ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing

Alsenani, Yousef; Crosby, Garth V.; Ahmed, Khaled R.; Velasco, Tomas

doi:10.1109/access.2020.3009051

Cited by 10 publications

(7 citation statements)

References 59 publications

(72 reference statements)

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“…The authors in [6] proposed ProTrust probabilistic trust framework in volunteer cloud computing. The framework proposed two terms; trustworthiness based on the priority of a task, and (2) trustworthiness computed by behavior detection.…”

Section: B Research In Volunteer Computingmentioning

confidence: 99%

“…Moreover, since volunteers offer spare computing resources of less powerful computers, their overall computing capability may not suffice to handle resource-intensive applications. As a result, volunteers can complement dedicated edge servers but not replace them [6], [7]. Managing the network of computing resources is crucial for effectively utilizing their computing power [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several studies have proposed approaches to enhance system performance and decrease task failure rates in edge computing environments [? ], [6], [10], [11]. However, existing approaches do not consider task dependencies.…”

Section: Introductionmentioning

confidence: 99%

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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: B Research In Volunteer Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alsenani,

Alnori

2023

IEEE Access

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“…New paradigms of cloud computing such as volunteer cloud computing (VCC) [74] are emerging to leverage volunteer contributions of large-scale cloud resources to reduce costs. Although VCC provides benefits in terms of cost, the proprietary/sensitive nature of certain scientific applications necessitates partial data processing to be performed at the edge resources i.e., closer to the application user sites.…”

Section: Significancementioning

confidence: 99%

“…Other notable works such as [71] and [72] propose a collaborative trust model based on the Beta distribution [73] and compare their model against random and deterministic resource selection strategies. Other probabilistic approaches such as [74] propose VCC trust by using the priority of tasks and behavioral changes as trustworthiness metrics.…”

Section: Significance and Related Workmentioning

confidence: 99%

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Pandey¹

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Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.

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