Balanced Resource Allocation

Resource management is a key issue in execution of business processes and tasks. While control flow structure is given by underlying business process models, assignment of process activities to resources is a runtime task that includes optimization questions. Thus reasoning about system optimization is based on precise specification of resources and tasks. However, resource models previously employed in business process management lacked preciseness to enable sound analysis and optimization. In this article a modeling method that contains necessary information will be utilized and formalized in order to pose and solve optimization questions. Furthermore assignment of human resources is combined with further education, thus an optimized resource assignment will foster balanced further education. In consequence performance peaks of single resources will be alleviated and risk of failure can be diminished

Optimal Resource Allocation in Random Networks with Transportation Bandwidths

We apply statistical physics to study the task of resource allocation in random sparse networks with limited bandwidths for the transportation of resources along the links. Useful algorithms are obtained from recursive relations. Bottlenecks emerge when the bandwidths are small, causing an increase in the fraction of idle links. For a given total bandwidth per node, the efficiency of allocation increases with the network connectivity. In the high connectivity limit, we find a phase transition at a critical bandwidth, above which clusters of balanced nodes appear, characterised by a profile of homogenized resource allocation similar to the Maxwell's construction.Comment: 28 pages, 11 figure

Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network

Load imbalance, together with inefficient utilization of system resource, constitute major factors responsible for poor overall performance in Long Term Evolution (LTE) network. In this paper, a novel scheme of joint dynamic resource allocation and load balancing is proposed to achieve a balanced performance improvement in 3rd Generation Partnership Project (3GPP) LTE Self-Organizing Networks (SON). The new method which aims at maximizing network resource efficiency subject to inter-cell interference and intra-cell resource constraints is implemented in two steps. In the first step, an efficient resource allocation, including user scheduling and power assignment, is conducted in a distributed manner to serve as many users in the whole network as possible. In the second step, based on the resource allocation scheme, the optimization objective namely network resource efficiency can be calculated and load balancing is implemented by switching the user that can maximize the objective function. Lagrange Multipliers method and heuristic algorithm are used to resolve the formulated optimization problem. Simulation results show that our algorithm achieves better performance in terms of user throughput, fairness, load balancing index and unsatisfied user number compared with the traditional approach which takes resource allocation and load balancing into account, respectively

Load Balanced Dynamic Resource Allocation for MTC Relay

A Load Balancing Relay Algorithm (LBRA) was proposed to solve the unfair spectrum resource allocation in the traditional mobile MTC relay. In order to obtain reasonable use of spectrum resources, and a balanced MTC devices (MTCDs) distribution, spectrum resources are dynamically allocated by MTCDs regrouped on the MTCD to MTC gateway link. Moreover, the system outage probability and transmission capacity are derived when using LBRA. The numerical results show that the proposed algorithm has better performance in transmission capacity and outage probability than the traditional method. LBRA had an increase in transmission capacity of about 0.7dB, and an improvement in outage probability of about 0.8dB with a high MTCD density.Comment: 6pages, 5figure

OPTIMIZING LOCAL BUDGET BALANCING IN ROMANIA

The importance of the local public finance is growing in accordance with the increasing proportion of the decentralization process. The mechanism of resource allocation, and especially the allocation criteria used, constitutes subjects of debate. Our objective pursued is to assess whether the avoidance of the first step for balancing the allocation of funds can provide enhanced fairness in balancing the local budgets across the country. Local budgets in Romania receive significant resources from the state budget in the form of amounts and quotas distributed from certain taxes, which are revenues for the state budget. Some of these amounts are designed to balance the local budgets. The distribution of funds from the state budget to the local budgets requires two steps. Firstly, the amounts are divided by county, secondly, these amounts are directed within the county especially towards localities which have a lower financial standing. Given the significant disparities between counties, we believe that this mechanism does not ensure fairness in the allocation because the funds distributed according to the first step may not use fair criteria to meet the requirements for balanced local budgets. Therefore, we intend to simulate a balanced allocation of national funds for eliminating the first step that produces the most significant inequities. Direct application of the second step of allocation, with its two phases, will provide more funds serving those local administrative units for the income tax per capita is lower than the national average. Comparing the values allocated for the year 2011 with those obtained in the simulation we will examine changes that occur after the application of this method which seems to be more equitable and appropriate. This work was supported by CNCSIS-UEFISCSU, project number PNII-IDEI 1780/2008local budget, budgetary resources, budget balancing, resource repartition

Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environment

Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree

Resource Allocation for Query Optimization in Data Grid Systems: Static Load Balancing Strategies

International audienceResource allocation is one of the principal stages of relational query processing in data grid systems. Static allocation methods allocate nodes to relational operations during query compilation. Existing heuristics did not take into account the multi-queries environment, where some nodes may become overloaded because they are allocated to too many concurrent queries. Dynamic resource allocation mechanisms are currently developed to modify the physical plan during query execution. In fact, when a node is detected to be overloaded, some of the operations on it will migrate. However, if the resource contention is too heavy in the initial execution plan, the operation migration cost may be very high. In this paper, we propose two load balancing strategies adopted during the static resource allocation phase, so that the workload is balanced at the beginning, the operation migration cost is decreased during the query execution, and therefore the average response time is reduced