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Experimental Performances Analysis of Load Balancing Algorithms in IEEE 802.11  [PDF]
Hamdi Salah,Soudani Adel,Tourki Rached
Computer Science , 2009,
Abstract: In IEEE 802.11, load balancing algorithms (LBA) consider only the associated stations to balance the load of the available access points (APs). However, although the APs are balanced, it causes a bad situation if the AP has a lower signal length (SNR) less than the neighbor APs. So, balance the load and associate one mobile station to an access point without care about the signal to noise ratio (SNR) of the AP cause possibly an unforeseen QoS, such as the bit rate, the end to end delay, the packet loss. In this way, we study an improvement load balancing algorithm with SNR integration at the selection policy.
Locally Optimal Load Balancing  [PDF]
Laurent Feuilloley,Juho Hirvonen,Jukka Suomela
Computer Science , 2015,
Abstract: This work studies distributed algorithms for locally optimal load-balancing: We are given a graph of maximum degree $\Delta$, and each node has up to $L$ units of load. The task is to distribute the load more evenly so that the loads of adjacent nodes differ by at most $1$. If the graph is a path ($\Delta = 2$), it is easy to solve the fractional version of the problem in $O(L)$ communication rounds, independently of the number of nodes. We show that this is tight, and we show that it is possible to solve also the discrete version of the problem in $O(L)$ rounds in paths. For the general case ($\Delta > 2$), we show that fractional load balancing can be solved in $\operatorname{poly}(L,\Delta)$ rounds and discrete load balancing in $f(L,\Delta)$ rounds for some function $f$, independently of the number of nodes.
Load Balancing in Grid Computing
Belabbas Yagoubi,Hadj Tayeb Lilia,Halima Si Moussa
Asian Journal of Information Technology , 2012,
Abstract: Grid computing has recently emerged as popular platforms for deploying large-scale and resource-intensive applications. This kind of infrastructure raises challenging issues in many areas of computer science and especially in the area of distributed computing. One motivation of Grid computing is to aggregate the power of widely distributed resources and provide non-trivial services to users. To achieve this goal, large collaborative efforts are currently underway to provide the necessary software infrastructure. Resource management is an essential function provided at the service level of this software infrastructure. To improve the global throughput of these environments, workloads have to be evenly balanced among the available resources. Several load balancing strategies and algorithms have been proposed in this area. Most of them were developed in mind, assuming homogeneous set of sites linked with homogeneous and fast networks. However for computational grids we must address main new issues, like: heterogeneity, autonomy, dynamicity and so forth. This paper deals with a survey for grid load balancing problem. First, the essential aspects of load balancing system are overviewed to provide a global image of the load balancing process. Then specificities and challenges for grid are discussed and compared to traditional distributed systems. Finally, the state of the art of current research and some popular algorithms are outlined.
Load Balancing in Optical Grids  [PDF]
Mohamed Abouelela,Mohamed El-Darieby
International Journal of Grid Computing & Applications , 2012,
Abstract: This collaboration of geographically distributed domains in multi-domain optical grid environment should be done in a way that maintains the privacy of each participant domain. This calls for a new load balancing hierarchical approach to deal with such environments. In this paper, we propose two load balancing techniques within this hierarchical approach: Network Aware Divisible Load Algorithm (NADLA) and Genetic Algorithm based Load Distribution (GA-LD). Both algorithms are considered as Network Aware. Simulation results show the scalability and feasibility of the proposed approach and the advantages of the two proposed techniques compared to the classical Divisible Load Algorithm (DLA).
Quasirandom Load Balancing  [PDF]
Tobias Friedrich,Martin Gairing,Thomas Sauerwald
Computer Science , 2010, DOI: 10.1137/100799216
Abstract: We propose a simple distributed algorithm for balancing indivisible tokens on graphs. The algorithm is completely deterministic, though it tries to imitate (and enhance) a random algorithm by keeping the accumulated rounding errors as small as possible. Our new algorithm surprisingly closely approximates the idealized process (where the tokens are divisible) on important network topologies. On d-dimensional torus graphs with n nodes it deviates from the idealized process only by an additive constant. In contrast to that, the randomized rounding approach of Friedrich and Sauerwald (2009) can deviate up to Omega(polylog(n)) and the deterministic algorithm of Rabani, Sinclair and Wanka (1998) has a deviation of Omega(n^{1/d}). This makes our quasirandom algorithm the first known algorithm for this setting which is optimal both in time and achieved smoothness. We further show that also on the hypercube our algorithm has a smaller deviation from the idealized process than the previous algorithms.
Threshold Load Balancing in Networks  [PDF]
Martin Hoefer,Thomas Sauerwald
Computer Science , 2013,
Abstract: We study probabilistic protocols for concurrent threshold-based load balancing in networks. There are n resources or machines represented by nodes in an undirected graph and m >> n users that try to find an acceptable resource by moving along the edges of the graph. Users accept a resource if the load is below a threshold. Such thresholds have an intuitive meaning, e.g., as deadlines in a machine scheduling scenario, and they allow the design of protocols under strong locality constraints. When migration is partly controlled by resources and partly by users, our protocols obtain rapid convergence to a balanced state, in which all users are satisfied. We show that convergence is achieved in a number of rounds that is only logarithmic in m and polynomial in structural properties of the graph. Even when migration is fully controlled by users, we obtain similar results for convergence to approximately balanced states. If we slightly adjust the migration probabilities in our protocol, we can also obtain fast convergence to balanced states.
