International Business Machines Corporation

Asser Nasreldin Tantawi

Malgorzata Steinder

Yurdaer Nezihi Doganata

Giovanni Pacifici

Michael Joseph Spreitzer

Wolfgang Segmuller

Hai Shan Wu

Merve Unuvar

Arun Kwangil Iyengar

Charles P Wright

Erich Miles Nahum

Hongbo Jiang

Ren Shi Luo

Ian N Whalley

Diana Jeanne Arroyo

Claris Castillo

Edith H Stern

Barry Edward Willner

Gennaro A Cuomo

Brian Keith Martin

Alaa Salah Youssef

Maxim I Sviridenko

Magda Mahmoud Mourad

Tracy Jay Kimbrel

Bogdan Ionut Ghit

Li Feng Zhang

Lior Aronovich

Xiao Liang Hu

Chatschik Bisdikian

Seetharami R Seelam

Jonathan Paul Munson

Yixin Diao

Nikolaos Anerousis

Colin George Harrison

Laurent Schares

Mariusz Sabath

Hao Yu

James Edwin Hanson

Rui Xiong Tian

Alan G Ganek

Michael Masin

Thomas R Gissel

Marc Hubert Willebeek-LeMair

Michelle Yoonkyung Kim

Dinesh Kumar

Dominick Anthony Zumbo

Jia Jun Brandon Lum

Iqbal I Mohomed

Elizabeth Ann Black-Ziegelbein

Erik Alan Daughtrey

Edward Cholchin So

Zohar Feldman

Howard L Operowsky

Stephen Brady

A Steven Krantz

Franco Marconcini

Ahmed Nasr-El-Din Tantawy

Barry C Goldstein

Davis A Foulger

Pavlos Maniotis

Robert E Chapman

Gopalaswamy Soora

Mike Joseph Spreitzer

Thomas E Newman

Barry Marks

Marc A Block

Hanafy El Sayed Meleis

Marcelo Amaral

Ming-Hung Chen

Claudia Misale

Ying Lu

Systems, computer-implemented methods, and computer program products to facilitate optimization of resource usage based on quota trees are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a determination component that determines one or more quota trees that classify a job request as inadmissible. The computer executable components further comprise an optimization component that optimizes resource usage to enable admissibility of the job request based on the one or more quota trees.

Optimizing resource utilization based on quota trees in resource scheduling

Disclosed embodiments provide a virtual topology in response to an allocation request. The virtual topology is a synchronized subgraph of the physical topology. The synchronized subgraph virtual topology mirrors the physical topology in terms of tree structure. However, merely sharing the physical topology with users/customers is not feasible, as it may reveal additional infrastructure details such as MAC addresses, IP addresses, and the like, that can compromise security and/or jeopardize multi-tenant operations. Disclosed embodiments create virtual topology structures with obfuscated node data, so that important data such as physical IP addresses and/or MAC addresses are hidden from the end-users. Thus, disclosed embodiments provide the benefits of performance enhancement that comes from sharing the topology without the downside of compromising security by revealing physical topology details.

Resource topology generation for computer systems

Aspects of the invention include selecting a node for an infrastructure update. The selected node is included in a cluster of nodes executing workloads that include containers. A future workload is prevented from being scheduled on the selected node. A workload currently executing on the selected node is migrated to another node included in the cluster of nodes. Infrastructure code on the selected node is updated, and in response to the updating, the ability to schedule a future workload on the selected node is enabled.

Automated infrastructure updates in a cluster environment that includes containers

Techniques and a system for adaptive data packing are provided. In one example, a system includes a container component and a computing node component. The container component monitors one or more resources for container data in a network environment and determines variability data representative of a variability of the one or more resources for the container data during a period of time. The computing node component that selects a computing node in the network environment for particular container data based on the variability data.

Adaptive data packing

Techniques facilitating advising bid values for usage of resource in a resource allocation data analytics framework are provided. In one example, a computer-implemented method can include generating, by a system operatively coupled to a processor, one or more performance models. A performance model of the one or more performance models is associated with respective service classes for a resource of a computing infrastructure associated with a provider network and comprising one or more resources available for allocation. A service class of the respective service classes represents a performance differentiation compared to other ones of the respective service classes. The computer-implemented method can also include determining, by the system, a suggested bid value for the resource. The suggested bid value represents an expected performance metric of the resource as a function of a bid value and is based on the one or more performance models.

