Resource Management on Multicore Systems: The ACTORS Approach

Bini, Enrico; Buttazzo, Giorgio; Eker, Johan; Schorr, Stefan; Guerra, Raphael; Fohler, Gerhard; Årzén, Karl-Erik; Romero, Vanessa; Scordino, Claudio

doi:10.1109/mm.2011.1

Cited by 60 publications

(40 citation statements)

References 11 publications

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“…An effective optimization algorithm strongly determines the overall parallel performance of the high-performance computing system [12][13][14]. The proposed algorithm for effective resource allocation in multi-core and multi-machine platforms is based on Artificial Bee Colony (ABC) metaheuristic.…”

Section: Parallel Computing Model For Implementation Of the Optimizatmentioning

confidence: 99%

Artificial Bee Colony Optimization Technique for Effective Resource Allocation

Hristov¹,

Borovska²

2016

ERSICT

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Abstract. Accelerating the development and deployment of advanced communication technologies and complex databases will require a comprehensive strategy integrating efforts from invention to deployment. The concurrent highperformance computing systems are composed of hundreds of thousands of computational nodes, as well as deep memory hierarchies and complex interconnect topologies. Existing high performance algorithms and tools already require courageous programming and optimization efforts to achieve high efficiency on current supercomputers. On the other hand, these efforts are platform-specific and non-portable. Since most of the existing optimization algorithms and tools are not optimized for modern computer architectures and cannot efficiently exploit massively parallel systems, one aim of this research is to identify and to analyze the general problems and modern trends in this research area. This paper investigates the efficiency of artificial bee colony optimization algorithm for effective resource allocation. Parallel version of the algorithm have been proposed based on the flat parallel programming model with message passing for communication between the computational nodes in the platform and parallel programming model with multithreading for communication between the cores inside the computational node. Parallel communications profiling is made and parallel performance parameters are evaluated on the basis of experimental results.

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Section: Parallel Computing Model For Implementation Of the Optimizatmentioning

confidence: 99%

Artificial Bee Colony Optimization Technique for Effective Resource Allocation

Hristov¹,

Borovska²

2016

ERSICT

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“…Resource reservation can be effectively used in this system to isolate the temporal behavior of the applications and limit their reciprocal interference (Bini et al, 2011). To do that, the processor should be partitioned into three reservations, with bandwidth U s 1 , U s 2 ,a n dU s 3 , each behaving as a slower processor running at speed U s i .…”

Section: Resource Reservation In Multimedia Systemsmentioning

confidence: 99%

“…When the amount of allocated bandwidth results to be quite different from the real application needs, adaptive approaches based on feedback mechanisms can be applied at runtime to adjust the allocated bandwidth to the real resource needs (Abeni & Buttazzo, 1999;Bini et al, 2011). …”

Section: Resource Reservation In Multimedia Systemsmentioning

confidence: 99%

Handling Overload Conditions in Real-Time Systems

Buttazzo¹

2012

Real-Time Systems, Architecture, Scheduling, and Application

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This chapter deals with the problem of handling overload conditions, that is, those critical situations in which the computational demand requested by the application exceeds the processor capacity. If not properly handled, an overload can cause an abrupt performance degradation, or even a system crash. Therefore, a real-time system should be designed to anticipate and tolerate unexpected overload situations through speciﬁc kernel mechanisms

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“…An example of combined use of optimization and feedback was developed in the ACTORS project [3,2]. In this project, applications provide a table to the RM describing the required amount of CPU resources and the expected QoS achieved at each supported service level [3,2].…”

Section: Introductionmentioning

confidence: 99%

“…In this project, applications provide a table to the RM describing the required amount of CPU resources and the expected QoS achieved at each supported service level [3,2]. In the multi-core case, applications are partitioned over the cores and the amount of resources is given for each individual partition.…”

Section: Introductionmentioning

confidence: 99%

Distributed management of CPU resources for time-sensitive applications

Chasparis

Maggio

Årzén

et al. 2013

2013 American Control Conference

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The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to efficient CPU allocation for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure and according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. It is shown analytically that efficient resource allocation can be achieved in a distributed fashion through the proposed adjustment process. Experiments using the TrueTime Matlab toolbox show the validity of our proposed approach. AbstractThe number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately existing optimization-based resource managers consume too much resource themselves.In this paper, we address the problem of distributed convergence to efficient CPU allocation for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure and according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. It is shown analytically that efficient resource allocation can be achieved in a distributed fashion through the proposed adjustment process. Experiments using the TrueTime Matlab toolbox show the validity of our proposed approach.

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Resource Management on Multicore Systems: The ACTORS Approach

Cited by 60 publications

References 11 publications

Artificial Bee Colony Optimization Technique for Effective Resource Allocation

Artificial Bee Colony Optimization Technique for Effective Resource Allocation

Handling Overload Conditions in Real-Time Systems

Distributed management of CPU resources for time-sensitive applications

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