Hierarchical network-aware placement of service oriented applications in clouds

In cloud environments, resources can be requested on-demand when they are needed. A cloud management system is responsible for determining which physical machines are responsible for processing the requests. The problem of determining which servers are used for which services is referred to as the Cloud Application Placement Problem (CAPP), and multiple criteria such as cost and number of migrations must be taken into account. When applications are constructed as a collection of communicating services, such as in Service-Oriented Architectures, it becomes important to take the underlying network properties into account when these placement decisions are made. In this paper, we propose an Integer Linear Programming (ILP) formulation for the CAPP, which optimizes multiple criteria such as cost, latency and number of migrations between subsequent invocations by using multiple optimization criteria. We also present hierarchical algorithms based on particle swarm optimization and genetic algorithms to solve the CAPP. These algorithms are be executed within a management hierarchy, which reduces the amount of information needed for the algorithms to function, increasing scalability of the management system. Finally, we evaluate the hierarchical algorithms by comparing them to an optimal algorithm based on the ILP formulation.In cloud environments, resources can be requested on-demand when they are needed. A cloud management system is responsible for determining which physical machines are responsible for processing the requests. The problem of determining which servers are used for which services is referred to as the Cloud Application Placement Problem (CAPP), and multiple criteria such as cost and number of migrations must be taken into account. When applications are constructed as a collection of communicating services, such as in Service-Oriented Architectures, it becomes important to take the underlying network properties into account when these placement decisions are made. In this paper, we propose an Integer Linear Programming (ILP) formulation for the CAPP, which optimizes multiple criteria such as cost, latency and number of migrations between subsequent invocations by using multiple optimization criteria. We also present hierarchical algorithms based on particle swarm optimization and genetic algorithms to solve the CAPP. These algorithms are be executed within a management hierarchy, which reduces the amount of information needed for the algorithms to function, increasing scalability of the management system. Finally, we evaluate the hierarchical algorithms by comparing them to an optimal algorithm based on the ILP formulation.P