A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing

Balos, Cody J.; Vega, David; Abuelhaj, Zachariah; Kari, Chadi; Mueller, David; Pallipuram, Vivek K.

doi:10.1109/cloud.2018.00076

Cited by 5 publications

(10 citation statements)

References 17 publications

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“…This article presents the A2Cloud-random forest (RF) framework, an extension of our previously proposed A2Cloud framework. 8 This new framework adds statistical analysis of Cloud instance performance and most importantly, a random forest based machine learning strategy to dispense quality instance recommendations. All of the framework's components perform vital tasks that are highly instructive to the end users.…”

Section: Discussionmentioning

confidence: 99%

“…To compute the number of memory accesses (mem), the PERF engine adds the counter values of uncore read and uncore write operations (see Table 1 for counters) as shown in Equation (8). The current framework extracts the values of disk read and disk write components via user-guided micro-benchmarking.…”

Section: Perf Enginementioning

confidence: 99%

“…Furthermore, this raw analysis given by the A2Cloud-ext engine will increase in complexity should the users consider several Cloud instances. 8 In what follows, we elucidate how the RFC engine simplifies this analysis for the users.…”

Section: A2cloud Engine In Actionmentioning

confidence: 99%

“…The A2Cloud‐ext engine is an extension of our previously proposed A2Cloud model 8 . The engine inputs the application and Cloud traces to generate an application vector and a Cloud matrix.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed framework makes several contributions beyond our previous work. 8 Specifically, the framework:…”

mentioning

confidence: 99%

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A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Samuel

Khan

Balos

et al. 2020

Concurrency and Computation

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This article proposes a random-forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud-ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single-precision (SP) floating-point operations (FLOPs), double-precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud-ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application-to-Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application-specific random forests (ARF) and application-class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application-class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real-world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

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Section: Discussionmentioning

confidence: 99%

Section: Perf Enginementioning

confidence: 99%

Section: A2cloud Engine In Actionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The proposed framework makes several contributions beyond our previous work. 8 Specifically, the framework:…”

mentioning

confidence: 99%

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A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Samuel

Khan

Balos

et al. 2020

Concurrency and Computation

Self Cite

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Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.

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Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity

Jin

2021

J Supercomput

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A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing

Cited by 5 publications

References 17 publications

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Resource Management in a Containerized Cloud: Status and Challenges

Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity

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