Gaofeng Zhang

The Design of Cloud Workflow Systems

Cloud computing is the latest market-oriented computing paradigm which brings software design and development into a new era characterized by XaaS, i.e. everything as a service. Cloud workflows, as typical software applications in the cloud, are composed of a set of partially ordered cloud software services to achieve specific goals. However, due to the low QoS (quality of service) nature of the cloud environment, the design of workflow systems in the cloud becomes a challenging issue for the delivery of high quality cloud workflow applications. To address such an issue, this book presents a systematic investigation to the three critical aspects for the design of a cloud workflow system, viz. system architecture, system functionality and quality of service. Specifically, the system architecture for a cloud workflow system is designed based on the general four-layer cloud architecture, viz. application layer, platform layer, unified resources layer and fabric layer. The system functionality for a cloud workflow system is designed based on the general workflow reference model but with significant extensions to accommodate software services in the cloud. The support of QoS is critical for the quality of cloud workflow applications. This book presents a generic framework to facilitate a unified design and development process for software components that deliver lifecycle support for different QoS requirements. While the general QoS requirements for cloud workflow applications can have many dimensions, this book mainly focuses on three of the most important ones, viz. performance, reliability and security. In this book, the architecture, functionality and QoS management of our SwinDeW-C prototype cloud workflow system are demonstrated in detail as a case study to evaluate our generic design for cloud workflow systems. To conclude, this book offers a general overview of cloud workflow systems and provides comprehensive introductions to the design of the system architecture, system functionality and QoS management.

SwinDeW-C: A Peer-to-Peer Based Cloud Workflow System

Workflow systems are designed to support the process automation of large scale business and scientific applications. In recent years, many workflow systems have been deployed on high performance computing infrastructures such as cluster, peer-to-peer (p2p), and grid computing (Moore, 2004; Wang, Jie, & Chen, 2009; Yang, Liu, Chen, Lignier, & Jin, 2007). One of the driving forces is the increasing demand of large scale instance and data/computation intensive workflow applications (large scale workflow applications for short) which are common in both eBusiness and eScience application areas. Typical examples (will be detailed in Section 13.2.1) include such as the transaction intensive nation-wide insurance claim application process; the data and computation intensive pulsar searching process in Astrophysics. Generally speaking, instance intensive applications are those processes which need to be executed for a large number of times sequentially within a very short period or concurrently with a large number of instances (Liu, Chen, Yang, & Jin, 2008; Liu et al., 2010; Yang et al., 2008). Therefore, large scale workflow applications normally require the support of high performance computing infrastructures (e.g. advanced CPU units, large memory space and high speed network), especially when workflow activities are of data and computation intensive themselves. In the real world, to accommodate such a request, expensive computing infrastructures including such as supercomputers and data servers are bought, installed, integrated and maintained with huge cost by system users

A Generic QoS Framework for Cloud Workflow Systems

Due to the dynamic nature of cloud computing, how to achieve satisfactory QoS (Quality of Service) in cloud workflow systems becomes a challenge. Meanwhile, since QoS requirements have many dimensions, a unified system design for different QoS management components is required to reduce the system complexity and software development cost. Therefore, this paper proposes a generic QoS framework for cloud workflow systems. Covering the major stages of a workflow lifecycle, the framework consists of four components, viz. QoS requirement specification, QoS-aware service selection, QoS consistency monitoring and QoS violation handling. While there are many QoS dimensions, this paper illustrates a concrete performance framework as a case study and briefly touches others. We also demonstrate the system implementation and evaluate the effectiveness of the performance framework in our cloud workflow system.

An association probability based noise generation strategy for privacy protection in cloud computing

Cloud computing promises an open environment where customers can deploy IT services in pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers can exist. Such service providers may record service requests from a customer and then collectively deduce the customer private information. Therefore, customers need to take certain actions to protect their privacy. Obfuscation with noise injection, that mixes noise service requests with real customer service requests so that service providers will be confused about which requests are real ones, is an effective approach in this regard if those request occurrence probabilities are about the same. However, current obfuscation with noise injection uses random noise requests. Due to the randomness it needs a large number of noise requests to hide the real ones so that all of their occurrence probabilities are about the same, i.e. service providers would be confused. In pay-as-you-go cloud environment, a noise request will cost the same as a real request. Hence, with the same level of confusion, i.e. customer privacy protection, the number of noise requests should be kept as few as possible. Therefore in this paper we develop a novel historical probability based noise generation strategy. Our strategy generates noise requests based on their historical occurrence probability so that all requests including noise and real ones can reach about the same occurrence probability, and then service providers would not be able to distinguish in between. Our strategy can significantly reduce the number of noise requests over the random strategy, by more than 90% as demonstrated by simulation evaluation.

