The current trend to move from homogeneous to heterogeneous multicore systems provides compelling opportunities for achieving performance and energy efficiency goals. Running multiple threads in multicore systems poses challenges on meeting limited shared resources, such as memory bandwidth. We propose an optimization approach that includes an Integer Linear Programming (ILP) optimization model and a scheme to dynamically determine thread-to-core assignment. We present simulation analysis that shows energy savings and performance gains for a variety of workloads compared to state-of-the-art schemes. We implemented and evaluated a prototype of our thread assignment approach at user level, leveraging Linux scheduling and performance-monitoring capabilities.
This paper proposes and evaluates an approach for power and performance management in virtualized server clusters. The major goal of our approach is to reduce power consumption in the cluster while meeting performance requirements. The contributions of this paper are: (1) a simple but effective way of modeling power consumption and capacity of servers even under heterogeneous and changing workloads, and (2) an optimization strategy based on a mixed integer programming model for achieving improvements on power-efficiency while providing performance guarantees in the virtualized cluster. In the optimization model, we address application workload balancing and the often ignored switching costs due to frequent and undesirable turning servers on/off and VM relocations. We show the effectiveness of the approach applied to a server cluster testbed. Our experiments show that our approach conserves about 50% of the energy required by a system designed for peak workload scenario, with little impact on the applications' performance goals. Also, by using prediction in our optimization strategy, further QoS improvement was achieved.
In this paper we present an optimization solution for power and performance management in a platform running multiple independent applications. Our approach assumes a virtualized server environment and includes an optimization model and strategy to dynamically control the cluster power consumption, while meeting the application's workload demands.
We present a decision-level data fusion technique for monitoring and reporting critical health conditions of a hypertensive patient at home. Variables associated to the patient (physiological and behavioral) and to the living environment are considered in the solution, contributing to improve the confidence on the system outputs. In the paper, we model the problem variables as fuzzy, aiming to capture their intrinsic essence, and draw rules based on medical recommendations to identify the health condition of the patient. This initiative move towards to build an abstract framework for context-aware telemonitoring applications. We also describe the relevant components of the framework and provide an initial evaluation of its decision component. Our results demonstrate that a principled choice of rules and variables may lead to a consistent identification of critical patient's conditions. Pervasive health care, context-awareness, home care, decision making I.
This paper presents a comprehensive approach to describe, deploy and adapt component-based applications having dynamic non-functional requirements. The approach is centered on high-level contracts associated to architectural descriptions, which allow the non-functional requirements to be handled separately during the system design process. This helps to achieve separation of concerns facilitating the reuse of modules that implement the application in other systems. Besides specifying non-functional requirements, contracts are used at runtime to guide configuration adaptations required to enforce these requirements. The infrastructure required to manage the contracts follows an architectural pattern, which can be directly mapped to specific components included in a supporting reflective middleware. This allows designers to write a contract and to follow standard recipes to insert the extra code required to its enforcement in the supporting middleware.
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