Application placement using performance surfaces

Turgeon, André; Snell, Quinn; Clement, Mark J.

doi:10.1109/hpdc.2000.868654

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…In the area of parallel and grid computing, several object placement strategies (or, meta-scheduling strategies) have been investigated [19,20]. Communication overhead among objects placed on various machines in a heterogeneous distributed computing environment plays an important role in the object placement strategy.…”

Section: Related Workmentioning

confidence: 99%

Dynamic placement for clustered web applications

Karve¹,

Kimbrel²,

Pacifici³

et al. 2006

Proceedings of the 15th International Conference on World Wide Web

150

131

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We introduce and evaluate a middleware clustering technology capable of allocating resources to web applications through dynamic application instance placement. We define application instance placement as the problem of placing application instances on a given set of server machines to adjust the amount of resources available to applications in response to varying resource demands of application clusters. The objective is to maximize the amount of demand that may be satisfied using a configured placement. To limit the disturbance to the system caused by starting and stopping application instances, the placement algorithm attempts to minimize the number of placement changes. It also strives to keep resource utilization balanced across all server machines. Two types of resources are managed, one load-dependent and one load-independent. When putting the chosen placement in effect our controller schedules placement changes in a manner that limits the disruption to the system.

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Section: Related Workmentioning

confidence: 99%

Dynamic placement for clustered web applications

Karve¹,

Kimbrel²,

Pacifici³

et al. 2006

Proceedings of the 15th International Conference on World Wide Web

150

131

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“…Meta-scheduling algorithms for grid and parallel computing also deal with the placement problem [13], but in our case we are not concerned about communication overheads between application servers because this is not a key point in the management of dynamic web applications.…”

Section: Related Workmentioning

confidence: 99%

Utility-based placement of dynamic Web applications with fairness goals

Carrera

Steinder

Whalley

et al. 2008

NOMS 2008 - 2008 IEEE Network Operations and Management Symposium

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Abstract-We study the problem of dynamic resource allocation to clustered Web applications. We extend application server middleware with the ability to automatically decide the size of application clusters and their placement on physical machines. Unlike existing solutions, which focus on maximizing resource utilization and may unfairly treat some applications, the approach introduced in this paper considers the satisfaction of each application with a particular resource allocation and attempts to at least equally satisfy all applications. We model satisfaction using utility functions, mapping CPU resource allocation to the performance of an application relative to its objective. The demonstrated online placement technique aims at equalizing the utility value across all applications while also satisfying operational constraints, preventing the over-allocation of memory, and minimizing the number of placement changes. We have implemented our technique in a leading commercial middleware product. Using this real-life testbed and a simulation we demonstrate the benefit of the utility-driven technique as compared to other state-of-the-art techniques.

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“…(4) History Mean Scheduling (HMS): Use the mean of the history CPU load for the 5 minutes preceding the application start time for the value for effective CPU load. This approximates the estimates used in several common scheduling approaches[27,29]. (5) History Conservative Scheduling (HCS): Use the conservative estimate CPU load defined by adding the mean and variance of the history CPU load collected for 5 minutes preceding the application run as the effective CPU load.…”

mentioning

confidence: 99%

Conservative Scheduling

Yang

Schopf

Foster

2003

Proceedings of the 2003 ACM/IEEE Conference on Supercomputing

118

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In heterogeneous and dynamic environments, efficient execution of parallel computations can require mappings of tasks to processors whose performance is both irregular (because of heterogeneity) and time-varying (because of dynamicity). While adaptive domain decomposition techniques have been used to address heterogeneous resource capabilities, temporal variations in those capabilities have seldom been considered. We propose a conservative scheduling policy that uses information about expected future variance in resource capabilities to produce more efficient data mapping decisions. We first present techniques, based on time series predictors that we developed in previous work, for predicting CPU load at some future time point, average CPU load for some future time interval, and variation of CPU load over some future time interval. We then present a family of stochastic scheduling algorithms that exploit such predictions of future availability and variability when making data mapping decisions. Finally, we describe experiments in which we apply our techniques to an astrophysics application. The results of these experiments demonstrate that conservative scheduling can produce execution times that are both significantly faster and less variable than other techniques.

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Application placement using performance surfaces

Cited by 6 publications

References 10 publications

Dynamic placement for clustered web applications

Dynamic placement for clustered web applications

Utility-based placement of dynamic Web applications with fairness goals

Conservative Scheduling

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