Implementing Software as a Service (SaaS) into an existing business model can offer many advantages, but the entity must consider both the advantages and disadvantages of SaaS along with the overall effect of the implementation to their business. GetCloudServices.com (2012) highlights the advantages of adding SaaS to a corporation’s business model. Some of the perks include cost cutting measures, scalability, data protection, guaranteed service, always upgraded, information sharing, flexibility, and usability. According to the blog, SaaS is a lucrative option for a sound and cost effective IT support solution. Buyya, Broberg, and Goscinski (2011) point out similar advantages: scalability, flexibility, ease of accessibility and configurability, robust, secure, and affordable. They also state that SaaS affords “… a seamlessly and spontaneously coexist, correlate, and coordinate with one another dynamically with dexterity to understand one or more users’ needs, conceive, construct, and deliver them at right time at right place. Anytime anywhere computing tends towards everywhere every time and everything computing” (p. 59).
On the other hand, Buyya, Broberg, and Goscinski (2011) mention that the disadvantages must weigh into the decision as well. They cite controllability, visibility, security and privacy, availability, performance, integration, and standards as possible challenges of the SaaS paradigm integration. The CloudComputingTopics blog echoes similar concerns with SaaS implementation: security, capital outlay, disaster recovery, and deployment. If a large business already has the infrastructure in place along with the customized applications and the solution works well, the experts suggest avoiding SaaS unless the entity is considering end of lifing the existing solution or looking to upgrade.
Within the SaaS paradigm, entities must examine both the advantages and disadvantages before adoption. To recap, small to medium businesses who struggle to afford and in house customized software or large businesses looking to upgrade should consider a SaaS solution. SaaS offers cost cutting measures from the purchase of overhead infrastructure to support software, flexibility of scaling the solution during the peaks and troughs of demand cycles, and guaranteed up time by the vendor. However for large business who already have an existing infrastructure, they may not elect to use SaaS, because the existing solution already meets their needs.
One final element to consider when investigating cloud solution involves awareness of space. Business requires a place to call home, even if the employees travel nonstop and are stationed across the world. The employees need to find a place to call home or to identify as I work for them. These identification mechanisms are the awareness of place. A SaaS solution adopted by an entire entity can provide this awareness of place. Taun’s (1977) chapter, Attachment to Homeland, discusses various groups and time periods and their attachment to their homeland. He provides examples and illustrates the need to identify with a place. This awareness of place grows strong in the human spirit and continues to exhibit the same characteristics in today’s society. Our awareness of place still embodies a physical place, but has also started to exemplify a digital reality. A common business web portal or SaaS solution that has been customized for a business can now be called our homeland. We feel secure to log into this portal to do work as we travel around the world to complete our jobs. Awareness of place holds a strong piece of the puzzle when deciding to replace an existing solution to a SaaS solution.
References
4 reasons why businesses might not want to switch to the cloud. (2012). http://cloudcomputingtopics.com/2012/07/4-reasons-why-businesses-might-not-want-to-switch-to-the-cloud/ Retrieved November 15, 2012.
Buyya, R., Broberg, J., & Goscinski, A. (2011). Cloud computing. Hoboken, New Jersey, United States of America: John Wiley & Sons, Inc.
SaaS – Why does your business need it? (2012). http://www.getcloudservices.com/blog/saas-why-does-your-business-need-it Retrieved November 15, 2012.
Tuan, Yi-fu. (1977). Space and Place: The Perspective of Experience. University of Minnesota Press. Minneapolis, MN.
Showing posts with label cloud computing. Show all posts
Showing posts with label cloud computing. Show all posts
Thursday, January 3, 2013
Workflow
Workflow engines for clouds provide a unique solution for many corporations. A workflow offers a simplistic vantage point of a complex execution and management of applications. Processing and managing big data requires a distributed server farm and data centers. The emergence of recent virtualization technologies and the expansion of cloud acceptance have helped shift to a new paradigm in distributed computing. This distribution computing relies on existing resources for scalability computing.
Services within the cloud have opened new possibilities for vendors and corporations. The Infrastructure as a Service (IaaS) virtualization allows vendors to offer virtual hardware for intensive workflow applications. Platform as a Service (PaaS) clouds expose a high level development and runtime environment for building and deploying applications on the aforementioned IaaS. Finally, Software as a Service (SaaS) solutions give corporations the flexibility of leveraging their solutions to integrate into the existing workflows.