Bid advising in resource allocation data analytics frameworks

Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Adaptive algorithm for cloud admission policies

There are provided a method for operating a cloud computing infrastructure. In one embodiment, the method performs allocation domain modeling and provides a cloud scheduler framework that takes as input desired optimization objectives and the workload constraints and efficiently produces a placement solution that satisfies the constraints while optimizing the objectives in a way that adjusts itself depending on the objectives. As the objectives change, e.g., due to actions from system administrators or due to changes in business policies, the system optimizes itself accordingly and still produces efficient and optimized placement solutions. The method constructs an Allocation Domain (AD) that is a particular facet for allocating a logical entity to a physical entity. An AD is created using: variables, functional definitions (functions of variables), and a policy specification that includes a Boolean expression (of the functional definitions).

Application placement through multiple allocation domain agents and flexible cloud scheduler framework

Placing an application on a private portion and a public portion of a hybrid computing environment for processing. An application may be received for placement and processing. A primary processing objective and a split preference of the application may be determined. The split preference indicates whether the application can be processed using one or both of the private portion and the public portion of the hybrid computing environment. The application may be placed on one or both of the private portion and the public portion of the hybrid computing environment for processing, based on the primary processing objective and based on the split preference.

Managing hybrid cloud placement policies

A system detection method and apparatus. Throughput and response time of a system are acquired. Throughput reflects the number of requests processed by the system that are received from outside the system. The response time reflects time required to process requests from outside the system. A system performance characteristic parameter based on the throughput and response time is obtained and used to describe congestion of the system to determine bottlenecks in the system by at least obtaining system performance characteristic parameter based on the throughput and the response time and conducting noise filtering on the system performance characteristic parameter. A bottleneck is determined based on the noise filtered system performance characteristic parameter. The system detection method and apparatus allow detecting a bottleneck and flow control are provided in a simple and effective way to ensure that requests are processed in time.

System detection and flow control

A non-transitory computer program product with computer readable program code for system detection and flow control. The throughput and response time of the system are acquired, where the throughput reflects the number of requests from outside the system that has been processed by the system. The response time reflects time required by the system to process requests from outside the system. Acquiring throughput and response time includes directly sampling the throughput and the response time of the system; and conducting noise filtering on the sampled throughput and response time. A system performance characteristic parameter based on the filtered throughput and response time is obtained and is used to describe congestion condition of the system to determine whether there is a bottleneck in the system. Detecting a bottleneck and flow control is provided in a simple and effective way to ensure that requests are processed in time.

There are provided a system, a method and a computer program product for operating a cloud computing infrastructure. In one embodiment, the system and method performs allocation domain modeling and provides a cloud scheduler framework that takes as input desired optimization objectives and the workload constraints and efficiently produces a placement solution that satisfies the constraints while optimizing the objectives in a way that adjusts itself depending on the objectives. As the objectives change, e.g., due to actions from system administrators or due to changes in business policies, the system optimizes itself accordingly and still produces efficient and optimized placement solutions. The system and method constructs an Allocation Domain (AD) that is a particular facet for allocating a logical entity to a physical entity. An AD is created using: variables, functional definitions (functions of variables), and a policy specification that includes a Boolean expression (of the functional definitions).

Techniques are disclosed for load balancing in networks such as those networks handling telephony applications. By way of example, such techniques direct requests associated with calls to servers in a system comprised of a network routing calls between a plurality of callers and at least one receiver wherein a load balancer sends requests associated with calls to a plurality of servers as follows. A request associated with a call, a caller, or a receiver is received, depending on the particular load balancing technique. A server is selected to receive the request. A subsequent request is received. A determination is made whether or not the subsequent request is associated with the call, the caller, or the receiver, depending on the particular load balancing technique. The subsequent request is sent to the server based on determining that the subsequent request is associated with the call, the caller, or the receiver, again depending on the particular load balancing technique.

Method and apparatus for load balancing in network based telephony application

Techniques for workload management in cloud computing infrastructures are provided. In one aspect, a method for allocating computing resources in a datacenter cluster is provided. The method includes the steps of: creating multiple, parallel schedulers; and automatically selecting a resource allocation method for each of the schedulers based on one or more of a workload profile, user requirements, and a state of the datacenter cluster, wherein an optimistic resource allocation method is selected for at least a first one or more of the schedulers and a pessimistic resource allocation method is selected for at least a second one or more of the schedulers. Due to optimistic resource allocation conflicts may arise. Methods to resolve such conflicts are also provided.

Selecting resource allocation policies and resolving resource conflicts

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Somers, NY, United States of America

Asser Nasreldin Tantawi