Abstract: The present disclosure is directed to a system for monitoring and analyzing operation of a widely distributed service operated by an Infrastructure-as-a-Service (IaaS) tenant but deployed on a set of virtual resources controlled by an independent IaaS provider. The set of virtual resources can be organized into clusters in which resources are expected to behave similarly to each other. Virtual resources that do not behave similar to peer resources in the same cluster, i.e., outliers, may be indicative of problems that need to be addressed. The monitoring system can collect performance metric data from virtual resources, and compare the performance of each virtual resource in a cluster with the performance of every other virtual resource in the cluster to detect outliers. This comparison can involve correlation analysis, ANOVA analysis, or regression analysis.

Abstract: The present disclosure is directed to a monitoring system for automatically inferring, without human modelling input or information regarding actual physical network connectivity, a service architecture of a widely distributed service operated by an Infrastructure-as-a-Service (IaaS) tenant but deployed on a set of virtual resources controlled by an independent IaaS provider. The monitoring system can collect infrastructure metadata and/or system-level metric data characterizing the set of virtual resources from the IaaS provider, and automatically infer from the metadata and/or metric data how the virtual resources should be organized into groups, clusters and hierarchies. The monitoring system can automatically infer this service architecture using naming conventions, security rules, software types, deployment patterns, and other information gleaned from the metadata and/or metric data. The monitoring system can then run analytics based on this inferred service architecture to report on service operation.

Abstract: The present disclosure is directed to a monitoring system for automatically inferring, without human modelling input or information regarding actual physical network connectivity, a service architecture of a widely distributed service operated by an Infrastructure-as-a-Service (IaaS) tenant but deployed on a set of virtual resources controlled by an independent IaaS provider. The monitoring system can collect infrastructure metadata and/or system-level metric data characterizing the set of virtual resources from the IaaS provider, and automatically infer from the metadata and/or metric data how the virtual resources should be organized into groups, clusters and hierarchies. The monitoring system can automatically infer this service architecture using naming conventions, security rules, software types, deployment patterns, and other information gleaned from the metadata and/or metric data. The monitoring system can then run analytics based on this inferred service architecture to report on service operation.

Abstract: The present disclosure is directed to a system for monitoring and analyzing operation of a widely distributed service operated by an Infrastructure-as-a-Service (IaaS) tenant but deployed on a set of virtual resources controlled by an independent IaaS provider. The set of virtual resources provided to the IaaS tenant by the IaaS provider is hosted on a set of physical resources selected by the IaaS provider, and both the set of virtual resources and the set of physical resources can change rapidly in both size and composition (i.e., the resources are “ephemeral”). Although the monitoring system may not have visibility into the composition, configuration, location, or any other information regarding the set of physical resources, the monitoring system can evaluate the performance of the virtual resources and infer that a virtual resource within the set of virtual resources may be hosted on at least one physical resource that is underperforming.

Abstract: The present disclosure is directed to a system for monitoring and analyzing operation of a widely distributed service operated by an Infrastructure-as-a-Service (IaaS) tenant but deployed on a set of virtual resources controlled by an independent IaaS provider. The set of virtual resources can be organized into clusters in which resources are expected to behave similarly to each other. Virtual resources that do not behave similar to peer resources in the same cluster, i.e., outliers, may be indicative of problems that need to be addressed. The monitoring system can collect performance metric data from virtual resources, and compare the performance of each virtual resource in a cluster with the performance of every other virtual resource in the cluster to detect outliers. This comparison can involve correlation analysis, ANOVA analysis, or regression analysis.

Abstract: The present disclosure is directed to a monitoring system for rapidly updating a service architecture of a widely distributed service operated by an Infrastructure-as-a-Service (IaaS) tenant but deployed on a set of virtual resources controlled by an independent IaaS provider. The set of virtual resources provided to the IaaS tenant by the IaaS provider is selected by the IaaS provider and can change rapidly in both size and composition (i.e., the virtual resources are “ephemeral”). The monitoring system can infer from infrastructure metadata and/or system-level metric data how the virtual resources should be organized into groups, clusters and hierarchies. The monitoring system can also update the service architecture frequently to capture an expected rate of change of the resources, e.g., every five minutes. The monitoring system can then run analytics based on this inferred service architecture to report on service operation.

Abstract: A system, program storage device, and method of optimizing data placement on a storage device, the method comprising establishing a specified time constraint for which the storage device is to delete data stored thereon; dividing a data object into a plurality of data bits; programming a block of data and the data bits with a logic operand if the storage device is incapable of deleting the data within the specified time constraint; creating an encoded block of data from the programmed block of data and the data bits; organizing the encoded block of data and the data bits in the storage device according to data deletion requirements; and removing the data bits from the storage device if the data bits are organized within a specified data deletion requirement, wherein the data bits are removed using a data shredding process, and wherein the logic operand comprises an exclusive-or (XOR) operator.