Tornadoes In The Cloud: Worst-Case Attacks on Distributed Resources Systems

Geographically distributed cloud networks are used by a variety of applications and services worldwide. As the demand for these services increases, their data centers form an attractive target for malicious attackers, aiming at harming the services. In this study we address sophisticated attackers who aim at causing maximal-damage to the service. A worst-case (damage-maximizing) attack is an attack which minimizes the revenue of the system operator, due to disrupting the users from being served. A sophisticated attacker needs to decide how many attacking agents should be launched at each of the systems regions, in order to inflict maximal damage. We characterize and analyze damage-maximization strategies for a number of attacks including deterministic attack, concur-rent stochastic agents attack, approximation of a virus-spread attack and over-size binomial attack. We also address user-migration defense, allowing to dynamically migrate demands among regions, and we provide efficient algorithms for deriving worst-case attacks given a system with arbitrary placement and demands. The results form a basis for devising resource allocation strategies aiming at minimizing attack damages.