Computing and Information Systems - Theses
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ItemCost-efficient resource provisioning for large-scale graph processing systems in cloud computing environments( 2018)A large amount of data that is being generated on Internet every day is in the form of graphs. Many services and applications namely as social networks, Internet of Things (IoT), mobile applications, business applications, etc. in which every data entity can be considered as a vertex and the relationships between entities shape the edges of a graph, are in this category. Since 2010, exclusive large-scale graph processing frameworks are being developed to overcome the inefficiency of traditional processing solutions such as MapReduce. However, most frameworks are designed to be employed on high performance computing (HPC) clusters which are only available to whom can afford such infrastructure. Cloud computing is a new computing paradigm that offers unprecedented features such as scalability, elasticity and pay-as-you-go billing model and is accessible to everyone. Nevertheless, the advantages that cloud computing can bring to the architecture of large-scale graph processing systems are less studied. Resource provisioning and management is a critical part of any processing system in cloud environments. To provide the optimized amount of resources for a particular operation, several factors such as monetary cost, throughput, scalability, network performance, etc. can be taken into consideration. In this thesis, we investigate and propose novel solutions and algorithms for cost-efficient resource provisioning for large-scale graph processing systems. The outcome is a series of research works that increase the performance of such processing by making it aware of the operating environment while decreasing the dollar cost significantly. We have particularly made the following contributions: 1. We introduced iGiraph, a cost-efficient framework for processing large-scale graphs on public clouds. iGiraph also provides a new graph algorithm categorization and processes the graph accordingly. 2. To demonstrate the impact of network on the processing in cloud environment, we developed two network-aware algorithms that utilize network factors such as traffic, bandwidth and also the computation power. 3. We developed an auto-scaling technique to take advantage of resource heterogeneity on clouds. 4. We introduced a large-scale graph processing service for clouds where we consider the service level agreement (SLA) requirements in the operations. The service can handle multiple processing requests by its new prioritization and provisioning approach.