Summary One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption.

Most research on movie recommender systems has been conducted with Collaborative Filtering (CF) methods. The lack of sufficient information about users’ interests in bootstrapping is one of the most critical problems of the CF method. Using a content-based filtering method can mitigate some of these problems. On the other hand, recent research has proven that utilising contextual information about the movie, such as genre, actors, and cast, can increase the efficiency of the recommender system. This paper uses a combined Genetic Algorithm (GA) and content-based filtering to find the best combination of genre, cast, and crew weights. We first convert each movie’s contextual information into a descriptive vector, including cast, crew, and genre. Then, we calculate the distance between each pair of sentence vectors using two separate approaches, fully connected and metadata-based. Finally, with GA, we tune the weight of each of the contextual information of movies to maximise the recommender system’s efficiency. Performance evaluation on the well-known MovieLens dataset shows that GA can improve the Precision@k criterion by producing fewer, more accurate recommendations. Weight adjustment by GA improves the F-Measure metric by approximately 58%. This, in turn, can improve Precision and Recall metrics. Also, the GA offers a higher correct recommendation rate than other methods.

Abstract Resource provisioning and allocation are two essential operations of cloud service providers (CSPs). Due to limited resources, it may sometimes not be cost‐effective for CSPs to obtain them from the market. On the other hand, releasing some less profitable resources may violate the service level agreement (SLA). This article presents an auction‐inspired model for providing/demanding resources without exchanging money among CSPs. The core idea is that CSPs do not need to pay monetary values. Instead, they exchange free resources through an auction mechanism and are encouraged to cooperate. This model, which we call bartering double auction resource allocation, helps CSPs receive the resources they need in the form of virtual machine instances with different types of functionalities. Under this mechanism, if one party violates the SLA, the other takes back its resources to avoid possible loss. The experimental results using the CloudSim simulator and the statistical analyses show that the proposed mechanism can increase CSPs' social welfare and profitability while significantly reducing SLA violations. Also, the proposed model provides four essential economic properties, that is, individual rationality , incentive compatibility , budget balancing , and economic efficiency .

This paper targets at service provisioning and bandwidth allocation in single-service overlay multicast network by modeling it as a first-price monopoly auction. We propose a game theoretic pricing mechanism for determining the distribution of a typical multimedia content from an origin server, via the unicast links. By leveraging the rich theory of strategic mechanism design of microeconomics, we formulate this problem as an optimization problem that aims to achieve Nash equilibrium for bidding strategy of the buyers. In our proposal, the content of the multimedia service accounts for the good. The sellers of this auction economy are either the origin server or the peers who forward the multimedia service to their downstream peers. For each seller, its corresponding downstream peers play the role of buyers who are referred to as bidders. Each bidder submits a sealed bid to its corresponding seller. The high bidder wins and pays its bid for the service. The analytical and experimental results prove the efficiency of the proposed mechanism in terms of the overlay path stretch.