Multi-cloud provisioning and load distribution for three-tier applications
Cloud data centers are becoming the preferred deployment environment for a wide range of
business applications because they provide many benefits compared to private in-house
infrastructure. However, the traditional approach of using a single cloud has several
limitations in terms of availability, avoiding vendor lock-in, and providing legislation-
compliant services with suitable Quality of Experience (QoE) to users worldwide. One way
for cloud clients to mitigate these issues is to use multiple clouds (ie, a Multi-Cloud). In this …
business applications because they provide many benefits compared to private in-house
infrastructure. However, the traditional approach of using a single cloud has several
limitations in terms of availability, avoiding vendor lock-in, and providing legislation-
compliant services with suitable Quality of Experience (QoE) to users worldwide. One way
for cloud clients to mitigate these issues is to use multiple clouds (ie, a Multi-Cloud). In this …
Cloud data centers are becoming the preferred deployment environment for a wide range of business applications because they provide many benefits compared to private in-house infrastructure. However, the traditional approach of using a single cloud has several limitations in terms of availability, avoiding vendor lock-in, and providing legislation-compliant services with suitable Quality of Experience (QoE) to users worldwide. One way for cloud clients to mitigate these issues is to use multiple clouds (i.e., a Multi-Cloud). In this article, we introduce an approach for deploying three-tier applications across multiple clouds in order to satisfy their key nonfunctional requirements. We propose adaptive, dynamic, and reactive resource provisioning and load distribution algorithms that heuristically optimize overall cost and response delays without violating essential legislative and regulatory requirements. Our simulation with realistic workload, network, and cloud characteristics shows that our method improves the state of the art in terms of availability, regulatory compliance, and QoE with acceptable sacrifice in cost and latency.
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