International Business Machines Corporation

International Busniss Machines Coporation

David Breitgand

Onn Menahem Shehory

Amir Epstein

Ealan Abraham Henis

Maayan Goldstein

John Michael Lake

Sergey Zeltyn

Alexander Zadorojniy

Kenneth Nagin

Benny Rochwerger

Yosef Moatti

Lior Limonad

Michael Masin

Asaf Adi

Nir Mashkif

Yaron Weinsberg

Alexander Glikson

Ari Volcoff

Michael Edward Factor

Norbert George Vogl

Elliot Karl Kolodner

Shmuel Ben-Yehuda

Julian Satran

Vijay Krishnarao Naik

Yih-Shin Tan

Kirk Alan Beaty

Eran Raichstein

Dan S Pelleg

Michael R Hines

Kyung D Ryu

Dilma Menezes Da Silva

Ofer Biran

Irit Loy

Ilyas Mohamed Iyoob

Dean Lorenz

Marcio Augusto Silva

Inbar Shapira

Ezra Silvera

Alex Glikson

Oshrit Feder

Edya Ladan-Mozes

Elena Yerushalmi

Zvi Dubitzky

Giovanni Toffetti-Carughi

Pavel Kravchenco

Daniel Marom

Avi Weit

Doron Podoleanu

Embodiments may include novel techniques to communicate user preferences to the FaaS provider so as to provide full applicability of FaaS for business critical applications and to provide full realization of the FaaS model flexibility. For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise receiving a request for processing of a computing task and associated data indicating a context of an overall process flow for the computing task, wherein the request for processing is a function invocation for a FaaS computing system, evaluating the data indicating the context and scheduling computing resources for performing the computing task based on the data indicating the context, and performing the computing task using the scheduled computing resources.

System and apparatus for FaaS business goals optimization

A heterogeneous resource reservation (HRR) manager configured to classify historical application requests from a past time interval for a first workload to generate labeled historical application requests. The HRR manager further configured to generate a forecast based on the labeled historical application requests and for predicting future application requests for the first workload for a future time interval and calculate a joint plan based on the forecast. The joint plan including a set of virtual resources, a set of billing contracts, and a set of load balancer weights. The HRR manager further configured to implement the joint plan for a distributed computing workload during the future time interval.

Heterogeneous resource reservation

This invention relates to generating benchmarks without the need for executing the application on a specific benchmarking platform or by testing the application against known inputs and outputs. Instead, the system, in an embodiment, allows for source code comparisons to known systems to predict performance. The benchmarking can be done for varying deployment platforms and various usage patterns. The source code comparison process allows for fast performance prediction by using a distance vector.

System and method for predictive performance analysis via dynamic benchmark generation

An example method includes receiving a nominal equivalent resource usage data, an infrastructure usage data, an effective production capacity, a demand elasticity curve, and workload scheduling constraints across a plurality of accounts. The method includes calculating an equivalent resource utilization based on the nominal equivalent resource usage data, the infrastructure usage data, and the effective production capacity. The method includes calculating a potential value increase for a service based on the workload scheduling constraints, the nominal equivalent resource usage data, the effective production capacity, and the demand elasticity curve. The method includes calculating a value increase scheme for the service based on the potential value increase and sending the value increase scheme to a user workload device. The method includes receiving a workload constraint from the user workload device and scheduling a workload service operation based on the infrastructure usage data, the value increase scheme, and the workload constraint.

Scheduling workload service operations using value increase scheme

Machines, systems and methods for managing quality of service in a virtualized computing environment, the method comprising: provisioning one or more active virtual machines (VMs) over one or more hosts in a virtualized computing network, wherein one or more resources are allocated to the active VMs before the active VMs service one or more requests; monitoring information associated with quality of service defined for servicing of the requests; and designating at least an active VM as a shadow VMs, in response to results of the monitoring, wherein at least one resource remains allocated to the shadow VM, while the shadow VM enters a dormant state and no longer services any requests.

Efficient provisioning and deployment of virtual machines

Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.

Physical-interactive IDS (P2D)

A method for providing dynamic resizing of at least one virtual machine in a cloud infrastructure includes steps of: obtaining a measure of resource usage for the virtual machine and/or application performance of an application running on the virtual machine; determining, as a function of prescribed rules relating to resource usage and the measure of resource usage and/or application performance, whether the virtual machine requires resizing; and resizing the virtual machine to dynamically change resource availability for the virtual machine so as to maintain resource usage and/or application performance within prescribed limits defined by the prescribed rules.

