In this paper, the Stackelberg game models are used for supporting the decisions on task scheduling and resource utilization in computational clouds. Stackelberg games are asymmetric games, where a specific group of players’ acts first as leaders, and the rest of the players follow the leaders’ decisions and make their decisions based on the leader’s actions. In the proposed model, the optimal schedules are generated under the security criteria along with the generation of the optimal virtual machines set for the scheduled batch of tasks. The security criteria are defined as security requirements for mapping tasks onto virtual machines with specified trust level. The effectiveness of the proposed method has been verified in the realistic use cases with in the cloud environment with OpenStack and Amazon Cloud standards.
Background: Our research assumes that the software quality affects the product validity. This assumption also refers to embedded software. Objectives: This paper analyses the Stackelberg equilibrium in which the consumer is the leader and the producer of embedded software is the follower. Methods/Approach: A comparative statics analysis of a producer's reaction is carried out and confirms our intuition that the product price is positively correlated to the number of employees and the software quality. Results: An increase in wage has an adverse effect on producer’s reaction. Derived results are illustrated numerically and Stackelberg and cooperative equilibrium are compared. It is shown that the welfare loss is smaller with higher quality software for any number of employees. Conclusions: Although the equilibrium involves less employed workers, the optimal software quality is higher. The optimal product price is lower, which puts the consumer and the producer in a better position
Stochastic multilevel programming is a mathematical programming problem with some given number of hierarchical levels of decentralized decision makers and having some kind of randomness properties in the problem definition. The introduction of some randomness property in its hierarchical structure makes stochastic multilevel problems computationally challenging and expensive. In this article, a systematic sampling evolutionary method is adapted to solve the problem. The solution procedure is based on realization of the random variables and systematic partitioning of each hierarchical level’s decision space for searching an optimal reaction. The search goes sequentially upwards starting from the bottom up through the top hierarchical level problem. The existence of solution and convergence of the solution procedure is shown. The solution procedure is implemented and tested on some selected deterministic test problems from literature. Moreover, the proposed algorithm can be used to solve stochastic multilevel programming problems with additional complexity in their problem definition.
Stackelberg games are non-symmetric games where one player or specified group of players have the privilege position and make decision before the other players. Such games are used in telecommunication and computational systems for supporting administrative decisions. Recently Stackleberg games became useful also in the systems where security issues are the crucial decision criteria. In this paper authors briefly survey the most popular Stackelberg security game models and provide the analysis of the model properties illustrated in the realistic use cases.
W artykule zaprezentowane zostało środowisko wieloagentowe, w którym każdy z agentów dąży do osiągnięcia własnego celu. Zakłada się, że preferencje osiągnięcia poszczególnych podcelów, składających się na cel, mogą być różne dla poszczególnych agentów, natomiast same cele mogą być sprzeczne ze sobą. Dodatkowo zakłada się strukturę hierarchiczną wśród agentów, wynikającą z różnych możliwości ich oddziaływania na otoczenie.
EN
The article presents multi-agent environment, in which each agent seeks to achieve its objective. It is assumed that the preferences of achieving the specific sub-goals which define the goal may be different for individual agents, and agent goals may be in conflict with each other. Additionally it is assumed a hierarchical structure among agents, due to different possibilities of their impact on the environment.
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