Service-Centric Globally Distributed Computing

An automated service demand–supply control system can improve a large-scale grid infrastructure comprising a federation of distributed utility data centers. T he growing complexity of large-scale systems raises concerns about their effectiveness and man-ageability. New approaches to system design, use, and management address these concerns through the aggregation and consolidation of system and application components into larger building blocks; systematic and standard ways of integrating such systems and communicating between them; sharing of distributed resources; and automated system management and operation control. Due to their scale and dynamism, large-scale systems cannot provide global information about resource availability and service demand. Decision-making algorithms thus need to deal with partial information, yet provide good approximations of localized assignment solutions. Moreover, they need to make decisions within the duration of the overall control cycle of an automated resource demand–supply control system. Various control cycles occur simultaneously, ranging from short-term reactive response (range of seconds to minutes) to mid-term system rebalancing (hours to days) to longer-term adjustments and system evolvement (months). Usually, decision algorithms implement trade-offs between the time spent identifying a solution and the quality of that solution. Two of the most prominent and practical approaches to solving these problems are utility computing and grid computing. Adopting the utility computing approach, Hewlett-Packard developed the Utility Data Center (UDC; see www.hp. com/go/hpudc/) to consolidate computing resources. Virtual data centers 1 consolidate the resources of a federation of distributed UDCs into virtual resources that users, user applications, and user services can share using grid-type mechanisms , thus significantly reducing deployment and operation costs. We propose an architecture for such an