11/11/2022 0 Comments Google prototype scopes![]() ![]() Despite this run-time dynamicity, data management decisions across a federated cloud storage setup are commonly based on the static properties of the operational environment (e.g., cloud storage system X always provides significantly better overall performance than the cloud storage system Y). In practice, federated cloud storage setups are subject to dynamicity, e.g., relevant dynamic properties are performance characteristics (i.e., latency and throughput), evolving price conditions, new providers arrival, cloud provider availability (i.e., uptime), etc. Therefore, a federated storage cloud setup, which combines different storage resources and SLA guarantees from multiple cloud providers has become an increasingly popular tactic and proven practice for designing the storage tier of cloud-based applications (e.g., SaaS applications, IoT applications, etc). In addition, applications backed by a single cloud provider are highly subject to vendor lock-in, data unavailability, provider reliability, data security, etc. However, service providers find it difficult to select the best candidate when faced with numerous cloud storage providers (CSPs) and their underlying heterogeneous storage systems, as well as their different promised Service Level Agreement (SLA) guarantees. As such, this enables service providers to offer enhanced services in a scalable and timely manner. Therefore, nowadays, a growing number of applications (e.g., Software-as-a-Service (SaaS) applications, Internet of Things (IoT) applications, etc) take maximum advantage of the flexible services such as Storage-as-a-Service offered by the cloud to minimize the high up-front cost and optimize the overall maintenance cost. ![]() In addition, our in-depth performance evaluation results indicate that the benefits are achieved with acceptable performance overhead, and as such highlight the applicability of the proposed middleware for real-world application cases.Ĭloud computing has become a highly attractive paradigm due to its potential to significantly reduce costs through optimization and increase operating and economic benefits. The evaluation results demonstrate (i) the ability of the middleware to perform data management decisions that take into account the run-time dynamicity (i.e., dynamic properties) of a federated cloud storage setup to meet the promised SLAs, and (ii) the self-adaptive behavior of SCOPE without the need for operator intervention. ![]() We have validated SCOPE in the context of a realistic SaaS application, performed an extensive functional validation, and conducted a thorough experimental evaluation. To address these concerns, we present SCOPE, a policy-based and autonomic middleware that provides self-adaptiveness for data management in federated clouds. Additionally, due to the sheer complexity of cloud-based applications coupled with the heterogeneous and volatile nature of federated cloud setups, the complexity of building, maintaining, and expending such applications increases dramatically and therefore managing them manually is no longer simply an option. In general, existing federated cloud systems are oblivious to dynamic properties of the underlying operational environment, resulting in both sub-optimal data management decisions and costly SLA violations. ![]() However, federated cloud storage setups are prone to run-time dynamicity: many dynamic properties impact the way such a setup is governed and evolved over time, e.g., storage providers enter or leave the market QoS metrics and SLA guarantees may change over time etc. A federated cloud storage setup which integrates and utilizes storage resources from multiple cloud storage providers has become an increasingly popular and attractive paradigm for the persistence tier in cloud-based applications (e.g., SaaS applications, IoT applications, etc). ![]()
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