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Workload characterization: A survey revisited
Workload characterization is a well-established discipline that plays a key role in many
performance engineering studies. The large-scale social behavior inherent in the …
performance engineering studies. The large-scale social behavior inherent in the …
Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management
Predictive cloud resource management has been widely adopted to overcome the
limitations of reactive cloud autoscaling. The predictive resource management is highly …
limitations of reactive cloud autoscaling. The predictive resource management is highly …
An adaptive prediction approach based on workload pattern discrimination in the cloud
Generally speaking, the workloads are changing rapidly on the Internet, but there is still
regularity of changing patterns. Currently, workload prediction has become a promising tool …
regularity of changing patterns. Currently, workload prediction has become a promising tool …
A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances
Cloud providers sell their idle capacity on markets through an auction-like mechanism to
increase their return on investment. The instances sold in this way are called spot instances …
increase their return on investment. The instances sold in this way are called spot instances …
TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud
Workload patterns of cloud applications are changing regularly. The workload prediction
model is key for auto-scaling of resources in a cloud environment. It is hel** with cost …
model is key for auto-scaling of resources in a cloud environment. It is hel** with cost …
PEAS: A performance evaluation framework for auto-scaling strategies in cloud applications
Numerous auto-scaling strategies have been proposed in the past few years for improving
various Quality of Service (QoS) indicators of cloud applications, for example, response time …
various Quality of Service (QoS) indicators of cloud applications, for example, response time …
Cloudinsight: Utilizing a council of experts to predict future cloud application workloads
Many predictive approaches have been proposed to overcome the limitations of reactive
autoscaling on clouds. These approaches leverage workload predictors that are usually …
autoscaling on clouds. These approaches leverage workload predictors that are usually …
Comparing model-based predictive approaches to self-adaptation: CobRA and PLA
Modern software-intensive systems must often guarantee certain quality requirements under
changing run-time conditions and high levels of uncertainty. Self-adaptation has proven to …
changing run-time conditions and high levels of uncertainty. Self-adaptation has proven to …
Spotweb: Running latency-sensitive distributed web services on transient cloud servers
Many cloud providers offer servers with transient availability at a reduced cost. These
servers can be unilaterally revoked by the provider, usually after a warning period to the …
servers can be unilaterally revoked by the provider, usually after a warning period to the …
Measuring cloud workload burstiness
Workload burstiness and spikes are among the main reasons for service disruptions and
decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate …
decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate …