Abstract
The Cloud Computing (CC) provides access to the resources with usage based payments model. The application service providers can seamlessly scale the services. In CC infrastructure, a different number of virtual machine instances can be created depending on the application requirements. The capability to scale Software-as-a-Service (SaaS) application is very attractive to the providers because of the potential to scale application resources to up or down, the user only pay for the resources required. Even though the large-scale applications are deployed on cloud infrastructures on pay-per-use basis, the cost of idle resources (memory, CPU) is still charged to application providers. The issues of saturation and wastage of cloud resources are still unresolved. This paper attempts to propose the resource allocation models for SaaS applications deployments over CC platforms. The best balanced resource allocation model is proposed keeping in view cost and user requirements.
Highlights
The Cloud computing (CC) covers two main areas which include applications delivery over the Internet as a service and system software deployed in datacenters offering the services normally on pay-per-use basis pricing model [1]
Infrastructure-as-a-Service (IaaS) provider charge by 3500 virtual instances because a peak load occurred at a certain time frame and when this peak disappeared, it would pay for unused resources [4]
To address under and over utilization issues, this work recommends a model for allocating workload when deploying SaaS platform and its applications over cloud infrastructures
Summary
The Cloud computing (CC) covers two main areas which include applications delivery over the Internet as a service and system software deployed in datacenters offering the services normally on pay-per-use basis pricing model [1]. With CC a variable number of virtual machine instances can be created depending on the application requirement and this is the elasticity feature of this computing technique [2]. Infrastructure-as-a-Service (IaaS) provider charge by 3500 virtual instances because a peak load occurred at a certain time frame and when this peak disappeared, it would pay for unused resources [4]. This effect is still a barrier for SaaS providers, whose applications have different peak loads and they are highly prone to suffer over and under provisioning of resources [6, 7]
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