Key Research Issues for Privacy Protection and Preservation in Cloud Computing

Cloud computing promises an open and promising environment where customers or users can utilise and deploy IT services in a pay-as-you-go style while saving huge capital investments on their own IT infrastructure. The openness and virtualisation features in cloud environments make privacy protection and preservation be a challenging issue. Currently, in existing privacy protection and preservation fields, many approaches and methods have been investigated and presented to withstand different kinds of attackers and risks. On the basis of this, many researchers start to consider these in cloud environments. But current work is still at the early stage. Therefore, a systematic investigation and an overall classification of key issues in cloud privacy protection and preservation are necessary to keep current research on the right track while reducing unnecessary work as much as possible. Hence, in this paper, we investigate and classify various privacy issues in cloud environments. Especially, we focus on some key areas of cloud privacy protection and preservation from the perspective of cloud roles and cloud service levels. This paper can help to provide an overall picture of cloud privacy protection and preservation and point out potential key areas in cloud privacy protection and preservation.

A Privacy-Leakage-Tolerance Based Noise Enhancing Strategy for Privacy Protection in Cloud Computing

Cloud computing promises a service-oriented environment where customers can utilise IT services in a pay-as-you-go fashion while saving huge capital investments on their own IT infrastructures. Due to the openness, malicious service providers may exist in these environments. Some of these service providers could record service data in cloud service processes about a customer and then collectively deduce the customers private information without authorisation. Noise obfuscation is an effective approach in this regard by utilising noise data. For example, it can generate and inject noise service requests into real customer service requests so that service providers are not able to distinguish which ones are real ones. However, existing typical noise obfuscations do not consider the customer-defined privacy-leakage-tolerance in noise obfuscation processes. Specifically, cloud customers could define a boundary of privacy leakage possibility to require noise obfuscation on privacy protection in cloud computing. In other words, under this boundary -- privacy-leakage-tolerance, noise obfuscation could be enhanced by the efficiency improvement on privacy protection, such as reducing noise service requests injected into real ones. So, the customer can obtain a lower cost on noise data in the pay-as-you-go fashion for cloud environments, with a reasonable effectiveness of privacy protection. Therefore, to address this privacy concern, a novel noise enhancing strategy can be presented. We firstly analyse the privacy-leakage-tolerance for cloud customers in terms of noise generation. Then, the creation of a noise generation set can be presented based on the privacy-leakage-tolerance, and the set can guide and enhance existing noise generation strategies by this boundary. Lastly, we present our novel privacy-leakage-tolerance based noise enhancing strategy for privacy protection in cloud computing. The simulation evaluation demonstrates that our strategy can significantly improve the efficiency of privacy protection on existing noise obfuscations in cloud environments.

Workflow Systems in the Cloud

In this chapter, we will present an overview about the background of cloud computing and workflow systems. Specifically, Sect. 1.1 introduces the novel cloud computing paradigm. Section 1.2 reviews the workflow systems, especially in the distributed computing environments. Section 1.3 introduces the cloud workflow systems. In Sect. 1.4, we demonstrate two motivating examples, one for large-scale data and computation intensive e-science application in Astrophysics and one for instance intensive e-business application in the stock market. Finally, Sect. 1.5 points out the key issue in the design of cloud workflow systems.

Cloud Workflow System Quality of Service

Along with system functionality, the management of quality of service (QoS) in cloud workflow system is attracting increasing and even more efforts [3, 31, 45, 47, 54, 73]. This is mainly because of the following two reasons. First, the cloud computing environment is very dynamic and uncertain. Therefore, it is difficult to achieve targeted service quality if without effective QoS management strategies; Second, cloud computing adopts the market-oriented model and strict service contracts. Therefore, high service quality is necessary for improving customer satisfaction and avoiding penalty for the breach of service contracts. Therefore, QoS management plays a significant role in cloud workflow systems, and hence included as significant part of this book. In Sect. 4.1, we will first present an overview about the QoS of Web and cloud services. In Sect. 4.2, we introduce the QoS of cloud workflows. In Sect. 4.3, a generic QoS framework is presented as a high level guideline for the design of software components to deliver lifecycle QoS support in cloud workflow systems. Afterwards, as concrete examples, specific strategies for performance management (on workflow response time), cost management (on data storage), reliability management (on data replication), and security management, will be discussed and demonstrated.

Case Study: SwinDeW-C Cloud Workflow System

The previous chapters have given a general overview of cloud workflow system architecture, functionality and quality of service. In this chapter, we will demonstrate our SwinDeW-C cloud workflow system as a concrete case study to illustrate the design and development of a cloud workflow system for running large scale workflow applications.

Cloud Workflow System Functionality

In this chapter, we will present the cloud workflow system functionality. In Sect. 3.1, we will first introduce the classical workflow reference model which defines the basic functionalities for a workflow system. In Sect. 3.2, we will then illustrate those system functionalities which are typical for the running of workflows in the cloud computing environment.