Analysis on LTE/Wi-Fi Data Offloading in Hetnets

We propose a dynamic load balancing scheme for the tele-traffic hot spot problem in cellular networks. A tele-traffic hot spot is a region of adjacent hot cells where the channel demand has exceeded a certain threshold. A hot spot is depicted as a stack of hexagonal `Rings' of cells and is classified as complete if all cells within it are hot. Otherwise it is termed incomplete. The rings containing all cold cells outside the hot spot are called `Peripheral Rings'. Our load balancing scheme migrates channels through a structured borrowing mechanism from the cold cells within the `Rings' or `Peripheral Rings' to the hot cells constituting the hot spot. A hot cell in `Ring i' can only borrow a certain fixed number of channels from adjacent cells in `Ring i+1'. We first propose a load balancing algorithm for a complete hot spot, which is then extended to the more general case of an incomplete hot spot. In the latter case, by further classifying a cell as cold safe, cold semi-safe or cold unsafe, a demand graph is constructed which describes the channel demand of each cell within the hot spot or its `Peripheral Rings' from its adjacent cells in the next outer ring. The channel borrowing algorithm works on the demand graph in a bottom up fashion, satisfying the demands of the cells in each subsequent inner ring until `Ring 0' is reached. A Markov chain model is first developed for a cell within a hot spot, the results of which are used to develop a similar model which captures the evolution of the entire hot spot region. Detailed simulation experiments are conducted to evaluate the performance of our load balancing scheme. Comparison with another well known load balancing strategy, known as CBWL, shows that under moderate and heavy tele-traffic conditions, a performance improvement as high as 12% in terms of call blockade is acheived by our load balancing scheme.

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

This paper reports on the results of a preliminary study in dynamic load balancing on an Intel Hypercube. The purpose of this research is to provide experimental data in how parallel algorithms should be constructed to obtain maximal utilization of a parallel architecture. This study is one aspect of an ongoing research project into the construction of an automated parallelization tool. This tool will take FORTRAN source as input, and construct a parallel algorithm that will produce the same results as the original serial input. The focus of this paper is on the load balancing aspect of that project. The basic idea is to reserve a certain percentage of the computation task, subdivide that percentage into arbitrarily fine tasks, and dole those small tasks out to nodes on request. Ij” the percentage is chosen correctly, then a minority of nodes should be involved in consuming the filler tasks, and the overall throughput of the job should increase as a result of the individual node efJciencies having increased. This paper will outline our approach to performing dynamic load balancing on an Intel iPSC/2. We take the view that the problem of load balancing is really a problem of dividing a “computational task” into smaller components, each of roughly equal complexity, and each an independent event. After this is done, the components of the task can be sent to a node for execution. The key to an optimally balanced load across all computational nodes is the ability to form a statistical profile of the individual components of each computational task. This statistical profile will determine an initial sequence of execution. Our experience indicates that a speedup on the order of 80% is achievable with the judicious use of profiled load balancing. During the process of execution, the initial profile will be altered according to the actual behavior exhibited by the nodes. The difference between the actual and expected performance will be used to determine how much additional time should be devoted to altering the current execution schedule. Currently, our work involves statically setting the load balancing parameters. Our load balancing system determines the execution schedule

