Holger Trinks

A framework for analyzing and organizing complex systems

The paper discusses a framework and technologies enabling the quantitative analysis, organization and optimization of large-scale, globally distributed enterprise and e-services systems. The goal is to organize complex systems in such ways that traffic can be better explained, predicted and controlled at application and service layers rather than at the network layers. Our work approaches higher system perspectives where architectural decisions are made about the overall organization of work and task flows, the global placement of data and applications, etc. Those decisions are significant for the traffic induced in the system later on. Little support is provided today for designing and evaluating large-scale systems from these perspectives, primarily caused by the difficulties in developing realistic computerized models reflecting the dynamic behavior of services and applications. We reduce complex environments to uniform representations of resource demands and capacities and use them to improve the overall system organization. Case studies with earlier versions of our approach have been carried out with two corporate partners, which are discussed at the end.

Resource-sharing and service deployment in virtual data centers

The expectation of a global presence of services leads to the need for large numbers of service instances allocated in a multitude of regional data centers in order to provide sufficient service capacity close to where the demand occurs. Scale of service instances is anticipated growing /spl Gt/10/sup 4/ raising new challenges for control and management. Pragmatically, it must become much easier to deploy service instances in data center, allocating resources, sharing them, installing and configuring data and software needed for service instances and integrating them into a singular service that appears to a consumer. Adjusting numbers and locations of service instances is seen as a basic control mechanism in order to follow regional or temporal fluctuations in demands. The paper proposes a new concept of virtualizing whole data center environments and quickly deploying massive amounts of service instances. A virtualization layer takes care of resource allocation from different data center locations and all specifics when service instances are allocated in a particular data center. Virtualized data centers provide a consistent operating environment spanning multiple physical data center locations for the whole family of service instances. And vice versa, physical data centers host several execution environments for different services. After a brief discussion of challenges coming with the scale of service instances we anticipate, the paper overviews virtual data centers and discusses one aspect in more detail how massive amounts of service instances can be deployed using a recursive approach.

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.

Optimization of E-service solutions with the Systems of Servers library

The paper describes a modeling library for analysis and optimization of distributed service systems: enterprise computing infrastructures, E-commerce, and E-service systems. The library uses a small number of basic notions, such as service, server, cluster (of servers), clients and message, which allow describing, analyzing and optimizing various system configurations and distributions of services among servers. The service requests and responses are modeled by messages traveling in the system and using its common resources. Clients send their requests to servers, which either return responses or may issue secondary requests for other services. To find the optimal deployment of services among servers, different partition policies can be deployed including partitions based on genetic algorithms. The library is built on top of Communicating Structures Library (CSL), a modeling environment for large-scale distributed systems (V.E. Kotov, 1998). In its turn, Systems of Servers is a base for specialized libraries that target specific classes of service systems.

Self-Organizing Control in Plantetary-Scale Computing

The explosion in globally connected devices, computers, and services ultimately evolves into very large-scale, inter-connected systems approaching a new era of planetary-scale computing. The backbone of planetary-scale computing is a global network of data centers. The paper provides an overview of research at HP Laboratories exploring new approaches under such a scenario based on HPs virtual data center platform enabling large-scale distributed virtual data center environments. Our approach is founded in a service-centric system view, and we explore agent technology for resource control, system organization, and balancing resource demand and supply.

Adaptive service placement algorithms for autonomous service networks

Motivated by trends in the industry towards transforming IT in large integrated service networks, this paper describes algorithms for the adaptive placement of “services” (as abstractions of collections of applications) in networks of “servers” (as abstractions for locations where services can be hosted). Networks comprised of interacting services as the foundation is also a vision pronounced by the Grid [9]. Manageability and “self-operation” of Grids is highly desirable. We analyze the requirements for algorithms one specific problem: the service placement problem. We discuss algorithms that neither require central control nor complete information about the system state. Algorithms are performed on a distributed overlay structure which summarizes load conditions in the underlying service network. The presented algorithms fulfill tasks of making initial placement decisions as well as initiating rearrangements when imbalance is detected. Presented algorithms have different characteristics regarding the tradeoff between accuracy (or quality) of a placement decision and its timeliness within which a decision can be made determining responsiveness.