The Study On Load Balancing Strategies In Distributed Computing System  [PDF]
Md. Firoj Ali,Rafiqul Zaman Khan
International Journal of Computer Science and Engineering Survey , 2012,
Abstract: A number of load balancing algorithms were developed in order to improve the execution of a distributed application in any kind of distributed architecture. Load balancing involves assigning tasks to each processor and minimizing the execution time of the program. In practice, it would be possible even to execute the applications on any machine of worldwide distributed systems. However, the ‘distributed system’ becomes popular and attractive with the introduction of the web. This results in a significant performance improvement for the users. This paper describes the necessary, newly developed, principal concepts for several load balancing techniques in a distributed computing environment. This paper also includes various types of load balancing strategies, their merits, demerits and comparison depending on certain parameters.
Load Balancing and Parallelism in Cloud Computing
Pragati Priyadarshinee,Pragya Jain
International Journal of Engineering and Advanced Technology , 2012,
Abstract: Large-scale heterogeneous distributed computing environments (such as Computational Grids and Clouds) offer the promise of access to a vast amount of computing resources at a relatively low cost. In order to ease the application development and deployment on such complex environments, high-level parallel programming languages exist that need to be supported by sophisticated runtime systems. The anticipated uptake of Cloud computing, built on well-established research in Web Services, networks, utility computing, distributed computing and virtualization, will bring many advantages in cost, flexibility and availability for service users. These benefits are expected to further drive the demand for Cloud services, increasing both the Cloud’s customer base and the scale of Cloud installations. This has implications for many technical issues in Service Oriented Architectures and Internet of Services (IoS)-type applications; including fault tolerance, high availability and scalability. Central to these issues is the establishment of effective load balancing techniques. It is clear the scale and complexity of these systems makes centralized assignment of jobs to specific servers infeasible; requiring an effective distributed solution.
Enhanced Genetic Algorithm Based Load Balancing in Grid  [PDF]
Sandip Kumar Goyal,Manpreet Singh
International Journal of Computer Science Issues , 2012,
Abstract: Load Balancing (LB) has been an increasingly important issue for handling computational intensive task in a grid system. By developing strategies that can schedule such tasks to resources in a way that balance out the load, the total processing time will be reduced with improved resource utilization. In this paper, an Enhanced Genetic Algorithm (EGA) is proposed for achieving task scheduling with load balancing. The simulation results show that proposed algorithm yields better performance when compared with other traditional heuristic approaches.
Balancing Energy Consumption in Clustered Wireless Sensor Networks  [PDF]
Tony Ducrocq,Micha?l Hauspie,Nathalie Mitton
ISRN Sensor Networks , 2013, DOI: 10.1155/2013/314732
Abstract: Clustering in wireless sensor networks is an efficient way to structure and organize the network. It aims at identifying a subset of nodes within the network and binding it to a leader (i.e., cluster head). The leader becomes in charge of specific additional tasks like gathering data from all nodes in its cluster and sending them using a longer range communication to a sink. As a consequence, a cluster head exhausts its battery more quickly than regular nodes. In this paper, we present four variants of BLAC, a novel battery level aware clustering family of schemes. BLAC considers the battery level combined with another metric to elect the cluster-head. The cluster-head role is taken alternately by each node to balance energy consumption. Due to the local nature of the algorithms, keeping the network stable is easier. BLAC aims at maximizing the time with all nodes alive to satisfy the application requirements. Simulation results show that BLAC improves the full network lifetime three times more than the traditional clustering schemes by balancing energy consumption over nodes and still deliveres high data ratio. 1. Introduction Multihop wireless sensor networks (MWNs) consist of sets of mobile wireless nodes without support of any preexisting fixed infrastructure. Such large scale wireless sensor networks offer great application perspectives. Wireless sensors are often tiny devices with hardware constraints (low memory storage, low computational resources) that rely on battery. Sensor networks thus require energy-efficient algorithms to make them work properly in a way that suits their hardware features and application requirements. In this paper, we focus on a given application defined by the ANR BinThatThinks (http://binthatthink.inria.fr) project. The project aims to ease the collect and recycling of waste and reduce its cost through the use of wireless sensors placed on dustbins. Dustbins are also equipped with GPRS chips for long range communications. In this paper, our goal is to propose a novel clustering algorithm for wireless sensor networks in which each sensor node sends its data to its cluster head (potentially through Multihop paths) based on the context of the BinThatThinks project. In this context, cluster heads collect data from all sensors in their cluster and send them through their GPRS link. Since activating the GPRS consumes more energy than peer-to-peer communications (as shown in Table 1, Section 6), each node should take the cluster head role in turn in order to allow the network to be operational as long as possible without too
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