In Cloud service composition, collaboration between brokers and service providers is essential to promptly satisfy incoming Cloud consumer requirements. These requirements should be mapped to Cloud resources, which are accessed via web services, in an automated manner. However, distributed and constantly changing Cloud-computing environments pose new challenges to automated service composition such as: (i) dynamically contracting service providers, which set service fees on a supply-and-demand basis, and (ii) dealing with incomplete information regarding Cloud resources (e.g., location and providers). To address these issues, in this work, an agent-based Cloud service composition approach is presented. Cloud participants and resources are implemented and instantiated by agents. These agents sustain a three-layered self-organizing multi-agent system that establishes a Cloud service composition framework and an experimental test bed. The self-organizing agents make use of acquaintance networks and the contract net protocol to evolve and adapt Cloud service compositions. The experimental results indicate that service composition is efficiently achieved despite dealing with incomplete information as well as coping with dynamic service fees.

Load management in cloud data centers must take into account 1) hardware diversity of hosts, 2) heterogeneous user requirements, 3) volatile resource usage profiles of virtual machines (VMs), 4) fluctuating load patterns, and 5) energy consumption. This work proposes distributed problem solving techniques for load management in data centers supported by VM live migration. Collaborative agents are endowed with a load balancing protocol and an energy-aware consolidation protocol to balance and consolidate heterogeneous loads in a distributed manner while reducing energy consumption costs. Agents are provided with 1) policies for deciding when to migrate VMs, 2) a set of heuristics for selecting the VMs to be migrated, 3) a set of host selection heuristics for determining where to migrate VMs, and 4) policies for determining when to turn off/on hosts. This paper also proposes a novel load balancing heuristic that migrates the VMs causing the largest resource usage imbalance from overloaded hosts to underutilized hosts whose resource usage imbalances are reduced the most by hosting the VMs. Empirical results show that agents adopting the distributed problem solving techniques are efficient and effective in balancing data centers, consolidating heterogeneous loads, and carrying out energy-aware server consolidation.