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This timely text/reference presents a comprehensive review of the workflow scheduling algorithms and approaches that are rapidly becoming essential for a range of software applications, due to their ability to efficiently leverage diverse and distributed cloud resources. Particular emphasis is placed on how workflow-based automation in software-defined cloud centers and hybrid IT systems can significantly enhance resource utilization and optimize energy efficiency.

Topics and features: describes dynamic workflow and task scheduling techniques that work across multiple (on-premise and off-premise) clouds; presents simulation-based case studies, and details of real-time test bed-based implementations; offers analyses and comparisons of a broad selection of static and dynamic workflow algorithms; examines the considerations for the main parameters in projects limited by budget and time constraints; covers workflow management systems, workflow modeling and simulation techniques, and machine learning approaches for predictive workflow analytics.

This must-read work provides invaluable practical insights from three subject matter experts in the cloud paradigm, which will empower IT practitioners and industry professionals in their daily assignments. Researchers and students interested in next-generation software-defined cloud environments will also greatly benefit from the material in the book.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Stepping into the Digital Intelligence Era

Abstract
There are several noteworthy implications of the digitization movement. A myriad of digitation-enabling processes, platforms, products, patterns, and practices are hitting the market. There is a kind of convergence blurring the boundaries of physical and virtual worlds. Further on, all kinds of physical, mechanical, electrical, and electronics systems are adequately empowered to be instrumented and interconnected to exhibit intelligent behavior. Further on, all kinds of connected devices and digitized objects in our everyday environments are systematically integrated with remotely held and cloud-hosted applications, services, and data sources in order to be adaptive in their deeds, decisions, and deliveries.
The faster maturity and stability of edge technologies such as bar codes, application-specific chips, microcontrollers, stickers, labels, tags, smart dust, specks, sensors, actuators, LED lights, etc. speeds up the production of digitized objects, alternatively termed as smart or sentient materials. Every common, casual, and cheap item can be transitioned into digitized entities and elements in order to join in the mainstream computing. Next in line is that the explosion of instrumented and connected devices that are very generic and specific. That is, there are portable, wearable, wireless, mobile, implantable, hearable, and nomadic devices in plenty. Now with the emergence of microservices architecture (MSA), all kinds of enterprise-scale, operational, analytical, and transactional applications are being designed and developed using the MSA pattern. Thus scores of ground-level digitized elements in conjunction with cyber/virtual applications being run on cloud platforms and infrastructures strengthen the digitization era. This chapter is to tell all about the digitization-inspired possibilities and opportunities and how software-defined cloud centers are the best fit for digital applications.
In this chapter, we are to describe some of the impactful developments brewing in the IT space, how the tremendous amount of data getting produced and processed all over the world is to impact the IT and business domains, and how next-generation IT infrastructures are accordingly getting refactored, remedied, and readied for the impending big data-induced challenges, how likely the move of the big data analytics discipline toward fulfilling the digital universe requirements of extracting and extrapolating actionable insights for the knowledge-parched is, and finally for the establishment and sustenance of the dreamt smarter planet.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 2. Demystifying the Traits of Software-Defined Cloud Environments (SDCEs)

Abstract
Definitely the cloud journey is on the fast track. The cloud idea got originated and started to thrive from the days of server virtualization. Server machines are being virtualized in order to have multiple virtual machines, which are provisioned dynamically and kept in ready and steady state to deliver sufficient resources (compute, storage, and network) for optimally running any software application. That is, a physical machine can be empowered to run multiple and different applications through the aspect of virtualization. Resultantly, the utilization of expensive compute machines is steadily going up.
This chapter details and describes the nitty-gritty of next-generation cloud centers. The motivations, the key advantages, and the enabling tools and engines along with other relevant details are being neatly illustrated there. An SDCE is an additional abstraction layer that ultimately defines a complete data center. This software layer presents the resources of the data center as pools of virtual and physical resources to host and deliver software applications. A modern SDCE is nimble and supple as per the vagaries of business movements. SECE is, therefore, a collection of virtualized IT resources that can be scaled up or down as required and can be deployed as needed in a number of distinct ways. There are three key components making up SDCEs:
1.
Software-defined computing
 
2.
Software-defined networking
 
3.
Software-defined storage
 
The trait of software enablement of different hardware systems has pervaded into other domains so that we hear and read about software-defined protection, security, etc. There are several useful links in the portal (Sang-Woo et al. “Scalable multi-access flash store for big data analytics” FPGA’14, Monterey, CA, USA, February 26–28, 2014) pointing to a number of resources on the software-defined cloud environments.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 3. Workflow Management Systems

Abstract
Today’s scientific application requires tremendous amount of computation-driven as well as data-driven supported resources. The scientific applications are represented as workflows. The workflow management systems are designed and developed to depict the workflows of complex nature. The workflow management systems are able to reliably and efficiently coordinate among various resources in a distributed environment. This chapter describes various workflow management software like Kepler, Taverna, Triana, Pegasus, and Askalon. The architecture and functionalities of these workflow management systems are explained in the following sections.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 4. Workflow Scheduling Algorithms and Approaches

