Abstract: Exemplary methods for distributed multi-component service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. A root agent in the hierarchy can receive service requests specifying resource requirements and optionally location or affinity constraints, transform these into service request descriptions, and pass the service request descriptions down through the hierarchy to arrive at leaf nodes. The leaf nodes can each, perhaps in parallel, generate solution encodings indicating possible placements of some or all of the components of the service request that one or more computing devices associated with each agent can locally provide while still satisfying the resource requirements. The generated solution encodings can be passed back up and be consolidated as they flow through the hierarchy, allowing the root agent to quickly and accurately determine whether the service request may be fulfilled, and optionally place the service.

Abstract: Exemplary methods for distributed multi-component service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. A root agent in the hierarchy can receive service requests specifying resource requirements and optionally location or affinity constraints, transform these into service request descriptions, and pass the service request descriptions down through the hierarchy to arrive at leaf nodes. The leaf nodes can each, perhaps in parallel, generate solution encodings indicating possible placements of some or all of the components of the service request that one or more computing devices associated with each agent can locally provide while still satisfying the resource requirements. The generated solution encodings can be passed back up and be consolidated as they flow through the hierarchy, allowing the root agent to quickly and accurately determine whether the service request may be fulfilled, and optionally place the service.

Abstract: Exemplary methods for distributed multi-component service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. A root agent in the hierarchy can receive service requests specifying resource requirements and optionally location or affinity constraints, transform these into service request descriptions, and pass the service request descriptions down through the hierarchy to arrive at leaf nodes. The leaf nodes can each, perhaps in parallel, generate solution encodings indicating possible placements of some or all of the components of the service request that one or more computing devices associated with each agent can locally provide while still satisfying the resource requirements. The generated solution encodings can be passed back up and be consolidated as they flow through the hierarchy, allowing the root agent to quickly and accurately determine whether the service request may be fulfilled, and optionally place the service.

Abstract: Exemplary methods for distributed multi-component network-aware service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. An agent in the hierarchy can merge solution encodings to find cover sets indicating feasible placement solutions that can cover an entire application placement request. The agent can partition the components across its children nodes such that global network traffic is minimized. An application graph is generated with components as vertices and edges indicating connections between the components and having associated weights indicating a data transfer rate between the components. The edges can be sorted, and each cover set can be processed by repeatedly assigning unassigned pairs of components having higher data transfer rates to a common assignment set. If multiple placement solutions are found, determined placement costs for each can be used to identify the preferred placement.

Abstract: Exemplary methods for distributed multi-component network-aware service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. An agent in the hierarchy can merge solution encodings to find cover sets indicating feasible placement solutions that can cover an entire application placement request. The agent can partition the components across its children nodes such that global network traffic is minimized. An application graph is generated with components as vertices and edges indicating connections between the components and having associated weights indicating a data transfer rate between the components. The edges can be sorted, and each cover set can be processed by repeatedly assigning unassigned pairs of components having higher data transfer rates to a common assignment set. If multiple placement solutions are found, determined placement costs for each can be used to identify the preferred placement.

Abstract: Exemplary methods for distributed multi-component service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. A root agent in the hierarchy can receive service requests specifying resource requirements and optionally location or affinity constraints, transform these into service request descriptions, and pass the service request descriptions down through the hierarchy to arrive at leaf nodes. The leaf nodes can each, perhaps in parallel, generate solution encodings indicating possible placements of some or all of the components of the service request that one or more computing devices associated with each agent can locally provide while still satisfying the resource requirements. The generated solution encodings can be passed back up and be consolidated as they flow through the hierarchy, allowing the root agent to quickly and accurately determine whether the service request may be fulfilled, and optionally place the service.

Abstract: Methods, using a hardware processor, for online server workload management are provided, comprising: receiving information regarding client device requests; determining, using a hardware processor, a workload distribution for the requests based on electricity cost and carbon footprint of one or more data centers using Lyapunov optimization; sending the workload distribution to the one or more data centers; and causing servers in the one or more data center to be active or inactive based on the workload distribution. Systems are provided, comprising at least one hardware processor configured to: receive information regarding client device requests; determine a workload distribution for the requests based on electricity cost and carbon footprint of one or more data centers using Lyapunov optimization; send the workload distribution to the one or more data centers; and cause servers in the one or more data center to be active or inactive based on the workload distribution.

Abstract: Methods, using a hardware processor, for online server workload management are provided, comprising: receiving information regarding client device requests; determining, using a hardware processor, a workload distribution for the requests based on electricity cost and carbon footprint of one or more data centers using Lyapunov optimization; sending the workload distribution to the one or more data centers; and causing servers in the one or more data center to be active or inactive based on the workload distribution. Systems are provided, comprising at least one hardware processor configured to: receive information regarding client device requests; determine a workload distribution for the requests based on electricity cost and carbon footprint of one or more data centers using Lyapunov optimization; send the workload distribution to the one or more data centers; and cause servers in the one or more data center to be active or inactive based on the workload distribution.