Dynamic virtual machine resizing in a cloud computing infrastructure

Physical interactive IDs (P2D)

Machines, systems and methods for allocating resources to in a virtualized computing environment, the method comprising detecting one or more host machines with resources allocated to one or more virtual machines (VMs) that are in an idle state; reducing resource entitlements for at least one of the VMs that is detected to be in the idle state to make more resources available for allocation to VMs that are not in the idle state; and increasing resource entitlements for at least one of the VMs with reduced entitlement, in response to determining that the VM with reduced entitlement is no longer in the idle state.

Optimized resource allocation and management in a virtualized computing environment

Systems and methods for managing capacity over-commitment in a resource provisioning environment. The method comprising collecting historical data about capacity usage in a resource provisioning environment and failure statistics from an operational database, in addition to prior service commitments due to previously contracted service level agreements (SLAs) stored in a SLA repository; calculating equivalent capacity for the resource provisioning environment based on a defined residual benefit goal, and other business goals instrumented by way of a policy engine; and determining whether placing requested virtual resources on one or more hosts is feasible taking into account anti-collocation constraints that prohibit placement of two or more virtual resources on same host.

Capacity over-commit management in resource provisioning environments

A system and method for correlating events in data processing resources processing a workload. An event correlation service comprising an event correlation service software framework is provided on a processor unit. The event correlation service is configured, using the event correlation service software framework, for correlating the events from monitored data for the workload, wherein the workload is processed using the data processing resources provided by a provider of the data processing resources. The event correlation service receives monitored data for the workload as the workload is being processed. The monitored data is processed by the event correlation service to identify an occurrence of an event of interest for the workload as the workload is being processed. An output is provided in response to identifying the occurrence of the event of interest.

Data processing environment event correlation

Machines, systems and methods for deciding whether a plurality of virtual machines (VMs) may be hosted on a computing system with resources to support at least one of the VMs, the method comprising determining maximum permissible probability of infringement (maxPermInfrProb(v)) for a VM(v), wherein 'v' is a value from 1 to n, such that n is the number of VMs in a set G, as stipulated by the VMs' SLA, wherein 'v' is a value from 1 to n, such that n is the number of VMs in a set G; partitioning the set G into a plurality of groups G(G, . . . G, . . . G), so that each group is homogenous with respect to maxPermInfrProb defined for members of the group G; and organizing the groups Gbased on the value of maxPermInfrProb for each G.

Resource allocation in a virtualized computing environment

Systems and methods for deploying a virtual machine (VM) on a host are provided. An exemplary method comprises notifying a host to download a master copy of a VM image from a remotely located network storage device, in response to a service provider providing a definition manifest for a service request supported by the VM, wherein the host deploys the VM directly from the VM image downloaded to a storage medium locally connected to the host machine, wherein deployment of the VM allows the host to locally service the service request associated with the definition manifest, wherein the host replicates copies of the VM image, in response to receiving additional service requests to create one or more VM clones; wherein the host customizes the one or more VM clones based on the definition manifest.

Optimized deployment and replication of virtual machines

Systems and methods for policy-driven capacity management in a resource provisioning environment, the method comprising storing, in an operational database one or more virtual resource sets (VRSs) and elasticity ranges for components to be deployed in a resource provisioning environment, and probabilistic guarantees on the elasticity ranges defined in a service manifest provided by a service subscriber; collecting historical data about capacity usage in the resource provisioning environment and failure statistics from the operational database, in addition to prior service commitments due to previously contracted service level agreements (SLAs) stored in a SLA repository; calculating equivalent capacity for the resource provisioning environment based on a defined residual benefit goal, and other business goals instrumented by way of a policy engine; and placing requested virtual resources on physical resources, in response to determining that sufficient physical capacity is available to host the calculated equivalent capacity.

Policy-driven capacity management in resource provisioning environments

A method for monitoring performance includes monitoring packet traffic on at least one socket that is associated with an application running on a computer and is communicated over a data network. First and second instances of a performance indicator of the packet traffic are measured respectively during first and second monitoring intervals. An alert is issued upon making a determination that a change between the first instance and the second instance exceeds a predetermined threshold.

Platform-level indicators of application performance

Growing community of inventors

Modiin, Israel

David Breitgand