دانلود رایگان مقاله تأمین منابع کیفیت خدمات (QoS) -محور برای پردازش گراف بزرگ مقیاس در محیط های محاسبات ابری – سال 2019
مشخصات مقاله:
عنوان فارسی مقاله:
تأمین منابع کیفیت خدمات (QoS) -محور برای پردازش گراف بزرگ مقیاس در محیط های محاسبات ابری: پردازش گراف به عنوان یک سرویس(GPaaS)
عنوان انگلیسی مقاله:
Quality of Service (QoS)-driven resource provisioning for large-scale graph processing in cloud computing environments: Graph Processing-as-a-Service (GPaaS)
کلمات کلیدی مقاله:
پردازش گراف، محاسبات ابری، کیفیت خدمات، تامین منابع
مناسب برای رشته های دانشگاهی زیر:
مهندسی کامپیوتر – مهندسی فناوری اطلاعات
مناسب برای گرایش های دانشگاهی زیر:
الگوریتم و محاسبات – مهندسی نرم افزار – اینترنت و شبکه های گسترده
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Outline
Highlights
Abstract
Keywords
۱٫ Introduction
۲٫ Related work
۳٫ Overview of the proposed solution
۴٫ Dynamic scalable resource provisioning
۵٫ Performance evaluation
۶٫ Conclusions and future work
References
قسمتی از مقاله انگلیسی:
Abstract
Large-scale graph data is being generated every day through applications and services such as social networks, Internet of Things (IoT) and mobile applications. Traditional processing approaches such as MapReduce are inefficient for processing graph datasets. To overcome this limitation, several exclusive graph processing frameworks have been developed since 2010. However, despite broad accessibility of cloud computing paradigm and its useful features namely as elasticity and pay-as-you-go pricing model, most frameworks are designed for high performance computing infrastructure (HPC). There are few graph processing systems that are developed for cloud environments but similar to their other counterparts, they also try to improve the performance by implementing new computation or communication techniques. In this paper, for the first time, we introduce the large-scale graph processing-as-a-service (GPaaS). GPaaS considers service level agreement (SLA) requirements and quality of service (QoS) for provisioning appropriate combination of resources in order to minimize the monetary cost of the operation. It also reduces the execution time compared to other graph processing frameworks such as Giraph up to 10%–۱۵%. We show that our service significantly reduces the monetary cost by more than 40% compared to Giraph or other frameworks such as PowerGraph.
1. Introduction
Today data is an asset and being able to collect, store, analyze, protect and use this big data provides companies with critical advantages. Every second huge amount of data is being created by various applications such as social networks, Internet of things (IoT), mobile Apps, bloggers, and even smart web robots that are using artificial intelligent (AI) to produce news. According to [1], during each minute at 2017, 3.3 million posts were put on Facebook, 3.8 million queries were searched on Google search engine, 500 hours of new videos were uploaded on YouTube and 448.800 tweets were shared on Twitter. These numbers are almost doubled compared to the amount of content was made per minute in 2014. Moreover, a big fraction of generated data is in the form of graphs. Graph-shape data encompasses a set of vertices that are connected to each other via a set of edges. In a typical social network website, users are vertices and friendship relationships between users form the edges of the graph while in an IoT environment, sensors are considered as vertices and the connections between sensors shape the edges.
