Allocating modules to processors in a distributed system

Fernández-Baca, David

doi:10.1109/32.41334

Cited by 357 publications

(169 citation statements)

References 17 publications

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“…Factors involved in cost calculation include 24 : (i) cost of placing two processes on separate nodes (communication overheads); (ii) cost of placing processes on the same node (reduction in concurrency). Deriving the minimum cost has been shown to be an NP-complete problem 25,24 . Indeed, only by restricting the search space can tractable solutions be found.…”

Section: Allocation Of Periodic Processesmentioning

confidence: 99%

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Real-Time System Scheduling

Audsley

Burns

Davis

et al. 1995

Predictably Dependable Computing Systems

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Recent results in the application of scheduling theory to dependable real-time systems are reviewed. The review takes the form of an analysis of the problems presented by different application requirements and characteristics. Issues covered include uniprocessor and multiprocessor systems, periodic and aperiodic processes, static and dynamic algorithms, transient overloads and resource usage. Protocols that bound and reduce blocking are discussed. A review of specific real-time kernels is also included.

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Section: Allocation Of Periodic Processesmentioning

confidence: 99%

“…25 , (ii) the concurrency of the system, (iii) fault-tolerant considerations 48 , and (iv) relative periods. The cost of accessing resources or performing inter-process interactions is clearly more expensive for remote operations.…”

Section: Allocation Of Periodic Processesmentioning

confidence: 99%

Real-Time System Scheduling

Audsley

Burns

Davis

et al. 1995

Predictably Dependable Computing Systems

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“…We model services in a platform independent manner using the SPACE [3] methodology. As previously shown by Fernandez-Baca [4], the general module allocation problem is NP-complete except for certain communication configurations, thus heuristics are required to obtain solutions efficiently. Based on our service models, we apply an heuristic optimization method called the Cross-Entropy Ant System (CEAS) [5], which is able to take multiple parameters into account when making a decision on the deployment mapping.…”

Section: Introductionmentioning

confidence: 99%

Foraging for Better Deployment of Replicated Service Components

Csorba

Meling

Heegaard

et al. 2009

Distributed Applications and Interoperable Systems

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Abstract. Our work focuses on distributed software services and their requirements in terms of system performance and dependability. We target the problem of finding optimal deployment mappings involving multiple services, i.e. mapping service components in the software architecture to the underlying platforms for best possible execution. We capture important non-functional requirements of distributed services, regarding performance and dependability. These models are then used to construct appropriate cost functions that will guide our heuristic optimization method to provide better deployment mappings for service components. This paper mainly focuses on dependability. In particular, a logic enabling replication management and deployment for increased dependability is presented. To demonstrate the feasibility of our approach, we model a scenario with 15 services each with different redundancy levels deployed over a 10-node network. We show by simulation how the deployment logic proposed is capable to satisfy replica deployment requirements.

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“…Dynamic mapping is performed when the applications are mapped in an on-line fashion [25], e.g., when tasks arrive at unknown intervals and are mapped as they arrive (the workload is not known a priori). In both cases, the mapping problem has been shown, in general, to be NP-complete (e.g., [10,12,18]). Thus, the development of heuristic techniques to find near-optimal solutions for the mapping problem is an active area of research (e.g., [2,6,5,13,27,38]).…”

Section: Introductionmentioning

confidence: 99%

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

Kim

Shivle

Siegel

et al. 2007

Journal of Parallel and Distributed Computing

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In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.

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Allocating modules to processors in a distributed system

Cited by 357 publications

References 17 publications

Real-Time System Scheduling

Real-Time System Scheduling

Foraging for Better Deployment of Replicated Service Components

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

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