Minimizing Execution Costs when Using Globally Distributed Cloud Services

Pandey, Suraj; Barker, Adam; Gupta, Kapil Kumar; Buyya, Rajkumar

doi:10.1109/aina.2010.30

Cited by 38 publications

(18 citation statements)

References 13 publications

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“…It may be done through distributed processing like using Map-reduce framework of Apache hadoop or Google [34]. In 2010, Pandey et al [35] showed a nonlinear programming model for minimizing execution cost of workflows in cloud using globally distributed cloud storage servers.…”

Section: E Minimizing Computational Costmentioning

confidence: 99%

Storage cost minimizing in cloud — A proposed novel approach based on multiple key cryptography

Saha

Ali

2014

Asia-Pacific World Congress on Computer Science and Engineering

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Cloud computing has been able to change the present style of computation all over the internet. This technology has proved itself as a blessing of computational science because of its ability for releasing massive computational and storage load with reducing cost from anywhere to any user and any time. Now the concern is how secured and reliable the service can be provided to its customers with lower cost. Throughout this research we have first reviewed some cloud storage research, cost minimization procedures and data security approaches used in the cloud. Here we propose a novel approach for reducing file storage size based on data to RGB image conversion technique. Then we apply multiple key cryptography (MKC) technique which also provides security to the cloud data. Lastly we have been able to show the cost minimization strategy in the cloud storage using data-to-image-todata conversion procedure. This in turn helps power consumption in the cloud storage and thus also serves the purpose of green cloud computing. We have evaluated our approach using 10 different types of files within size of 1 MB where average file size reduction rate is 38.82%.

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Section: E Minimizing Computational Costmentioning

confidence: 99%

Storage cost minimizing in cloud — A proposed novel approach based on multiple key cryptography

Saha

Ali

2014

Asia-Pacific World Congress on Computer Science and Engineering

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“…Pandey et al [61] model the cost of an intrusion detection workflow execution on cloud resources using a Non-Linear Programming (NLP) solution. The NLP-model retrieves data partially from multiple data sources based on the cost of transferring data from those resources to a compute resource, so that the total cost of data-transfer and computation cost on that compute resource is minimized.…”

Section: Resource Provisioning In the Cloudmentioning

confidence: 99%

Cloud Auto-Scaling with Deadline and Budget Constraints

Mao

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The cloud has become an important computing platform. It has attracted many businesses and individual users by offering on-demand computing power and storage capacity. The economies of scale and pay-as-you-go billing model could save users large up-front capital investments and long term operation costs. A key feature of the cloud is the elasticity, the ability to dynamically acquire and release computing resources in response to demand. We believe the key to successful cloud adoption is to first decide how much and what type of resources is needed in the cloud ("provisioning") and then decide how to place computing activities onto each of the resources ("allocation"). This is a challenging problem because the mapping from user objectives to the resource provisioning and allocation plans is not trivial. It needs to carefully consider the following factors. A performance goal can be achieved through different types of resources with different costs. A fixed budget can be used to rent a wide variety of resource configurations for varying durations. The structure of a cloud application could be complex. Task precedence orders need to be preserved in a job. The workload may experience unexpected peaks. The performance requirements and cost constraints may be changed dynamically.This dissertation solves this resource provisioning and allocation problem using an auto-scaling approach.It solves the batch-queue application model based on the integer programming technique. By ensuring the computing power is always large enough to handle the workload for all the VM types, in our experiment, our approach finishes more than 95% jobs before the deadline and saves 20.2% -40.1% cost compared to a fixed machine type choice. Our approach contains several innovative heuristics for the workflow application model. In the unlimited budget case, the presented solution -dynamic scaling-consolidation-scheduling (SCS) -can save 9.4% -40.4% cost compared to two baseline approaches and can work well in both light and heavy workload environments. In the limited budget case, our scheduling-first and scaling-first algorithms can reduce 9.8% -45.2% job turnaround time than the standard machine choice and they also show good tolerance (between -10.2% and 16.7%) to inaccurate parameters (±20% error). Finally, this dissertation presents three job scheduling policies and a data prefetching strategy to manage the intermediate data for data-intensive applications running in the cloud. Particularly, the cost-deadline-first (CDF) algorithm can i Abstract ii save 13.5% -33.7% cost compared to the deadline-first (DF) algorithm and the data prefetching strategy can further improve the cost saving up to 44.6% through data locality aware job placement. Approval SheetThis dissertation is submitted in partial fulfillment of the requirements for the degree of I am so lucky to have so many friends to support me all the time. They share both the joys and tears with me. They help me out of troubles, bring me happiness and inspire me with great ideas. Th...

