The load balancing problem in OTIS-Hypercube interconnection networks

An interconnection network architecture that promises to be an interesting option for future-generation parallel processing systems is the OTIS (Optical Transpose Interconnection System) optoelectronic architecture. Therefore, all performance improvement aspects of such a promising architecture need to be investigated; one of which is load balancing technique. This paper focuses on devising an efficient algorithm for load balancing on the promising OTIS-Hypercube interconnection networks. The proposed algorithm is called Clusters Dimension Exchange Method (CDEM). The analytical model and the experimental evaluation proved the excellence of OTIS-Hypercube compared to Hypercube in terms of various parameters, including execution time, load balancing accuracy, number of communication steps, and speed.

Fine grained load balancing in multi-hop wireless networks

In this paper we address the problem of local balancing in multi-hop wireless networks. We introduce the notion of proactive routing: after a short pre-processing phase in which nodes build their routing tables by exchanging messages with neighbors, we require that nodes decide the relay of each message without any further interaction with other nodes. Besides delivering very low communication overhead, proactive routing protocols are robust against some well known active attacks to network routing. In this framework, we develop a proactive routing protocol that is able to balance the local load. Experiments show that our protocol improves network lifetime up to 98% and that it delivers a network that is more robust against attacks that have the goal of getting control over a large part of the network traffic.     

A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks

In many data-centric storage techniques, each event corresponds to a hashing location by event type. However, most of them fail to deal with storage memory space due to high percentage of the load is assigned to a relatively small portion of the sensor nodes. Hence, these nodes may fail to deal with the storage of the sensor nodes effectively. To solve the problem, we propose a grid-based dynamic load balancing approach for data-centric storage in sensor networks that relies on two schemes: (1) a cover-up scheme to deal with a problem of a storage node whose memory space is depleted. This scheme can adjust the number of storage nodes dynamically; (2) the multi-threshold levels to achieve load balancing in each grid and all nodes get load balancing. Simulations have shown that our scheme can enhance the quality of data and avoid hotspot of the storage while there are a vast number of the events in a sensor network.     

A new algorithm with segment protection and load balancing for single-link failure in multicasting survivable networks

In this paper, we investigate the problem of failure tolerated multicast requests in survivable networks and propose a new heuristic algorithm called segment protection with load balancing (SPLB) to address the single-link failure. In order to obtain better performances, in SPLB first we consider the techniques of cross-sharing and self-sharing to improve the resource utilization ratio, second we propose a segment protection routing algorithm to overcome the trap problem, and third we design a load balancing method to reduce the blocking probability. Compared with conventional algorithm, SPLB performs better performances. Simulation results meet our expectation.     

Hash-based proximity clustering for efficient load balancing in heterogeneous DHT networks

Distributed hash table (DHT) networks based on consistent hashing functions have an inherent load uneven distribution problem. The objective of DHT load balancing is to balance the workload of the network nodes in proportion to their capacity so as to eliminate traffic bottleneck. It is challenging because of the dynamism, proximity and heterogeneity natures of DHT networks and time-varying load characteristics.     

Meta-searches in peer-to-peer networks

We propose a method for carrying out enhanced collaborative searches, called meta-searches, in peer-to-peer networks. In addition to performing regular searches, our method supports searches based on other network users’ previous searches on the same or similar topic. In essence, when a user performs a search, s/he will receive not only the usual result set, but also information on other users’ previous results, as well as relevancy information (such as how many times a resource that appeared in the result set was successfully downloaded). The core components of meta-search are query relevancy calculation, query matching algorithms, and relevancy file format. In this paper we discuss the underlying concepts and principles, and describe the component design in detail. Meta-search provides a way of benefiting from other users’ successful searches without any additional effort, thus potentially improving the efficiency and experience of a search.     

DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach

Data gathering in wireless sensor networks (WSN) consumes more energy due to large amount of data transmitted. In direct transmission (DT) method, each node has to transmit its generated data to the base station (BS) which leads to higher energy consumption and affects the lifetime of the network. Clustering is one of the efficient ways of data gathering in WSN. There are various kinds of clustering techniques, which reduce the overall energy consumption in sensor networks. Cluster head (CH) plays a vital role in data gathering in clustered WSN. Energy consumption in CH node is comparatively higher than other non CH nodes because of its activities like data aggregation and transmission to BS node. The present day clustering algorithms in WSN use multi-hopping mechanism which cost higher energy for the CH nodes near to BS since it routes the data from other CHs to BS. Some CH nodes may die earlier than its intended lifetime due to its overloaded work which affects the performance of the WSN. This paper contributes a new clustering algorithm, Distributed Unequal Clustering using Fuzzy logic (DUCF) which elects CHs using fuzzy approach. DUCF forms unequal clusters to balance the energy consumption among the CHs. Fuzzy inference system (FIS) in DUCF uses the residual energy, node degree and distance to BS as input variables for CH election. Chance and size are the output fuzzy parameters in DUCF. DUCF assigns the maximum limit (size) of a number of member nodes for a CH by considering its input fuzzy parameters. The smaller cluster size is assigned for CHs which are nearer to BS since it acts as a router for other distant CHs. DUCF ensures load balancing among the clusters by varying the cluster size of its CH nodes. DUCF uses Mamdani method for fuzzy inference and Centroid method for defuzzification. DUCF performance was compared with well known algorithms such as LEACH, CHEF and EAUCF in various network scenarios. The experimental results indicated that DUCF forms unequal clusters which ensure load balancing among clusters, which again improves the network lifetime compared with its counterparts.     

Improving the performance of load balancing in software-defined networks through load variance-based synchronization

Software-Defined Networking (SDN) is a new network technology that decouples the control plane logic from the data plane and uses a programmable software controller to manage network operation and the state of network components. In an SDN network, a logically centralized controller uses a global network view to conduct management and operation of the network. The centralized control of the SDN network presents a tremendous opportunity for network operators to refactor the control plane and to improve the performance of applications. For the application of load balancing, the logically centralized controller conducts Real-time Least loaded Server selection (RLS) for multiple domains, where new flows pass by for the first time. The function of RLS is to enable the new flows to be forwarded to the least loaded server in the entire network. However, in a large-scale SDN network, the logically centralized controller usually consists of multiple distributed controllers. Existing multiple controller state synchronization schemes are based on Periodic Synchronization (PS), which can cause undesirable situations. For example, frequent synchronizations may result in high synchronization overhead of controllers. State desynchronization among controllers during the interval between two consecutive synchronizations could lead to forwarding loops and black holes. In this paper, we propose a new type of controller state synchronization scheme, Load Variance-based Synchronization (LVS), to improve the load-balancing performance in the multi-controller multi-domain SDN network. Compared with PS-based schemes, LVS-based schemes conduct effective state synchronizations among controllers only when the load of a specific server or domain exceeds a certain threshold, which significantly reduces the synchronization overhead of controllers. The results of simulations show that LVS achieves loop-free forwarding and good load-balancing performance with much less synchronization overhead, as compared with existing schemes.     

Contention-based performance evaluation of multidimensional range search in peer-to-peer networks

Performance evaluation of peer-to-peer search techniques has hitherto been based on simple performance metrics, such as message hop counts and total network traffic, mostly disregarding the inherently concurrent nature of peer-to-peer networks, where contention may arise. This paper is concerned with quantifying the effects of contention in P2P networks, focusing on networks for multidimensional range search. We evaluate peer-to-peer networks derived from recently proposed works, introducing two novel metrics related to concurrency and contention, namely responsiveness and throughput. Our results highlight the impact of contention on these networks, and demonstrate that some studied networks do not scale in the presence of contention. Also, our results indicate that certain P2P network properties believed to be desirable (e.g. even data distribution or uniform peer access) may not be as critical as previously believed.     

Skyframe: a framework for skyline query processing in peer-to-peer systems

This paper looks at the processing of skyline queries on peer-to-peer (P2P) networks. We propose Skyframe, a framework for efficient skyline query processing in P2P systems, which addresses the challenges of quick response time, low network communication cost and query load balancing among peers. Skyframe consists of two querying methods: one is optimized for network communication while the other focuses on query response time. These methods are different in the way in which the query search space is defined. In particular, the first method uses a high dominating point that has a large dominating region to prune the search space to achieve a low cost in network communication. On the other hand, the second method relaxes the search space in order to allow parallel query processing to speed up query response. Skyframe achieves query load balancing by both query load conscious data space splitting/merging during the join/departure of nodes and dynamic load migration. We further show how to apply Skyframe to both the P2P systems supporting multi-dimensional indexing and the P2P systems supporting single-dimensional indexing. Finally, we have conducted extensive experiments on both real and synthetic data sets over two existing P2P systems: CAN (Ratnasamy in A scalable content-addressable network. In: Proceedings of SIGCOMM Conference, pp. 161–172, 2001) and BATON (Jagadish et al. in A balanced tree structure for peer-to-peer networks. In: Proceedings of VLDB Conference, pp. 661–672, 2005) to evaluate the effectiveness and scalability of Skyframe.