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Trust calculation is an important aspect of social networks research which acts as the main pillar of the network’s existence. The research conducted in this domain still faces unsolved challenges that should be tackled to improve the accuracy of the results. One of these challenges is the cold start problem which we aim to reduce. We have achieved this goal by reducing the dependency of the trust calculation metrics on the links between two individual nodes. We calculate the trust score toward a node without the existence of a link between the trustor and a trustee according to individual characteristics and similarity factors. The consideration of both trust and distrust criteria in the model has resulted in higher accuracy and precision. The network is transformed into communities with nodes having similar subject interests by using topic modeling. The accuracy of the model presented in this research is not reduced despite the low number of communication edges. It has been shown in the evaluations that the mean absolute error of the trust calculations is about 0.01 even with the reduction of 90% of the edges. Hence, the model presented in this research is robust against the edge sparsity of the network. The proposed model is compared with notable current works namely the GCR method and the method presented by Akilal et al. Based on the average absolute error criterion, with a reduction of 90% of the edges, the accuracy of the proposed model shows a 98% increase compared to the GCR method and 96% increase compared to the Akilal et al.’s proposed method.

With the migration of the enterprise applications to micro-services and containers, cloud service providers, starting with Amazon in 2014, announced a new computational model called function-as-a-service. In these platforms, developers create a set of fine-grained functions with shorter execution times instead of developing coarse-grained software. In addition, the management of system resources and servers is entrusted to cloud service providers. This model has many benefits, such as reducing costs, improving resource utilization, and helping developers focus on the core logic of applications. But still faces many challenges such as balancing cost, balancing performance, programming models, using current dev tools, containers’ cold start problem, saving data in caches, security issues, privacy concerns, and scheduling challenges like execution time prediction. In this paper, we focus on scheduling and cold start problems. Compromise occurs when keeping warm operating environments which can reduce cold start times but increase costs. In this study, we propose a dynamic waiting time adjustment approach. We aim to create a trade-off by using four different types of decisions at runtime using a heuristic method and analyzing the function dependency graph, functions’ invocation frequency, and other environmental parameters. The proposed method shows a 32% improvement over the fixed-time method (i.e. the method used by Apache OpenWhisk). This comparison is made from the cumulative measurement viewpoint which is a combination of response time, turnaround time, cost, and utilization.

Trust models play an important role in computational environments. One of the main aims of the work undertaken in this domain is to provide a model that can better describe the socio-technical nature of computational trust. It has been recently shown that quantum-like formulations in the field of human decision making can better explain the underlying nature of these types of processes. Based on this research, the aim of this paper is to propose a novel model of trust based on quantum probabilities as the underlying mathematics of quantum theory. It will be shown that by using this new mathematical framework, we will have a powerful mechanism to model the contextuality property of trust. Also, it is hypothesized that many events or evaluations in the context of trust can be and should be considered as incompatible, which is unique to the noncommutative structure of quantum probabilities. The main contribution of this paper will be that, by using the quantum Bayesian inference mechanism for belief updating in the framework of quantum theory, we propose a biased trust inference mechanism. This mechanism allows us to model the negative and positive biases that a trustor may subjectively feel toward a certain trustee candidate. It is shown that by using this bias, we can model and describe the exploration versus exploitation problem in the context of trust decision making, recency effects for recently good or bad transactions, filtering pessimistic and optimistic recommendations that may result in good-mouthing or bad-mouthing attacks, the attitude of the trustor toward risk and uncertainty in different situations and the pseudo-transitivity property of trust. Finally, we have conducted several experimental evaluations in order to demonstrate the effectiveness of the proposed model in different scenarios.