An Economical and SLO-Guaranteed Cloud Storage Service Across Multiple Cloud Service Providers
An Economical and SLO-Guaranteed Cloud Storage Service Across Multiple Cloud Service Providers
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DOI：
10.1109/tpds.2017.2675422
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发表时间：
2016-04
影响因子：
5.3
通讯作者：
Guoxin Liu;Haiying Shen;Haoyu Wang
中科院分区：
文献类型：
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作者：
Guoxin Liu;Haiying Shen;Haoyu Wang
对于云服务代理来说，重要的是提供多云存储服务，以最大限度地减少云服务提供商（csp）的支付成本，同时向客户提供服务水平目标（SLO）保证。已经提出了许多多云存储服务或支付成本最小化或SLO保证。但是，以前的作品没有充分利用当前的云定价策略（如资源预留定价）来降低支付成本。同时实现成本最小化和SLO保证的工程很少。在本文中，我们提出了一种多云经济和慢速保证的存储服务（$ES^3$），它在支付成本最小化和慢速保证的情况下确定数据分配和资源预留计划。$ES^3$整合了(1)协调的数据分配和资源预留方法，该方法将每个数据项分配到一个数据中心，并通过利用所有定价策略来确定数据中心的资源预留量；(2)基于遗传算法的数据分配调整方法，减小各数据中心的数据Get/Put率差异，使预留效益最大化。我们还提出了几种算法来提高$ES^3$的成本效率和SLO保证性能，包括i)动态请求重定向，ii)降低成本的分组Get， iii)成本高效的put的延迟更新，以及iv)刚性Get SLO保证的并发请求。我们在超级计算集群和真实云（即Amazon S3， Windows Azure Storage和谷歌Cloud Storage）上进行的跟踪驱动实验表明，与之前的方法相比，在支付成本最小化和SLO保证方面具有$ES^3$的优越性能。
It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service ($ES^3$ ), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. $ES^3$ incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit. We also propose several algorithms to enhance the cost efficient and SLO guarantee performance of $ES^3$ 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 $ES^3$ in payment cost minimization and SLO guarantee in comparison with previous methods.