Conference Paper

Tradeoff Exploration between Reliability, Power Consumption, and Execution Time.

DOI: 10.1007/s10009-012-0263-9 Conference: Computer Safety, Reliability, and Security - 30th International Conference, SAFECOMP 2011, Naples, Italy, September 19-22, 2011. Proceedings
Source: DBLP

ABSTRACT For autonomous critical real-time embedded (e.g., satellite), guaranteeing a very high level of reliability is as important as keeping the power consumption as low as possible. We propose an off-line scheduling heuristic which, from a given software application graph and a given multiprocessor architecture (homogeneous and fully connected), produces a static multiprocessor schedule that optimizes three criteria: its length (crucial for real-time systems), its reliability (crucial for dependable systems), and its power consumption (crucial for autonomous systems). Our tricriteria scheduling heuristic, called TSH, uses the active replication of the operations and the data-dependencies to increase the reliability and uses dynamic voltage and frequency scaling to lower the power consumption. We demonstrate the soundness of TSH. We also provide extensive simulation results to show how TSH behaves in practice: first, we run TSH on a single instance to provide the whole Pareto front in 3D; second, we compare TSH versus the ECS heuristic (Energy-Conscious Scheduling) from the literature; and third, we compare TSH versus an optimal Mixed Linear Integer Program.

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Available from: Alain Girault, Jan 20, 2015
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    • "Zhao et al. [22] used multi-objective ACO for reliability optimization of series-parallel systems. Assayad et al. [27] presented an offline scheduling heuristic to optimize reliability , power consumption, and performance of realtime embedded systems. A recent paper by Etemaadi and Chaudron [8] proposed two new generic architectural DoFs in metaheuristic optimization of component-based embedded systems: topology of hardware platform and load balancing of software components. "
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    ABSTRACT: In this paper, we present a novel Multi-Objective Ant Colony System algorithm to optimize Cost, Performance, and Reliability (MOACS-CoPeR) in the cloud. The proposed algorithm provides a metaheuristic-based approach for the multi-objective cloud-based software component deployment problem. MOACS-CoPeR explores the search-space of architecture design alternatives with respect to several architectural degrees of freedom and produces a set of Pareto-optimal deployment configurations. Two salient features of the proposed approach are that it is not dependent on a particular modeling language and it does not require an initial architecture configuration. Moreover, it eliminates undesired and infeasible configurations at an early stage by using performance and reliability requirements of individual software components as heuristic information to guide the search process. We also present a Java-based implementation of our proposed algorithm and compare its results with Non-dominated Sorting Genetic Algorithm II (NSGA-II). We evaluate the two algorithms against a cloud-based storage service, which is loosely based on a real system. The results show that MOACS-CoPeR outperforms NSGA-II in terms of number and quality of Pareto-optimal configurations found.
    • "The reliability of a task T i executed once at speed f is R i (f ) = e −λ(f )×Exe(w i ,f ) . Because the fault rate is usually very small, of the order of 10 −6 per time unit in [9] [43], 10 −5 in [5], we can use the first order approximation of R i (f ) as "
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    • "Because the failure rate˜λ 0 is usually very small, of the order of 10 −5 per time unit [2], or even 10 −6 [7] [16], we can use the first order approximation of R i (f ) as "
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    ABSTRACT: In this paper, we consider the problem of scheduling an application on a parallel computational platform. The application is a particular task graph, either a linear chain of tasks, or a set of independent tasks. The platform is made of identical processors, whose speed can be dynamically modified. It is also subject to failures: if a processor is slowed down to decrease the energy consumption, it has a higher chance to fail. Therefore, the scheduling problem requires to re-execute or replicate tasks (i.e., execute twice a same task, either on the same processor, or on two distinct processors), in order to increase the reliability. It is a tri-criteria problem: the goal is to minimize the energy consumption, while enforcing a bound on the total execution time (the makespan), and a constraint on the reliability of each task. Our main contribution is to propose approximation algorithms for these particular classes of task graphs. For linear chains, we design a fully polynomial time approximation scheme. However, we show that there exists no constant factor approximation algorithm for independent tasks, unless P=NP, and we are able in this case to propose an approximation algorithm with a relaxation on the makespan constraint.
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