Abstract: A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.

Abstract: Embodiments of the present invention provide systems, methods, and computer program products for optimizing a placement plan. In one embodiment, a method is disclosed in which a request for registration with an external advisor is received. A time to live is received from each external advisor and used to determine an overall timeout period value for a placement engine. After receiving a predictive failure alert, internal and external advisors are ranked according to criteria and advice is received from the qualified advisors. A placement plan is generated based on the advice received from the advisors.

Abstract: Embodiments of the present invention provide systems, methods, and computer program products for optimizing a placement plan. In one embodiment, a method is disclosed in which a request for registration with an external advisor is received. A time to live is received from each external advisor and used to determine an overall timeout period value for a placement engine. After receiving a predictive failure alert, internal and external advisors are ranked according to criteria and advice is received from the qualified advisors. A placement plan is generated based on the advice received from the advisors.

Abstract: Embodiments disclosed herein are directed to recognition tunneling systems, methods and devices, and more particularly, to chemical reactions for selectively labeling proteins and peptides and placing protein and/or peptides into, or onto a nanopore formed in a solid support and threading such in and/or through the nanopore, with such nanopores, in some embodiments, including a molecular motor to pull or otherwise force the protein/peptide through the nanopore.

Abstract: Embodiments of the present invention provide systems, methods, and computer program products for optimizing a placement plan. In one embodiment, a method is disclosed in which a request for registration with an external advisor is received. A time to live is received from each external advisor and used to determine an overall timeout period value for a placement engine. After receiving a predictive failure alert, internal and external advisors are ranked according to criteria and advice is received from the qualified advisors. A placement plan is generated based on the advice received from the advisors.

Abstract: Embodiments of the present invention provide systems, methods, and computer program products for optimizing a placement plan. In one embodiment, a method is disclosed in which a request for registration with an external advisor is received. A time to live is received from each external advisor and used to determine an overall timeout period value for a placement engine. After receiving a predictive failure alert, internal and external advisors are ranked according to criteria and advice is received from the qualified advisors. A placement plan is generated based on the advice received from the advisors.

Abstract: Multi-site disaster recovery mechanism performed by the following steps: (i) providing a disaster recovery (DR) system that includes a plurality of sites where each site of the plurality of sites actively serves infrastructure-as-a-service to a set of tenant(s); (ii) for each site of the plurality of sites, determining the following characteristics of the site: workloads that require DR, workloads characteristics, tenants and capabilities; (iii) for each site of the plurality of sites, determining a plurality of associated sites; and (iv) on condition that a disaster occurs which impacts a first site of the plurality of sites, distributing a primary site workload of the first site across the associated sites of the first site. The determination of the plurality of associated sites associated with each site is based upon at least one of the following characteristics: capacity, workloads that require DR, workloads characteristics, tenants and/or capabilities.

Abstract: A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.