Abstract. Cloud service brokerage has been identified as a key concern for future cloud technology development and research. We compare service brokerage solutions. A range of specific concerns like architecture, programming and quality will be looked at. We apply a 2-pronged classification and comparison framework. We will identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We will discuss complex cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.
Abstract-Cloud service brokerage and related management and marketplace concepts have been identified as key concerns for future cloud technology development and research. Cloud service management is an important building block of cloud architectures that can be extended to act as a broker service layer between consumers and providers, and even to form marketplace services. We present a 3-pronged classification and comparison framework for broker platforms and applications. A range of specific broker development concerns like architecture, programming and quality are investigated. Based on this framework, selected management, brokerage and marketplace solutions will be compared, not only to demonstrate the utility of the framework, but also to identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We also discuss emerging cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.
Abstract. Scalability is a significant feature of cloud computing, which addresses to increase or decrease the capacities of allocated virtual resources at application, platform, database and infrastructure level on demand. We investigate scalable architecture solutions for cloud PaaS that allow services to utilize the resources dynamically and effectively without directly affecting users. We have implemented scalable architectures with different session state management solutions, deploying an online shopping cart application in a PaaS solution, and measuring the performance and cost under three server-side session state providers: Caching, SQL database and NoSQL database. A commercial solution with its supporting state management components has been used. Particularly when re-architecting software for the cloud, the trade-off between performance, scalability and cost implications needs to be discussed.
To achieve high levels of reliability, availability and performance in cloud environments, a fault tolerance approach to handle failures effectively is needed. In most existing research, the primary focus has been on explicit specification-driven solutions which requires too much effort for application developers, and leads to inflexibility. We propose a fuzzy job distributor (load balancer) for fault tolerance management to reduce levels of management complexity for the user. The proposed approach aims to reduce the possibility of fault occurrences in the system by a fair distribution of user job requests among available resources. In our self-adaptive approach, the system manages anomalous situations that might lead to failure by distributing the incoming job request based on the reliability of processing nodes, i.e., virtual machines (VMs). The reliability of VMs is a variable parameter and changes during its lifetime. Our approach is implemented and comparatively analysed using OpenStack. The experimental results show a significant reduction in the occurrence of faults in comparison with other load balancing algorithms.
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