Abstract:
There is provided a method and system for process migration in a data center network. The method includes selecting processes to be migrated from a number of overloaded servers within a data center network based on an overload status of each overloaded server. Additionally, the method includes selecting, for each selected process, one of a number of underloaded servers to which to migrate the selected process based on an underload status of each underloaded server, and based on a parameter of a network component by which the selected process is to be migrated. The method also includes migrating each selected process to the selected underloaded server such that a migration finishes within a specified budget.
Abstract:
A system for managing allocation of resources based on service level agreements between application owners and cloud operators. Under some service level agreements, the cloud operator may have responsibility for managing allocation of resources to the software application and may manage the allocation such that the software application executes within an agreed performance level. Operating a cloud computing platform according to such a service level agreement may alleviate for the application owners the complexities of managing allocation of resources and may provide greater flexibility to cloud operators in managing their cloud computing platforms.
Abstract:
The described implementations relate to processing of electronic data. One implementation is manifested as a system that can include an inference engine and at least one processing device configured to execute the inference engine. The inference engine can be configured to perform automated detection of concepts expressed in failure logs that include unstructured data. For example, the inference engine can analyze text of support tickets or diary entries relating to troubleshooting of an electronic network to obtain concepts identifying problems, actions, or activities. The inference engine can also be configured to generate output that reflects the identified concepts, e.g., via a visualization or queryable programming interface.
Abstract:
There is provided a method and system for process migration in a data center network. The method includes selecting processes to be migrated from a number of overloaded servers within a data center network based on an overload status of each overloaded server. Additionally, the method includes selecting, for each selected process, one of a number of underloaded servers to which to migrate the selected process based on an underload status of each underloaded server, and based on a parameter of a network component by which the selected process is to be migrated. The method also includes migrating each selected process to the selected underloaded server such that a migration finishes within a specified budget.
Abstract:
A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a “stripped down” architecture to complement its use in the above-summarized data-centric environment.
Abstract:
Optimization mechanism that dynamically splits the computation in an application (e.g., cloud), that is, which parts run on a client (e.g., mobile) and which parts run on servers in a datacenter. This optimization can be based on application characteristics, network connectivity (e.g., latency, bandwidth, etc.) between the client and the datacenter, power or energy available at the client, size of the application objects, load in the datacenter, security and privacy concerns (e.g., cannot share all data on the client with the datacenter), and other criteria, as desired.
Abstract:
An optimization framework for hosting sites that dynamically places application instances across multiple hosting sites based on the energy cost and availability of energy at these sites, application SLAs (service level agreements), and cost of network bandwidth between sites, just to name a few. The framework leverages a global network of hosting sites, possibly co-located with renewable and non-renewable energy sources, to dynamically determine the best datacenter (site) suited to place application instances to handle incoming workload at a given point in time. Application instances can be moved between datacenters subject to energy availability and dynamic power pricing, for example, which can vary hourly in day-ahead markets and in a time span of minutes in realtime markets.
Abstract:
An optimization framework for hosting sites that dynamically places application instances across multiple hosting sites based on the energy cost and availability of energy at these sites, application SLAs (service level agreements), and cost of network bandwidth between sites, just to name a few. The framework leverages a global network of hosting sites, possibly co-located with renewable and non-renewable energy sources, to dynamically determine the best datacenter (site) suited to place application instances to handle incoming workload at a given point in time. Application instances can be moved between datacenters subject to energy availability and dynamic power pricing, for example, which can vary hourly in day-ahead markets and in a time span of minutes in realtime markets.
Abstract:
Instances of a same application execute on different respective hosts in a cloud computing environment. Instances of a monitor application are distributed to concurrently execute with each application instance on a host in the cloud environment, which provides user access to the application instances. The monitor application may be generated from a specification, which may define properties of the application/cloud to monitor and rules based on the properties. Each rule may have one or more conditions. Each monitor instance running on a host, monitors execution of the corresponding application instance on that host by obtaining from the host information regarding values of properties on the host per the application instance. Each monitor instance may evaluate the local host information or aggregate information collected from hosts running other instances of the monitor application, to repeatedly determine whether a rule condition has been violated. On violation, a user-specified handler is triggered.
Abstract:
The discussion relates to middlebox reliability. One example can apply event filters to a dataset of middlebox error reports to separate redundant middlebox error reports from a remainder of the middlebox error reports of the dataset. The example can categorize the remainder of the middlebox error reports of the dataset by middlebox device type. The example can also generate a graphical user interface that conveys past reliability and predicted future reliability for an individual model of an individual middlebox device type.