Multiagent and Grid Systems

Multi-agent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multi-agent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. The problem that this paper seeks to solve is the coordination of decentralized defender agents in the presence of uncertainty while securing targets against an observing adversary. To address this problem, we offer the following novel contributions in this paper: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column generation that utilizes Decentralized Markov Decision Processes (Dec-MDPs) to generate defender strategies that incorporate uncertainty; (iii) New techniques to handle global events (when one or more agents may leave the system) during defender execution; (iv) Heuristics that help scale up in the number of targets and agents to handle real-world scenarios; (v) Exploration of the robustness of randomized pure strategies. The paper opens the door to a potentially new area combining computational game theory and multi-agent teamwork.

The computational grids as a distributed system are hardware and software infrastructures that are capable of solving large-scale issues, and they use heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling is a critical and prominent issue in grid computing. An appropriate prediction method for allocating jobs to resources may significantly affect quality of service parameters. In this paper, a hierarchical approach is presented for job scheduling in computational grid utilizing a resource prediction method based on the scoring system. It is inspired by meta-heuristic algorithms in order to improve parameters such as makespan, load balancing and the rate of meeting deadlines. To evaluate the proposed method, GridSim toolkit is exploited. According to the simulation results and comparison with some recent well-known methods, this approach has been successful in improving the mentioned parameters.

Resource allocation is an important issue in any shared computer system. Recently, dynamic multi-resource fair allocation became a hot topic in cloud resource management. The most recent studies assume that each agent submits infinite number of tasks. In this paper, we consider a more realistic dynamic environment, where every agent submits a bounded number of tasks. We design a generalized dynamic dominant resource fairness mechanism, and develop a combinatorial optimal algorithm to find a fair allocation. Experimental results show that the solution produced by the proposed mechanism is close to the optimal solution.

We present an analytical study on services price competition in the cloud computing monopolistic markets. Our aim is to prove the existence of an equilibrium price in such competitive and non-cooperative markets, which maximizes the social welfare by the attraction of users and maximization of providers' profits without disturbance of competing providers. We propose a set of offline algorithms that converge with optimal cost to this equilibrium price.