학술논문

ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing
Document Type
Periodical
Source
IEEE Access Access, IEEE. 8:135059-135074 2020
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Cloud computing
Task analysis
Computational modeling
Peer-to-peer computing
Probabilistic logic
Biological system modeling
Memory management
Volunteer cloud computing
volunteer computing
cloud computing
trust
reputation
IoT
Language
ISSN
2169-3536
Abstract
With the exponential growth of large data produced by IoT applications and the need for low-cost 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.