Forecasting Cloud Application Workloads with Cloud Insightfor Predictive Resource Management
ABSTARCT :
Predictive cloud resource management has been widely adopted to overcome the limitations of reactive cloud auto scaling. The predictive resource management is highly relying on workload predictors, which estimate short-/long-term fluctuations of cloud application workloads. These predictors tend to be pre-optimized for specific workload patterns. However, such predictors are still insufficient to handle real-world cloud workloads whose patterns may be unknown a priori, may dynamically change over time and maybe irregular. As a result, these predictors often cause over-/under-provisioning of cloud resources. To address this problem, we create Cloud Insight, a novel cloud workload prediction framework, leveraging the combined power of multiple workload predictors. Cloud Insight creates an ensemble model using multiple predictors to make accurate predictions for real workloads. The weights of the predictors in Cloud Insight are determined at runtime with their accuracy for the current workload using multi-class regression. The ensemble model is periodically optimized to handle sudden changes in the workload. We evaluated Cloud Insight with various real workload traces. The results show that Cloud Insight has 13%–27% higher accuracy than state-of-the-art predictors. Moreover, there sults from trace-based simulations with a cloud resource manager show that Cloud Insighthas 15%–20% less under-/over-provisioning periods, resulting in high cost-efficiency and low SLA violations.
EXISTING SYSTEM :
Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioral changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioral periodicity in the infrastructure.
DISADVANTAGE :
The application runs long enough so that the time taken by a potential virtual infrastructure rearrangement is negligible.
The application is executed repeatedly over a period of time.
PROPOSED SYSTEM :
This paper proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behavior discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (20%) better workload predictions for the future of simulated clouds than random workload selection.
ADVANTAGE :
? The result of this prediction subsequently enables to steer current and future cloud usage accordingly, including the option of resource rearrangement if indicated.
? The concept of a private-cloud level load prediction method based on the combination of historic traces, aimed at improving execution quality.
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