Dynamic Resource Allocation Algorithm Research Articles

Dynamic resource allocation for CDMA‐TDD indoor wireless systems

AbstractFuture wireless communication systems are expected to provide a broad range of multimedia services that have a significant traffic asymmetry between uplink and downlink. The code division multiple access‐time division duplex (CDMA‐TDD) system is a promising solution to cope with the problem of traffic asymmetry. However, the TDD system is subject to inter‐cell interference compared to frequency division duplex (FDD) system. Since both uplink and downlink share the same frequency in TDD, uplink and downlink may interfere each other especially when neighboring cells require different rates of traffic load. Thus, the resource allocation among cells is an important issue in TDD. In this paper, the resource allocation in the CDMA‐TDD is formulated as a mixed integer programming (MIP) problem. A dynamic resource allocation algorithm (DRAA) is provided that effectively solves the traffic asymmetry problem. The MIP problem is also solved by a well‐known branch and bound procedure. Both the crossed slot and non‐crossed slot allocation are examined and compared to the DRAA. Computational result shows that proposed DRAA gives a good performance as the traffic asymmetry increases between the uplink and downlink. Copyright © 2003 John Wiley & Sons, Ltd.

Improving Efficiency of Timeslot Assignment for Non-realtime Data in a DVB-RCS Return Link: Modeling and Algorithm

This paper presents a dynamic resource allocation algorithm with multi-frequency time-division multiple access for the return link of interactive satellite multimedia networks such as digital video broadcasting return channel via satellite systems. The proposed timeslot assignment algorithm, called the very efficient dynamic timeslot assignment (VEDTA) algorithm, gives an optimal assignment plan within a very short period. The optimality and computational efficiency of this algorithm demonstrate that it will be useful in field applications.

Dynamic resource allocation for quality of service on a PON with home networks

Earlier efforts on optical access concentrated on the design of PONs for the collection and distribution portion of the access network. A possible evolution scenario for these types of access networks could be the SuperPON system. The SuperPON system exploits all possible upgrades of an FSAN APON system. In these networks the optical hardware is very simple, but a media access control protocol is needed for upstream traffic control. Even so, the role of communications is already well established in the office environment, thanks to networking innovations such as the Ethernet LAN. With the development of cheap, affordable broadband communications and the increasing complexity of consumer goods, it seems natural to extend the network into homes. As home area network application ever increases, we consider connectivity between the access network and the home network, which generates multiple traffic, in order to design a MAC protocol over the SuperPON access network with home networks. Global FIFO is quite simple, and allows dynamic upstream bandwidth allocation on the basis of a request-and-permit mechanism on the APON architecture. It has good bandwidth efficiency; however, being cell-based, it does not consider multiple traffic types from home networks. In this article we describe and analyze a new dynamic MAC resource allocation algorithm called multiple queue-FIFO that can achieve good performance under the SuperPON access network in the home network environment.

Development, Analysis, and Verification of a Parallel Hybrid Dataflow Computer Architectural Framework and Associated Load-Balancing Strategies and Algorithms via Parallel Simulation

This paper first presents the requirements for and the architectural framework of a type of real-time parallel dataflow computer architecture, specifically, a Hybrid Data/Command Driven Architecture (HDCA). Application of such an architecture is discussed. Goals, strategies, and algorithms for both static load-balancing and dynamic resource allocation on an HDCA system are presented. A parallel simulation of an HDCA system employing the presented static load-balancing and dynamic resource allocation algorithms is developed and presented. The parallel simulation was executed on a parallel computer system for increased fidelity. Analysis of simulation results for two example applications of the architecture allowed verification of design goals and correct performance of the HDCA architectural framework and associated static load-balancing and dynamic resource allocation strategies and algorithms.

Joint optimal channel base station and power assignment for wireless access

The provision of personal communication services is the goal of the evolution of integrated communication systems. The fundamental problem underlying any phase (hand-off, new connection, etc.) of a dynamic resource allocation algorithm in a wireless network is to assign transmission powers, forward (downstream) and reverse (upstream) channels, and base stations such that every mobile of the system can establish a connection. Each one of these problems separately has been studied extensively. We consider the joint problem in a system with two base stations. An algorithm that achieves the optimal assignment is provided. It involves the computation of a maximum matching in a graph that captures the topological characteristics of the mobile locations. The traffic capacities, in terms of expected number of connections per channel, of the forward and reverse channel are obtained and compared, for both cases of power control and nonpower control. It turns out that when the transmission power is fixed, the capacities of the forward and reverse channel are different, while when power control is allowed they are the same. For systems with two mobiles the capacities of the forward and reverse channels are studied analytically. Finally, several versions of the two-way channel assignment problem are studied.

An R & D planning and control servosystem: A case study*

AbstractThis paper describes a planning and control system (PACS) to aid R & D decision‐making which has been developed and used at the Monsanto Company, St. Louis, Missouri, U.S.A. The planning model portion of the integrated system is a two‐dimensional dynamic programming resource allocation algorithm which performs project selection, budgeting and scheduling. Behavioral parameters that can be used to simulate certain organizational factors are included in the planning model. The control model portion of the integrated system develops cost‐effectiveness measures of variances between actual and planned performances for ongoing projects over time, which provide early warnings of pending project failures and guide control action. Applications of the system at Monsanto indicate that it significantly aids the development of satisfactory organizational decisions and also provides a laboratory for pretesting decisions.

Dynamic Effective Resource Allocation Based on Cloud Computing Learning Model

In cloud computing field, a service-provider offers large number of resources for customers with a relatively low cost. However, any resource allocation model has to consider computational resources as well as network resources to accurately reflect practical demands. In order to rationally allocate the limited resources to users and improve resource utilization and energy efficiency of cloud systems as much as possible, a dynamic effective resource allocation algorithm based on cloud computing learning model is proposed. According to the dynamic effective resources allocation, Quality of Service Standards Framework is adopted to obtain cloud service management mechanism. In addition, the resources auction strategy and dynamic bilateral game strategy are also adopted to effectively leverage the interest-relationship between the user and the cloud provider so as to assign resources to these users with bigger demand. In the computation of cloud system and resources allocation of storage tasks, the cloud-based learning model is used to balance the demand of energy and resources so as to achieve optimal system efficiency. Simulation experiment results show that our proposed algorithm can assign more reasonably resource and energy for system computation and storage tasks and the performance of the algorithm is superior to other comparison algorithms on the utilization of resource and energy