire extensive information about the work. After a user uploads and submits a 'DepDisk' file, it is stored in the jobs/directory of the 'Job Service' project [ 69 ]. 'Sprite.App' and 'Sprite.Data' were renamed along with the software and other records/files within each numbered directory in accordance with the proposed method.…”

Section: Proposed Modelmentioning

confidence: 99%

Section: Ad-hoc Cloud System Guimentioning

confidence: 99%

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

et al. 2022

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In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the “Ad-hoc Cloud System” idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

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“…The highest failure rates for tasks throughout the life cycle of the data utilization trace was found in these physical hosts. The information contained within this trace has already been used by a number of papers on volunteer cloud and reliability assessment models to leverage real-world workload trace scenarios [61]- [63]. To the best of our knowledge, Google trace is the best realworld workload trace that includes our main contributiontask priority-that can be used to evaluate and validate our work.…”

Section: Dataset Tracementioning

confidence: 99%

ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing

et al. 2020

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With the exponential growth of large data produced by IoT applications and the need for lowcost computational resources, new paradigms such as volunteer cloud computing (VCC) have recently been introduced. In VCC, volunteers do not disclose resource information before joining the system. This leads to uncertainties about the level of trust in the system. The majority of available trust models are suitable for peer-to-peer (P2P) systems, which rely on direct and indirect interaction, and might cause memory consumption overhead concerns in large systems. To address this problem, this paper introduces ProTrust, a probabilistic framework that defines the trust of a host in VCC. We expand the concept of trust in VCC and develop two new metrics: (1) trustworthiness based on the priority of a task, named loyalty, and (2) trustworthiness affected by behavioral change. We first utilized a modified Beta distribution function, and the behavior of resources are classified into different loyalty levels. Then, we present a behavior detection method to reflect recent changes in behavior. We evaluated ProTrust experimentally with a real workload trace and observed that the framework's estimation of the trust score improved by approximately 15% and its memory consumption decreased by more than 65% compared to existing methods.

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ReMot Reputation and Resource-Based Model to Estimate the Reliability of the Host Machines in Volunteer Cloud Environment

Cited by 7 publications

References 22 publications

Trust-Aware Scheduling for Edge Computing With Task Dependencies and Unreliable Servers

Trust-Aware Scheduling for Edge Computing With Task Dependencies and Unreliable Servers

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing

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