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

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

Hybrid workflow scheduling on edge cloud computing systems

Internet of Things applications can be represented as workflows in which stream and batch processing are combined to accomplish data analytics objectives in many application domains such as smart home, health care, bioinformatics, astronomy, and education. The main challenge of this combination is the differentiation of service quality constraints between batch and stream computations. Stream processing is highly latency-sensitive while batch processing is more likely resource-intensive. In this work, we propose an end-to-end hybrid workflow scheduling on an edge cloud system as a two-stage framework. In the first stage, we propose a resource estimation algorithm based on a linear optimization approach, gradient descent search (GDS), and in the second stage, we propose a cluster-based provisioning and scheduling technique for hybrid workflows on heterogeneous edge cloud resources. We provide a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrate the framework performance in controlling the execution of hybrid workflows by efficiently tuning several parameters including stream arrival rate, processing throughput, and workflow complexity. In comparison to a meta-heuristics technique using Particle Swarm Optimization (PSO), the proposed scheduler provides significant improvement for large-scale hybrid workflows in terms of execution time and cost with an average of 8% and 35%, respectively

Cloud resource provisioning for combined stream and batch workflows

The increasing adoption of Internet of Thing (IoT) technology in many application domains generates a new need for rationalized utilization of computing resources supporting such computations. IoT applications can be represented as workflows in which stream and batch applications are integrated to accomplish data analytics objectives in many application domains such as smart home, health care, bioinformatics, astronomy, education, etc. The main challenge of this combination is the differentiation of service quality constraints between the two computation paradigms. Stream processing is highly sensitive to real-time constraint while batch processes are usually resource-intensive. In this work we propose a resource provisioning framework for combined workflows which aims to find an optimal workflow configuration plan to minimize execution time and monetary cost. The framework has functions of execution plan generation, task clustering, and resource provisioning. Results show that framework is capable to control the execution of combined-workflows by efficient tunning several parameters including stream arrival rate and processing throughput

Hybrid workflow provisioning and scheduling on cooperative edge cloud computing

The dramatic growth of IoT-based applications in many domains such as real-time monitoring, interactive reporting, and smart manufacturing brings challenges for adoption of cloud-based solutions for integration of latency-sensitive and resource-intensive applications. We refer to this integration as a hybrid-workflow. This paper provides a resource estimation and task scheduling framework to run hybrid workflows on edge and cloud computing systems. We propose an adaptive resource estimation technique with an online gradient descent approximation to handle the complexity of hybrid workflows. In addition, a scheduling technique to execute workflow tasks on a cooperative edge cloud system to resolve the issues of latencysensitive application as well as to improve resource utilization at the edge layer is proposed. Experimental results show the capability of the cooperative model in reducing the time and cost of running complex and large scale hybrid workflows

Hybrid workflow provisioning and scheduling on edge cloud computing using a gradient descent search approach

The dramatic growth of the Internet of Things (IoT) technology in many application domains, ranging from intelligent video surveillance, smart retail to the Internet-of-Vehicles brings new computation challenges for rationalized utilization of computing resources. IoT application execution refers to hybrid processing model of stream and batch to achieve data analytics objectives. Hybrid workflow execution combines the challenges of latency-sensitive and resource-intensive processing. To resolve these challenges, we proposed a two stages hybrid workflow scheduling framework on edge cloud computing. In the first stage, we proposed a resource estimation algorithm based on a linear optimization approach, the gradient descent search (GDS) and in the second stage, we adopted a cluster-based provisioning and scheduling technique on heterogeneous edge cloud resources. This work provides a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrated the framework performance in controlling the execution of hybrid workflows by an efficient tuning for stream processing parameters, such as arrival rate and processing throughput. Under working constraints, the proposed scheduler provides significant improvement for large hybrid workflows in terms of execution time and monetary cost with an average of 8% and 35%, respectively

Cloud resource provisioning for combined stream and batch workflows

The increasing adoption of Internet of Thing (IoT) technology in many application domains generates a new need for rationalized utilization of computing resources supporting such computations. IoT applications can be represented as workflows in which stream and batch applications are integrated to accomplish data analytics objectives in many application domains such as smart home, health care, bioinformatics, astronomy, education, etc. The main challenge of this combination is the differentiation of service quality constraints between the two computation paradigms. Stream processing is highly sensitive to real-time constraint while batch processes are usually resource-intensive. In this work we propose a resource provisioning framework for combined workflows which aims to find an optimal workflow configuration plan to minimize execution time and monetary cost. The framework has functions of execution plan generation, task clustering, and resource provisioning. Results show that framework is capable to control the execution of combined-workflows by efficient tunning several parameters including stream arrival rate and processing throughput