PROJECT TITLE :
An Economical and SLO-Guaranteed Cloud Storage Service across Multiple Cloud Service Providers - 2017
ABSTRACT:
It's vital for cloud service brokers to supply a multi-cloud storage service to reduce their payment value to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services are proposed or payment cost minimization or SLO guarantee. But, no previous works absolutely leverage the present cloud pricing policies (like resource reservation pricing) to cut back the payment cost. Also, few works achieve each value minimization and SLO guarantee. In this paper, we tend to propose a multi-cloud Economical and SLO-guaranteed Storage Service (ES3), that determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 incorporates (1) a coordinated data allocation and resource reservation technique, that allocates every information item to a datacenter and determines the resource reservation quantity on datacenters by leveraging all the pricing policies; (two) a genetic algorithm based mostly knowledge allocation adjustment methodology, which cut back knowledge Get/Place rate variance in each datacenter to maximise the reservation profit. We have a tendency to additionally propose many algorithms to reinforce the price economical and SLO guarantee performance of ES3 including i) dynamic request redirection, ii) grouped Gets for cost reduction, iii) lazy update for cost-efficient Puts, and iv) concurrent requests for rigid Get SLO guarantee. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of ES3 in payment price minimization and SLO guarantee in comparison with previous ways.
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