Abstract
Cloud infrastructures typically offer access to boundless virtual resources dynamically provisioned on demand for hosting, running, and managing a variety of mission-critical applications like scientific workflows, big data processing application, business intelligence-based applications, high-performance computing (HTC), and high transaction computing (HTC). Due to the surging popularity of the irresistible cloud idea, there are cloud datacenters spreading across the globe comprising heterogeneous cloud platforms and infrastructures catering to fast-evolving demands of worldwide businesses. The pervasive connectivity has enabled for the unprecedented success of the cloud concept. However, intensive automation is the key to the originally intended success of the cloud paradigm. Researchers across the world are focusing on unearthing powerful and pioneering tools and techniques for automated infrastructure life-cycle management. Similarly there are pathbreaking work-around approaches, algorithms, and architectures for workload consolidation. In short, there are many cloud-related aspects yearning for technologically sound automation, acceleration, and augmentation capabilities.
Efficient scheduling algorithms become mandatory for automated operations of distributed and disparate cloud resources and workloads. The resource scheduling is a dynamic problem, and it is associated with on-demand resource provisioning, fault tolerance support, and hybrid resource scheduling with appropriate Quality of Service, considering time, cost, and budget. This chapter provides the details about various automated solutions for workflow scheduling and also comprehensive survey of various existing workflow scheduling algorithms in the cloud computing environment.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 5. Workflow Modeling and Simulation Techniques

Abstract
Modeling and simulation of scientific workflow play a vital role in resource allocation in a distributed environment. Simulation is one of the methods to solve the complex scientific workflows in distributed environment. There are many scientific workflow simulation software frameworks that are available for grid and cloud environment. WorkflowSim is an open-source simulator. WorkflowSim Simulator extends the existing CloudSim Simulator. The architecture, components, and scheduling algorithms used and also the simulation results are explained for CloudSim Simulator and WorkflowSim Simulator.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 6. Execution of Workflow Scheduling in Cloud Middleware

Abstract
Many scientific applications are often modeled as workflows. The data and computational resource requirements are high for such workflow applications. Cloud provides a better solution to this problem by offering the promising environment for the execution of these workflow. As it involves tremendous data computations and resources, there is a need to automate the entire process. Workflow management system serves this purpose by orchestrating workflow task and executing it on distributed resources. Pegasus is a well-known workflow management system that has been widely used in large-scale e-applications. This chapter provides an overview about the Pegasus Workflow Management System, describes the environmental setup with OpenStack and creation and execution of workflows in Pegasus, and discusses about the workflow scheduling in cloud with its issues.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 7. Workflow Predictions Through Operational Analytics and Machine Learning

Abstract
Workflow execution employs predictive analytics to extract significant, unidentified as well as precious insights from several stages of execution. Further, the operational analytics integrates these valuable insights directly into decision engine which enables analytical as well as machine learning-driven decision-making for an efficient workflow execution. This chapter highlights several analytical and machine learning approaches that are practiced in workflow predictions. Additionally, it explains the significance of hybrid approach which includes both analytical and machine learning models for workflow prediction. Finally, it describes the hybrid approach employed in PANORAMA architecture using two workflow applications.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 8. Workflow Integration and Orchestration, Opportunities and the Challenges

Abstract
Workflow orchestration is a method which smartly organizes the enterprise function with application, data, and infrastructure. The applications as well as their infrastructure can be dynamically scaled up or down using orchestration. On the contrary, integration enables the development of new applications with the capability to connect to any other application through specified interfaces. In this chapter, firstly, the opportunities and challenges in workflow orchestration and integration are explained. Following that, BioCloud, an architecture that demonstrates the task-based workflow orchestration using two bioinformatics workflows is explained in detail.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 9. Workload Consolidation Through Automated Workload Scheduling

Abstract
Workload consolidation is an approach to enhance the server utilization by grouping the VMs that are executing workflow tasks over multiple servers based on their server utilization. The primary objective is to optimally allocate the number of servers for executing the workflows which in turn minimize the cost and energy of data centers. This chapter consolidates the cost- and energy-aware workload consolidation approaches along with the tools and methodologies used in modern cloud data centers.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 10. Automated Optimization Methods for Workflow Execution

Abstract
Workflow optimization is an approach to enhance the speed, robustness, and compactness of workflows by exploiting their structure, runtime, and output. This chapter initially highlights the significance of workflow optimization along with different possible levels of optimization. Further, it outlines the Taverna optimization framework over single and distributed infrastructure together with the optimization plug-ins that are validated using two scientific workflow executions.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Chapter 11. The Hybrid IT, the Characteristics and Capabilities

Abstract
With the faster adoption of the cloud idea across industry verticals with all the elegance and the enthusiasm, the traditional IT is bound to enlarge its prospects and potentials. Thatis, the IT capabilities and capacities are being enhanced with the seamless and spontaneous association with the cloud paradigm in order to meet up fast-emerging and evolving business requirements. This is a kind of new IT getting enormous attention and garnering a lot of attraction among business executives and IT professionals lately. The systematic amalgamation of the cloud concepts with the time-tested and trusted enterprise IT environment is to deliver a bevy of significant advantages for business houses in the days ahead. This model of next-generation computing through the cognitive and collective leverage of enterprise and cloud IT environments is being touted as the hybrid IT. There are a variety of technologies and tools expressly enabling the faster realization of hybrid era. This chapter is specially crafted for digging deep and describing the various implications of the hybrid IT.
G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Backmatter

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