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Cloud Broker and Cloudlet for Workflow Scheduling

Authors: Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Publisher: Springer Singapore

Book Series : KAIST Research Series

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book blends the principles of cloud computing theory and discussion of emerging technologies in cloud broker systems, enabling users to realise the potential of an integrated broker system for scientific applications and the Internet of Things (IoT).

Due to dynamic situations in user demand and cloud resource status, scalability has become crucial in the execution of complex scientific applications. Therefore, data analysts and computer scientists must grasp workflow management issues in order to better understand the characteristics of cloud resources, allocate these resources more efficiently and make critical decisions intelligently. Thus, this book addresses these issues through discussion of some novel approaches and engineering issues in cloud broker systems and cloudlets for workflow scheduling. This book closes the gaps between cloud programmers and scientific applications designers, describing the fundamentals of cloud broker system technology and the state-of-the-art applications in implementation and performance evaluation.

The books gives details of scheduling structures and processes, providing guidance and inspiration for users including cloud programmers, application designers and decision makers with involvement in cloud resource management.

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Table of Contents

Frontmatter

Chapter 1. Integrated Cloud Broker System and Its Experimental Evaluation

Abstract

In distributed computing environment, there are a large number of similar or equivalent resources provided by different service providers. These resources may provide the same functionality, but optimize different Quality of Service (QoS) metrics. These computing resources are managed and sold by many different service providers [1]. Service providers offer necessary information about their services such as the service capability, and the utility measuring methods and charging policies, which will be later referred to as the “resource policy” in this book. Each resource policy bears a tuple of two components, such as (capability, price). For capability, we model the resource capability as a set of QoS metrics which include the CPU type, the memory size, and the storage/hard disk size.

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 2. VM Placement via Resource Brokers in a Cloud Datacenter

Abstract

Resource management in cloud datacenters is one of the most important issues for cloud service providers because it directly affects their profit. Energy and performance guarantee are two major concern of it. In energy aspect, the total estimated energy bill of datacenters is $11.5 billion and their energy bills double every five years [1, 2] Also, in performance guarantee aspect, many researches insist that performance metrics such as throughput and response time should be considered as well as availability in IaaS SLA [3, 4].

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 3. Cost Adaptive Workflow Resource Broker in Cloud

Abstract

As scientific applications become more complex, the management of resources that perform the workflow jobs has become one of the challenging issues

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 4. A Cloud Broker System for Connected Car Services with an Integrated Simulation Framework

Abstract

At present, the mobile market accounts for the largest portion in IT industry, and its proportion is increasing rapidly. With the rapid increase, mobile services are also becoming bigger and more complex. Therefore, with the development of network technology such as 5G, there exist on-going research on mobile services that follows client-server models capable of overcoming the limitations of computational performance and storage in mobile devices.

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 5. Mobile Device as Cloud Broker for Computation Offloading at Cloudlets

Abstract

With the development of cloud computing and the evergrowing number of mobile devices, many applications require higher user’s quality of experience (QoE).

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 6. Opportunistic Task Scheduling Over Co-located Clouds

Abstract

Nowadays, due to the explosive increase of mobile devices and data traffic, various innovative technologies have been developed to transfer data more efficiently by the use of large quantities of mobile devices connected with each other. However, as mobile devices have limitations in terms of computing power, memory, storage, communications and battery capacity, the computation-intensive tasks are hard to be handled locally.

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 7. Mobility-Aware Resource Scheduling Cloudlets in Mobile Environment

Abstract

The ever-growing number of smart phones is producing explosive amounts of traffic in order to support a wide plethora of multimedia services. A recent Cisco report estimates that global mobile traffic will exceed 24.3 exabytes monthly in 2019.

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Chapter 8. Machine-Learning Based Approaches for Cloud Brokering

Abstract

Machine learning is a field of computer science specifically aimed at a challenging goal, quite clearly illustrated by Samuel in 1959, stating that machine learning is that discipline that “gives computers the ability to learn without being explicitly programmed” [1].

Chan-Hyun Youn, Min Chen, Patrizio Dazzi

Title: Cloud Broker and Cloudlet for Workflow Scheduling
Authors: Chan-Hyun Youn
Min Chen
Patrizio Dazzi
Copyright Year: 2017
Publisher: Springer Singapore
Electronic ISBN: 978-981-10-5071-8
Print ISBN: 978-981-10-5070-1
DOI: https://doi.org/10.1007/978-981-10-5071-8

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