Synthesising robust schedules for minimum disruption repair using linear programming

An off-line scheduling algorithm considers resource, precedence, and synchronisation requirements of a task graph, and generates a schedule guaranteeing its timing requirements. This schedule must, however, be executed in a dynamic and unpredictable operating environment where resources may fail and tasks may execute longer than expected. To accommodate such execution uncertainties, this paper addresses the synthesis of robust task schedules using a slack-based approach and proposes a solution using integer linear programming (ILP). Earlier we formulated a time slot based ILP model whose solutions maximise the temporal flexibility of the overall task schedule. In this paper, we propose an improved, interval based model, compare it to the former, and evaluate both on a set of random scenarios using two public domain ILP solvers and a proprietary SAT/ILP mixed solver

Stannoxanes and phosphonates: new approaches in organometallic and transition metal assemblies

Phosphonate ligands, [RPO3]2-, are extremely versatile in the assembly of multi-tin and multi-copper architectures. We have used organostannoxane cores for supporting multi-ferrocene and multi-porphyrin peripheries. The copper-metalated multi-porphyrin compound is an excellent reagent for facile cleavage of DNA, even in the absence of a co-oxidant. Reaction oft-BuPO3H2 with Cu(C104)2. 6H2O in the presence of 2-pyridylpyrazole (2-Pypz) leads to the synthesis of a decanuclear copper (II) assembly

A hierarchical optimization framework for autonomic performance management of distributed computing systems

26th IEEE International Conference on Distributed Computing Systems, ICDCS 2006: pp. 1648796-1 - 1648796-10.This paper develops a scalable online optimization framework for the autonomic performance management of distributed computing systems operating in a dynamic environment to satisfy desired quality-ofservice objectives. To efficiently solve the performance management problems of interest in a distributed setting, we develop a hierarchical structure where a highlevel limited-lookahead controller manages interactions between lower-level controllers using forecast operating and environment parameters. We develop the overall control structure, and as a case study, show how to efficiently manage the power consumed by a computer cluster. Using workload traces from the Soccer World Cup 98 web site, we show via simulations that the proposed method is scalable, has low run-time overhead, and adapts quickly to time-varying workload patterns

Distributed cooperative control for adaptive performance management

IEEE Internet Computing, 11(1): pp. 31-39.The authors’ distributed cooperative-control framework uses concepts from optimal control theory to adaptively manage the performance of computer clusters operating in dynamic and uncertain environments. Decomposing the overall performance-management problem into smaller subproblems that individual controllers solve cooperatively allows for the scalable control of large computing systems. The control framework also adapts to controller failures and allows for the dynamic addition and removal of controllers during system operation. This article presents a case study showing how to manage the dynamic power consumed by a computer cluster processing a time-varying Web workload

Adaptive performance control of computing systems via distributed cooperative control: Application to power management in computing clusters

Proceedings of the 3rd International Conference on Autonomic Computing, ICAC 2006, pp. 165-174.Advanced control and optimization techniques offer a theoretically sound basis to enable self-managing behavior in distributed computing models such as utility computing. To tractably solve the performance management problems of interest, including resource allocation and provisioning in such distributed computing environments, we develop a fully decentralized control framework wherein the optimization problem for the system is first decomposed into sub-problems, and each sub-problem is solved separately by individual controllers to achieve the overall performance objectives. Concepts from optimal control theory are used to implement individual controllers. The proposed framework is highly scalable, naturally tolerates controller failures, and allows for the dynamic addition/removal of controllers during system operation. As a case study, we apply the control framework to minimize the power consumed by a computing cluster subject to a dynamic workload while satisfying the specified quality-of-service goals. Simulations using real-world workload traces show that the proposed technique has very low control overhead, and adapts quickly to both workload variations and controller failures