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“…It offers on-line processing of regional to global climate change related quantitative RS data products with these multi-source RS data across data centers. The RS data products generated by MDCPS include vegetation related parameters like NDVI 18 and NPP 19 , radiation and hydrothermal flux related parameters like AOD 20 and SM 21 , as well as global ice change and mineral related parameters. The 5-day global synthetic NDVI parameter product in 2014 generated using MODIS 1km data is showed in figure 10.…”

Section: Experiments and Discussionmentioning

confidence: 99%

pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang

Yan

et al. 2018

Future Generation Computer Systems

173

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With the increasing requirement of accurate and up-to-date resource & environmental information for regional and global monitoring, large-region covered multi-temporal, multi-spectral massive remote sensing (RS) datasets are exploited for processing. The remote sensing data processing generally follows a complex multi-stage processing chain, which consists of several independent processing steps subject to types of RS applications. In general the RS data processing for regional environmental and disaster monitoring are recognized as typical both compute-intensive and data-intensive applications.To solve the aforementioned issues efficiently, we propose pipsCloud which combine recent Cloud computing and HPC techniques to enable large-scale RS data processing system as on-demand real-time services. Benefiting from the ubiquity, elasticity and high-level of transparency of Cloud computing model, the massive RS data managing and data processing for dynamic environmental monitoring are all encapsulate as Cloud with Web interfaces. Where, a Hilbert-R + based data indexing mechanism is employed for optimal query and access of RS imageries, RS data products as well as interim data. In the core platform beneath the Cloud services, we provide a parallel file system for massive high-dimensional RS data and offers interfaces for intensive irregular RS data accessing so as to provide improved data locality and optimized I/O performance. Moreover, we adopt an adaptive RS data analysis workflow manage system for on-demand workflow construction and collaborative execution of distributed complex chain of RS data processing, such as forest fire detection, mineral resources and coastline monitoring. Through the experimental analysis we have show the efficiency of the pipsCloud platform.

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Minimizing Execution Costs when Using Globally Distributed Cloud Services

Abstract: Cloud computing is an emerging technology that allows users to utilize on-demand computation, storage, data

Cited by 38 publications

References 13 publications

Storage cost minimizing in cloud — A proposed novel approach based on multiple key cryptography

Storage cost minimizing in cloud — A proposed novel approach based on multiple key cryptography

Cloud Auto-Scaling with Deadline and Budget Constraints

pipsCloud: High performance cloud computing for remote sensing big data management and processing

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Minimizing Execution Costs when Using Globally Distributed Cloud Services

Abstract: Cloud computing is an emerging technology that allows users to utilize on-demand computation, storage, data

Cited by 38 publications

References 13 publications

Storage cost minimizing in cloud &#x2014; A proposed novel approach based on multiple key cryptography

Storage cost minimizing in cloud &#x2014; A proposed novel approach based on multiple key cryptography

Cloud Auto-Scaling with Deadline and Budget Constraints

pipsCloud: High performance cloud computing for remote sensing big data management and processing

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Product

Resources

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Storage cost minimizing in cloud — A proposed novel approach based on multiple key cryptography

Storage cost minimizing in cloud — A proposed novel approach based on multiple key cryptography