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 Balancing Hashing for Geographic Hash Tables

In this paper, we address the problem of balancing the network traffic load generated when querying a geographic hash table. State-of-the-art approaches can be used to improve load balancing by changing the underlying geo-routing protocol used to forward queries in the geographic hash table. However, this comes at the expense of considerably complicating the routing process, which no longer occurs along (near) straightline trajectories, but requires computing complex geometric transformations. Thus, current load balancing approaches are impractical in application scenarios where the nodes composing the geographic hash table have limited computational power, such as in most wireless sensor networks. In this paper, we propose a novel approach to solve the traffic load balancing problem in geographic hash tables: instead of changing the (near) straight-line geo-routing protocol used to send a query from the node issuing the query (the source) to the node managing the queried key (the destination), we propose to "reverse engineer" the hash function so that the resulting destination density, when combined with a given source density, yields a perfectly balanced load distribution. We first formally characterize the desired destination density as a solution of a complex integral equation. We then present explicit destination density functions (taken from the family of Beta distributions) yielding quasi-perfect load balancing under the assumption of uniformly distributed sources. Our theoretical results are derived under an infinite node density model. In order to prove practicality of our approach, we have performed extensive simulations resembling realistic wireless sensor network deployments showing the effectiveness of our approach in considerably improving load balancing. Differently from previous work, the load balancing technique proposed in this paper can be readily applied in geographic hash tables composed of computationally constrained nodes, as it is typically the case in wireless sensor networks

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations

A Submodular Optimization Framework for Outage-Aware Cell Association in Heterogeneous Cellular Networks

In cellular heterogeneous networks (HetNets), offloading users to small cell base stations (SBSs) leads to a degradation in signal to interference plus noise ratio (SINR) and results in high outage probabilities for offloaded users. In this paper, we propose a novel framework to solve the cell association problem with the intention of improving user outage performance while achieving load balancing across different tiers of BSs. We formulate a combinatorial utility maximization problem with weighted BS loads that achieves proportional fairness among users and also takes into account user outage performance. A formulation of the weighting parameters is proposed to discourage assigning users to BSs with high outage probabilities. In addition, we show that the combinatorial optimization problem can be reformulated as a monotone submodular maximization problem and it can be readily solved via a greedy algorithm with lazy evaluations. The obtained solution offers a constant performance guarantee to the cell association problem. Simulation results show that our proposed approach leads to over 30% reduction in outage probabilities for offloaded users and achieves load balancing across macrocell and small cell BSs

A novel weight-assignment load balancing algorithm for cloud applications

Load balancing dynamically optimizes cloud resources and performance, and enhances the performance ofapplications deployed on cloud. We have chosen to investigate the class of cloud deployed web-based threetier business applications. There is a problem with load balancing for this class of applications when theysuffer from overload due to sudden flash crowds and resource failures. We propose a novel weight assignmentload balancing algorithm to address this problem. Our approach utilises five carefully selected server metricsto efficiently distribute load among virtual machines. First, we validated our novel algorithm by comparing itwith a baseline load-balancing algorithm and round-robin algorithm. Then, we experimentally evaluated oursolution, by varying the number of user requests and carefully measuring response times and throughput. Theexperiments were performed on a private cloud environment testbed running OpenStack. Our experimentalresults show that our approach improves the response time of user requests by 5.66% compared to the baselinealgorithm and 15.15% compared to round-robin algorithm in flash crowd scenario. In addition, while handlingbetween 110% to 190% overload, our approach improved response times in all scenarios. Consequently, ournovel algorithm outperforms the baseline and round-robin algorithms in overload conditions

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids