Management of distributed resource allocations in multi-cluster environments

Abstract

The Office of Naval Research (ONR) funds research that will benefit the U. S. Navy. In managing this research, ONR must allocate resources to achieve maximum benefits within its resource constraints. This report addresses one aspect of the resource allocation problem, namely, identifying funding profiles (normalized program funds as a function of time) for programs, or groups of programs, that provide maximum adherence to some predetermined policy. Four examples are presented in this report. The first describes selection of an overall funding profile for a group of special programs called Accelerated Research Initiatives (ARIs). The profile was selected to ensure an infusion of new ARIs proportional to total budget, and the actual turnover of ARIs resulting from use of the profile is shown. The second example shows how, with the use of quadratic programming, additional constraints could be placed upon the funding profiles, if desired, and relatively stable ARI turnover would still result. The third example describes the use of quadratic programming in an experiment in which each member of a group of potential programs had maximum funding profile flexibility while obeying the group funding ceiling constraints. The fourth example describes an extension of the methodology of the third example that could be applied to determining the profiles of every program in any funding organization. OVERALL FUNDING PROFILE FOR ACCELERATED RESEARCH INITIATIVES A. Background In 1981, ONR decided to devote a fraction of its total research budget to accelerated research programs (hereafter referred to as Accelerated Research Initiatives-ARIs). The objective of these ARIs was to concentrate resources into promising areas of research for a finite period of time in order to accelerate progress in the technical fields of the ARIs. The overall ARI program was to be increased in size until it achieved a target fraction of the total research budget, and then it would remain at approximately this (steady state) target level. The ARI selection process was designed to be competitive. Internal Navy organizations would submit proposals to ONR for these ARIs, and a fraction of these proposals would be funded (see Kostoff [ 1988] for a detailed description of this competitive process). Initially, funding profiles for the individual ARIs were not specified by ONR and were at the discretion of the proposers. Consequently, the programs that won the competition for the first few years had a wide range of profiles, with the main ONR control being the first year's funds for each ARI. After the ARI programs had been in existence for about three years, projections for the annual pool of funds available for new ARIs (hereafter referred to as the margin) as a function of time showed that the margin would be highly oscillatory over time. It would range in amplitude from near-zero in some years to large amounts in other years. The desired policy was to have the annual infusion of new ARI funds proportional to the total ARI funds available, at least in the s...

Resource State Monitoring of Service Transactions in Cloud Systems

“…In case a node owns multiple children, if these children are executed in parallel, then the resource units of the parent is allocated to its children equally; if the children are invoked sequentially, then all resource units of the parent are given to the child that will be the first one to consume the resource 5 . In case a node only has one child, all its resource unit will be assign to the child.…”

“…Our previous work [5] manages cloud resources between clusters, which is a single-layer resource management solution. To tackle the challenge faced by monitoring, we extend it to adapt to transactions in clouds, in which resource budget is allocated between services constituting transactions and/or subtransactions.…”

A Tree-Based Protocol for Enforcing Quotas in Clouds

“…Accordingly, the key challenges for managing quotas in clouds are [2]: 1) To monitor services in real time, ensuring that resource usage across all nodes remains within specified global limits. 2) To satisfy resource demands immediately provided that the global quota is not exhausted.…”

“…Quota management techniques from distributed file systems [3] or grid computing [4] are also not suitable for clouds due to the unpredictability of services' resource demands. There are also distributed solutions in which a coordinator-which can be selected either statically [5] or dynamically [2] among nodes-performs global operations such as verifying that quotas have not been exceeded. While these distributed solutions (cf.…”

“…While these distributed solutions (cf. [2] and [5]) address each of the challenges enumerated above to various extents, they require the coordinator to query every node before it can determine whether a quota has been exhausted. Querying every node causes latency and communication overheads as the number of nodes and the distances between them increase.…”