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Fog integrated Cloud Computing is a distributed computing paradigm where near-user end devices known as fog nodes cooperate with cloud resources hosted at distant datacentres for providing computational and storage services to end user applications. One of the most challenging issues in fog integrated cloud based system is task scheduling. Most of the existing scheduling approaches involve centralized decision making which fail to exploit the advantages that may be achieved by a decentralized approach, that directly maps with the distributed architecture of fog based systems. This work proposes a decentralized heuristic algorithm for scheduling real-time IoT applications bounded by tolerable latency as the Quality of Service (QoS) constraint. The proposed technique aims to take into consideration the resource constraints of the fog resources to yield a schedule that not only meets the QoS requirements defined in terms of tolerable latency but also improves the response time of applications hosted on a fog-cloud infrastructure. Performance evaluation on different IoT applications indicate that the presented algorithm delivers better performance by reducing response time by 11% on an average in comparison to the other state-of-the-art policies. [ABSTRACT FROM AUTHOR]

Rise of Big Data techniques has led to the requirement for low latency analysis of high-velocity continuous data streams in real time. Several solutions, including Stream Processing Systems (SPSs), have been developed to enable real-time distributed stream processing. However, emerging application scenarios such as smart cities and wearable assistance that involve highly variable data rates keep on posing new challenges to the established stream processing engines for maintaining cost-effective executions. To cater to such scenarios, many modern SPSs have been proposed that leverage Cloud environment. The run-time scalability incorporated in these SPSs is in their early adaptations and are based on fixed scale sizes. Moreover, these scaling approaches do not adequately consider both the structure of the hosted streaming applications and the characteristic features of the underlying Cloud environment. Achieving true cost benefits of orchestrating streaming applications on Cloud-based pay-as-you-go model while maintaining the desired QoS, necessitates that both these issues are accounted in making the scaling decisions. This work presents a heterogeneity-aware, efficient auto-scaling strategy StreamScale-H which addresses both these issues. Simulation experiments, on representative stream applications, indicate that the proposed StreamScale-H auto-scaling algorithm exhibits much better performance in comparison with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing is a promising utility-based distributed computing environment in which resources (hardware/software) are offered as a service over the Internet on a pay per use basis. It involves elastic resource provisioning capabilities and hence charge only for those Cloud resources that are actually needed. However, true elasticity and cost-effectiveness in the pay-per-use Cloud business model has not been achieved yet. Most of the auto-scaling techniques in Cloud allow horizontal scaling by adding duplicate instances. They do not fully consider both; users' expected performance and pricing of the services. Since, Cloud platforms offer a plethora of server configurations at different pricing, the ability to scale by varied size instances can provide greater elasticity and hence significant improvements in cost and resource utilization. In this work, a heterogeneity-aware auto scaling algorithm that adapts to the workload changes while maintaining the Quality of Service (QoS) is proposed. The proposed method uses online profiling of Cloud resources and workload history to analytically estimate subsequent resource requirements. It then employs a heuristic to provision the smallest set of different size resource configurations in order to meet the QoS targets at reduced cost and improved resource utilization. Simulation based experimental evaluation on representative web and scientific workload patterns indicate that the proposed auto-scaling technique exhibits better performance as compared to the other contemporary horizontal scaling approaches. [ABSTRACT FROM AUTHOR]