The Contemporary Affirmation of Taxonomy and Recent Literature on Workflow Scheduling and Management in Cloud Computing

The Cloud computing systemspreferred over the traditional forms of computing such as grid computing, utility computing, autonomic computing is attributed forits ease of access to computing, for its QoS preferences, SLA2019;s conformity, security and performance offered with minimal supervision. A cloud workflow schedule when designed efficiently achieves optimalre source sage, balance of workloads, deadline specific execution, cost control according to budget specifications, efficient consumption of energy etc. to meet the performance requirements of today2019; svast scientific and business requirements. The businesses requirements under recent technologies like pervasive computing are motivating the technology of cloud computing for further advancements. In this paper we discuss some of the important literature published on cloud workflow scheduling

A Reference Architecture for Provisioning of Tools as a Service: Meta-Model, Ontologies and Design Elements

Abstract not availableMuhammad Aufeef Chauhan, Muhammad Ali Babar, Quan Z. Shen

Supporting Collaborative Business Processes: a BPaaS Approach.

Collaborative business processes are increasingly driven by business flexibility and agility. Cloud-based business process management services have provided small medium enterprises (SMEs) with a pay-per-use manner for their daily business needs, i.e. some simple business process applications, e.g. salesforce provides cloud-based CRM to boost SMEs' sales. This raises the question how cloud-based business process management solutions can support the fast pace of change of business collaborations among business partners? For example, collaborative processes for managing industrial incidents are short term, low frequency processes. This paper proposes an architecture meta-model, which is used to design the concrete architecture and to further analyse the performance of the proposed solution. A real world case of collaborative processes for incident and maintenance notifi cations is used to explain the design and implementation of the cloud-based solution for supporting collaborative business processes. Service improvement of the new solution and computing power costs are analysed accordingly

A service-oriented cloud modeling method and process

The transition of software development from web to cloud has been accelerated. The development of cloud services requires a modeling method that reflects the characteristics of cloud including personalized service, resource sharing service, grouped and distributed services, and cross-platform operability. This study aimed to suggest a method of developing UML-based cloud services suitable for the characteristics of cloud services. A cloud service metamodel was defined using cloud applications’ characteristic modeling elements, and after that, how these cloud modeling elements are expressed into UML modeling elements was defined with an integrated metamodel between cloud and UML. By applying this hierarchical cloud metamodel, an MDA and MVC-based service-oriented cloud modeling process was established. By doing so, it will be possible to easily design services (applications) and solutions that are suitable for cloud computing environments, and in particular, to create hierarchical reuse models by the level of the abstraction of model-driven development

Big SaaS: The Next Step Beyond Big Data

Software-as-a-Service (SaaS) is a model of cloud computing in which software functions are delivered to the users as services. The past few years have witnessed its global flourishing. In the foreseeable future, SaaS applications will integrate with the Internet of Things, Mobile Computing, Big Data, Wireless Sensor Networks, and many other computing and communication technologies to deliver customizable intelligent services to a vast population. This will give rise to an era of what we call Big SaaS systems of unprecedented complexity and scale. They will have huge numbers of tenants/users interrelated in complex ways. The code will be complex too and require Big Data but provide great value to the customer. With these benefits come great societal risks, however, and there are other drawbacks and challenges. For example, it is difficult to ensure the quality of data and metadata obtained from crowdsourcing and to maintain the integrity of conceptual model. Big SaaS applications will also need to evolve continuously. This paper will discuss how to address these challenges at all stages of the software lifecycle