Splice: An Automated Framework for Cost-and Performance-Aware Blending of Cloud Services
Splice: An Automated Framework for Cost-and Performance-Aware Blending of Cloud Services
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DOI：
10.1109/ccgrid54584.2022.00021
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发表时间：
2022-05
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通讯作者：
Myungjun Son;S. Mohanty;Jashwant Raj Gunasekaran;Aman Jain;M. Kandemir;G. Kesidis;B. Urgaonkar
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作者：
Myungjun Son;S. Mohanty;Jashwant Raj Gunasekaran;Aman Jain;M. Kandemir;G. Kesidis;B. Urgaonkar
随着采用公共云运行其应用程序的用户的快速增长，从不同的公共云资源产品中采购的资源类型对于同时实现令人满意的性能和降低部署成本至关重要。通常，没有一种资源类型可以满足所有应用程序的要求，因此，结合不同的资源供应是众所周知的，以大大减少性能成本问题。然而，由于设计和实现这种混合方法的手动开销，使用混合资源并不简单。具体来说，它需要重写应用程序代码以适应给定的资源，并根据需要对其进行扩展。为了克服这个手动障碍，我们迈出了第一步，提出了Splice，这是一个用于IaaS和FaaS服务的成本和性能感知混合的自动化框架。Splice的三个主要目标是：（1）虽然混合资源存在节省成本的机会，但我们的目标是通过编译器驱动的方法在很大程度上自动化公共云服务的混合过程;（2）更具体地说，我们专注于虚拟机和无服务器功能的自动混合;以及（3）对于包含多个链式函数的无服务器应用，我们发掘潜在的选择，以确定一部分服务将以成本效益的方式混合。我们使用抽象树（AST）在Amazon Web Services（AWS）上实现了Splice，并使用几个具有真实跟踪的应用程序广泛评估了其有效性。我们的实验表明，通过自动混合，与基于VM的资源采购方案相比，拼接能够将SLO违规减少31%，同时将成本最低化高达32%。
With the rapid growth of users adopting public clouds to run their applications, the types of resources procured from the different public cloud resource offerings are critical in simultaneously achieving satisfactory performance and reducing deployment costs. Typically, no one resource type can meet all application requirements, and thus combining different resource offerings is known to considerably reduce the performance-cost problem. However, it is non-trivial to use blended resources, due to the manual overhead of designing and implementing such blended approaches. Specifically, it necessitates rewriting the application code to suit a given resource and scaling it on demand. In order to overcome this manual hurdle, we take the first step by proposing Splice, an automated framework for cost-and performance-aware blending of IaaS and FaaS services. The three major goals of Splice are: (1) while cost-saving opportunities exist from blending resources, we aim to largely automate the blending process for public cloud services through a compiler-driven approach; (2) more specifically, we focus on automated blending of VMs and serverless functions; and (3) for serverless applications which contain multiple chained functions, we unearth the potential choices in determining a portion of the services to be blended cost-efficiently. We implement Splice on Amazon Web Services (AWS) using an Abstract Syntax Tree (AST), and extensively evaluate its effectiveness using several ap-plications with real-world traces. Our experiments demonstrate that, through automated blending, Splice is able to reduce SLO violations by 31 % compared to VM - based resource procurement schemes, while simultaneously minimizing costs by up to 32 %.