International Business Machines Corporation

Tracy Jay Kimbrel

Maxim I Sviridenko

Jayram S Thathachar

Baruch M Schieber

Robert Krauthgamer

Maria Minkoff

Asser Nasreldin Tantawi

Malgorzata Steinder

Hubertus Franke

Robert William Wisniewski

Tarun Kumar

Orran Yaakov Krieger

Bryan Savoye Rosenburg

Marc Alan Auslander

Kevin McKenzie

David Joel Edelsohn

Richard D Prewitt, Jr

Debra Tomkowid

Robert Krauthgarner

An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Dynamic application placement under service and memory constraints

A system and method that provides an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences can not be meet, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.

Scheduling heterogeneous partitioned resources with sharing constraints

A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server 'farm' given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server 'farm' problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

Dynamic resource allocation using known future benefits

A method for server allocation in a Web server 'farm' is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of 'discounting the future'. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

Dynamic resource allocation using projected future benefits

In a computer system, a method and apparatus for scheduling activities' access to a resource with minimal involvement of the kernel of the operating system. More specifically, a &ldquo;next bid&rdquo; is maintained, and this parameter identifies the highest bid for the resource by any activity not currently accessing the resource. The accessing activity then compares its bid, which can be time varying, with the &ldquo;next bid&rdquo; to determine whether it should release the resource to another activity. The &ldquo;next bid&rdquo; can be accessed without any system calls to the operating system. This allows the activity to determine whether to relinquish control to the system without the necessity of communication between the two. Likewise, the operating system can access the bid of the accessing activity without explicit communication. This allows the system to determine whether to preempt the accessing activity without the necessity of communication between the two.

Locally made, globally coordinated resource allocation decisions based on information provided by the second-price auction model

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Tracy Jay Kimbrel