The background of the scientific workflows happens on infrastructures like OpenScience Grid and dedicated clusters. Existing workflow engine systems typically take advantage of these free Open Science Grids through some type of research agreement. The CloudbusToolkit workflow engine gives an example of the background of scaling workflow applications on clouds using market oriented computing.
The primary benefit of moving to clouds is application scalability. The elastic nature of clouds improves the process of adjusting the resource quantities and characteristics to vary during the application runtime. In other words, the resources scale higher when there is a higher demand and lower when there is less demand. With this capability, the services can easily meet Quality of Service (QoS) requirements for applications. This feature was never truly available in traditional computing methods.
With the change in dynamics from traditional to cloud, Service Level Agreements (SLA) has become the primary focus among both service providers and corporate consumers. Competition among service providers have driven SLAs to be drafted with extreme care in order to entice corporate consumers by offering specific niches and advantages over competitors. Cloud service providers also utilize economies of scale; providing computing, storage, and bandwidth resources at substantially lower costs.
The workflow system involves the workflow engine, a resource broker, and plug-ins for communicating with various platforms. To illustrate the workflow system architectura lstyle, we will examine scientific applications. Scientific applications consist of tasks, data elements, control sequences, and data dependencies. The components within the core are responsible for managing the execution of workflows. The plug-ins supports the workflow executions in different environments and platforms. The resource brokers populate the bottom of the diagram and consist of grids, clusters, and clouds.
The workflow system has great potential for growth in the future. Gartner has the paradigm ranked at the top of the hype cycle in 2010. There are still challenges to overcome, but the potential for growth exceeds imagination. Giants in the field like Microsoft, Amazon, Google own enormous data centers to provide these services to corporate consumers. Eventually, these consumers can start to form hybrid models where they select different sections of the vendor services to create their workflow cloud. The current workflow model is just the first step to offer customers a complete solution.
Reference
Buyya, R., Broberg, J., & Goscinski, A. (2011). Cloud computing. Hoboken, New Jersey, United States of America: John Wiley & Sons, Inc.
Services within the cloud have opened new possibilities for vendors and corporations. The Infrastructure as a Service (IaaS) virtualization allows vendors to offer virtual hardware for intensive workflow applications. Platform as a Service (PaaS) clouds expose a high level development and runtime environment for building and deploying applications on the aforementioned IaaS. Finally, Software as a Service (SaaS) solutions give corporations the flexibility of leveraging their solutions to integrate into the existing workflows.
The background of the scientific workflows happens on infrastructures like OpenScience Grid and dedicated clusters. Existing workflow engine systems typically take advantage of these free Open Science Grids through some type of research agreement. The CloudbusToolkit workflow engine gives an example of the background of scaling workflow applications on clouds using market oriented computing.
The primary benefit of moving to clouds is application scalability. The elastic nature of clouds improves the process of adjusting the resource quantities and characteristics to vary during the application runtime. In other words, the resources scale higher when there is a higher demand and lower when there is less demand. With this capability, the services can easily meet Quality of Service (QoS) requirements for applications. This feature was never truly available in traditional computing methods.
With the change in dynamics from traditional to cloud, Service Level Agreements (SLA) has become the primary focus among both service providers and corporate consumers. Competition among service providers have driven SLAs to be drafted with extreme care in order to entice corporate consumers by offering specific niches and advantages over competitors. Cloud service providers also utilize economies of scale; providing computing, storage, and bandwidth resources at substantially lower costs.
The workflow system involves the workflow engine, a resource broker, and plug-ins for communicating with various platforms. To illustrate the workflow system architectura lstyle, we will examine scientific applications. Scientific applications consist of tasks, data elements, control sequences, and data dependencies. The components within the core are responsible for managing the execution of workflows. The plug-ins supports the workflow executions in different environments and platforms. The resource brokers populate the bottom of the diagram and consist of grids, clusters, and clouds.
The workflow system has great potential for growth in the future. Gartner has the paradigm ranked at the top of the hype cycle in 2010. There are still challenges to overcome, but the potential for growth exceeds imagination. Giants in the field like Microsoft, Amazon, Google own enormous data centers to provide these services to corporate consumers. Eventually, these consumers can start to form hybrid models where they select different sections of the vendor services to create their workflow cloud. The current workflow model is just the first step to offer customers a complete solution.
Reference
Buyya, R., Broberg, J., & Goscinski, A. (2011). Cloud computing. Hoboken, New Jersey, United States of America: John Wiley & Sons, Inc.
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