Autonomic Information Flows

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Schwan, Karsten
Cooper, Brian F.
Eisenhauer, Greg S.
Gavrilovska, Ada
Wolf, Matthew
Abbasi, Hasan
Agarwala, Sandip
Cai, Zhongtang
Kumar, Vibhore
Lofstead, Jay
Mansour, Mohamed S.
Seshasayee, Balasubramanian
Widener, Patrick M. (Patrick McCall)
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Today's enterprise systems and applications implement functionality that is critical to the ability of society to function. These complex distributed applications, therefore, must meet dynamic criticality objectives even when running on shared heterogeneous and dynamic computational and communication infrastructures. Focusing on the broad class of applications structured as distributed information flows, the premise of our research is that it is difficult, if not impossible, to meet their dynamic service requirements unless these applications exhibit autonomic or self-adjusting behaviors that are `vertically' integrated with underlying distributed systems and hardware. Namely, their autonomic functionality should extend beyond the dynamic load balancing or request routing explored in current web-based software infrastructures to (1) exploit the ability of middleware or systems to be aware of underlying resource availabilities, (2) dynamically and jointly adjust the behaviors of interacting elements of the software stack being used, and even (3) dynamically extend distributed platforms with enterprise functionality (e.g., network-level business rules for data routing and distribution). The resulting vertically integrated systems can meet stringent criticality or performance requirements, reduce potentially conflicting behaviors across applications, middleware, systems, and resources, and prevent breaches of the `performance firewalls' that isolate critical from non-critical applications. This paper uses representative information flow applications to argue the importance of vertical integration for meeting criticality requirements. This is followed by a description of the AutoFlow middleware, which offers methods that drive the control of application services with runtime knowledge of current resource behavior. Finally, we demonstrate the opportunities derived from the additional ability of AutoFlow to enhance such methods by also dynamically extending and controlling the underlying software stack, first to better understand its behavior and second, to dynamically customize it to better meet current criticality requirements.
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