3,170 research outputs found

    Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

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    In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society

    Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments

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    This chapter presents software architectures of the big data processing platforms. It will provide an in-depth knowledge on resource management techniques involved while deploying big data processing systems on cloud environment. It starts from the very basics and gradually introduce the core components of resource management which we have divided in multiple layers. It covers the state-of-art practices and researches done in SLA-based resource management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
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