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

ABSTRACTMesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among proces-sors on a parallel machine. We present a novel method todynamically balance the processor workloads with a globalview. This paper presents, for the first time, the imple-mentation and integration of all major components withinour dynamic load balancing strategy for adaptive grid cal-culations. Mesh adaption, repartitioning, processor assign-ment, and remapping are critical components of the frame-work that must be accomplished rapidly and efficiently soas not to cause a significant overhead to the numerical sim-ulation. Previous results indicated that mesh repartitioningand data remapping are potential bottlenecks for perform-ing large-scale scientific calculations. We resolve these issuesand demonstrate that our framework remains viable on alarge number of processors.1 INTRODUCTIONDynamic mesh adaption on unstructured grids is a power-ful tool for computing unsteady three-dimensional problemsthat require grid modifications to efficiently resolve solu-tion features. By locally refining and coarsening the meshto capture flowfield phenomena of interest, such proceduresmake standard computational methods more cost effective.Highly refined meshes are required to accurately captureshock waves, contact discontinuities, vortices, and shear lay-ers. Local mesh adaption provides the opportunity to obtainsolutions that are comparable to those obtained on globally-refined grids but at a much lower cost.Unfortunately, the adaptive solution of unsteady prob-lems causes load imbalance among processors on a paral-lel machine. This is because the computational intensity isboth space and time dependent. An efficient parallel imple-mentation of such methods is extremely difficult to achieve,primarily because of the dynamically-changing nonuniformgrid. Various methods on dynamic load balancing have beenreported to date [5-9,11-14,16-18,24-26]; however, most ofthem either lack a global view of loads across processors orProceedings of the 9th ACM Symposium on Parallel Al-gorithms and Architectures, Newport, Rhode Island, June22-25, 1997.do not apply their techniques to realistic large-scale appli-cations.Figure 1 depicts our framework for parallel adaptive flowcomputation. It consists of a flow solver and a mesh adap-tor, with a partitioner and a remapper that load balancesand redistributes the computational mesh when necessary.Our goal is to build a portable system for efficiently per-forming adaptive large-scale flow calculations in a parallelmessage-passing environment. The mesh is first partitionedand mapped among the available processors. The flow solverthen runs for several iterations, updating solution variables.Once an acceptable solution is obtained, the mesh adaptionprocedure is invoked. It first targets edges for coarseningand refinement based on an error indicator computed fromthe flow solution. The old mesh is then coarsened, resultingin a smaller grid. Since edges have already been marked forrefinement, it is possible to exactly predict the new meshbefore actually performing the refinement step. Programcontrol is thus passed to the load balancer at this time. Aquick evaluation step determines if the new mesh will be sounbalanced as to warrant a repartitioning. If the currentpartitions will remain adequately load balanced, control mpassed back to the subdivision phase of the mesh adaptor.Otherwise, a repartitioning procedure is used to divide thenew mesh into subgrids. The new partitions are then reas-signed to the processors in a way that minimizes the costof data movement. If the remapping cost is less than thecomputational gain that would be achieved with balancedpartitions, all necessary data is appropriately redistributed.Otherwise, the new partitioning is discarded. The computa-tional mesh is then actually refined and the flow calculationis restarted.Notice from the framework in Fig. 1 that splitting themesh refinement step into two distinct phases of edge mark-ing and mesh subdivision allows the subdivision phase tooperate in a more load balanced fashion. In addition, sincedata remapping is performed before the mesh grows in sizedue to refinement, a smaller volume of data is moved. Thiscan lead to a potentially significant savings in the redistribu-tion cost. The load balancer also balances the computationalload for the flow solver while reducing the runtime commu-nication. This is important because flow solvers are usuallyseveral times more expensive than mesh adaptors. In anycase, it is obvious that mesh adaption, repartitioning, pro-cessor assignment, and remapping are critical componentsof the framework and must be accomplished rapidly and ef-ficiently so as not to cause a significant overhead to the flowcomputation.

Cellular wirelesses heterogeneous networks (HetNets) have become an important part of the growth of the telecom industry. The rapid growth of cellular networks and widespread use of wireless services have led to a huge increase in energy use. This makes it hard to make cellular networks that use less energy and last longer. In HetNets, load balancing (LB) and energy balance have a big effect on cell range extension (CRE) and network lifetime extension (NLE). optimal energy efficient LB in wireless cellular HetNets is presented in this paper using a self-tuning algorithm. Several self-tuning methods for HetNets have been developed that are driven by the quality of experience (QoE). Unlike earlier heuristic-based approaches, the suggested technique uses a gradient ascending method to ensure that parameter modifications constantly increase overall system QoE, LB, throughput, and energy efficiency. The planned network's packet loss rate is extremely low. The suggested and contemporary models are tested in an experimental investigation, demonstrating the importance of the suggested model in comparison to other contemporary methodologies.

Load balancing (LB) aims at tackling the hot-spot cell problem caused by uneven load distribution and resources shortage. Traditional LB schemes mainly focus on the load reduction of the hot-spot cell, while some partner cells might suffer from aggravating load after receiving inappropriate traffic, which triggers ripple LB and reduces the LB efficiency. To deal with this problem, a self- optimizing LB scheme in fixed relay station (RS) cellular networks is proposed. In the scheme, partner cells share the load jointly to guarantee each partner has similar load after LB. Meanwhile, the partner response mechanism considers the resources usage of partner's RSs to further reduce the ripple LB. Simulation results show that the proposed scheme can effectively reduce the number of ripple LB and reduce the call blocking rate.

This paper proposes a reliable routing protocol called LBR for wireless sensor networks, which find high-quality paths with considering load balance among nodes. The LBR protocol transmits messages through a reliable path to reduce the packet loss rate. To the problem of increasing data transmission reliability, our routing protocol uses ETX as a metric for estimating link quality. By using ETX, the LBR protocol can choose good links in the network. In addition to the above, the problem of load balance is also considered in our protocol. By taking this into account, the LBR protocol avoid routing through the congested nodes and balance load among nodes better. This results in increasing the lifetime of the network. The simulation results using the Qualnet simulator showed that the proposed protocol outperforms other protocols in terms of packet delivery ratio, end-to-end delay and the efficiency of load balance.KeywordsWireless sensor networksload balancelink qualityrouting protocol

In this paper we study the problem of dynamic load balancing in broadcast WDM networks by retuning a subset of transceivers in response to changes in the overall traffic pattern. Assuming an existing wavelength assignment and some information regarding the new traffic demands, we present two approaches to obtaining a new wavelength assignment such that (a) the new traffic load is balanced across the channels, and (b) the number of transceivers that need to be retuned is minimized. The latter objective is motivated by the fact that tunable transceivers take a non-negligible amount of time to switch between wavelengths during which parts of the network are unavailable for normal operation. Our main contribution is a new approximation algorithm for the load balancing problem that provides for tradeoff selection, using a single parameter, between the two conflicting goals. This algorithm leads to a scalable approach to reconfiguring the network since, in addition to providing guarantees in terms of load balancing, the expected number of retunings scales with the number of channels, not the number of nodes in the network.

P2P Grids could solve large-scale scientific problems by using geographically distributed heterogeneous resources. However, a number of major technical obstacles must be overcome before this potential can be realized. One critical problem to improve the effective utilization of P2P Grids is the efficient load balancing. This chapter addresses the above-mentioned problem by using a distributed load balancing policy. In this chapter, we propose a P2P communication mechanism, which is built to deliver varied information across heterogeneous Grid systems. Basing on this P2P communication mechanism, we develop a load balancing policy for improving the utilization of distributed computing resources. We also develop a P2P resource monitoring system to capture the dynamic resource information for the decision making of load balancing. Moreover, experimental results show that the proposed load balancing policy indeed improves the utilization and achieves effective load balancing.

Mobile data offloading is a promising approach to alleviate network congestion and enhance quality of service (QoS) in mobile cellular networks. In this paper, we investigate the economics of mobile data offloading through third-party WiFi or femtocell access points (APs). Specifically, we consider a market-based data offloading solution, where macrocellular base stations (BSs) pay APs for offloading traffic. The key questions arising in such a marketplace are following: (i) how much traffic should each AP offload for each BS? and (ii) what is the corresponding payment of each BS to each AP? We answer these questions by using the non-cooperative game theory. In particular, we define a multi-leader multi-follower data offloading game (DOFF), where BSs (leaders) propose market prices, and accordingly APs (followers) determine the traffic volumes they are willing to offload. We characterize the subgame perfect equilibrium (SPE) of this game, and further compare the SPE with two other classic market outcomes: (i) the market balance (MB) in a perfect competition market (i.e., without price participation), and (ii) the monopoly outcome (MO) in a monopoly market (i.e., without price competition). Our results analytically show that (i) the price participation (of BSs) will drive market prices down, compared to those under the MB outcome, and (ii) the price competition (among BSs) will drive market prices up, compared to those under the MO outcome.

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks, and calls for novel network architectures and communication solutions. Data offloading through third-party WiFi or femtocell access points (APs) can effectively alleviate the cellular network congestion in a low operational and capital expenditure. This solution requires the cooperation and agreement of mobile cellular network operators (MNOs) and AP owners (APOs). In this paper, we model and analyze the interaction among one MNO and multiple APOs (for the amount of MNO's offloading data and the respective APOs' compensations) by using the Nash bargaining theory. Specifically, we introduce a one-to-many bargaining game among the MNO and APOs, and analyze the bargaining solution (game equilibrium) systematically under two different bargaining protocols: (i) sequential bargaining, where the MNO bargains with APOs sequentially, with one APO at a time, in a given order, and (ii) concurrent bargaining, where the MNO bargains with all APOs concurrently. We quantify the benefits for APOs when bargaining sequentially and earlier with the MNO, and the losses for APOs when bargaining concurrently with the MNO. We further study the group bargaining scenario where multiple APOs form a group bargaining with the MNO jointly, and quantify the benefits for APOs when forming such a group. Interesting, our analysis indicates that grouping of APOs not only benefits the APOs in the group, but may also benefit some APOs not in the group. Our results shed light on the economic aspects and the possible outcomes of the MNO/APOs interactions, and can be used as a roadmap for designing policies for this promising data offloading solution.

Adaptive passenger-finding recommendation system for taxi drivers with load balancing problem

The wireless networks have a very bright future in networks and communications because of which they have taken a high interest of the researchers. As the users increased the purpose to use MANETs, they also became more diverse and wide due to which better performance is needed in MANETs. QoS is needed for applications for an efficient communication and load balancing is a feature in the routing protocol that can help in a better use of the resources and can help to increase the performance of the network. We propose a new approach for load balancing in AOMDV routing protocol for MANETs that can enhance the network performance by selecting paths using the temporal load on the intermediate nodes and by distributing the load amongst the free nodes while transmission of data, which is proved by simulations in NS-2.

Despite the fast evolution of Distributed Shared Memory (DSM) systems, the load balance problem has not received enough attentions. This problem arises naturally after multithreading was introduced to DSM systems few years ago. The cognizance of it would bring us a significant improvement in system performance. In this paper, we address it by proposing and experimentally evaluating a load balancing method called Dependence-Driven Load Balancing (DDLB) that is dedicated for multithreaded DSM systems. The most attractive feature of this method is to take thread dependence into account in making decisions for migration. In contrast to existing thread scheduling works which for the most part have relied on the information supplied before execution, we require no a priori information. Typically, DDLB embraces three policies, i.e. transfer policy, location policy and selection policy, and applies affinity scheduling. Finally, from the experimental results, the performance of the system with load balancing is improving evidently.