Load Balancing and Replication

Routing and wavelength assignment (RWA) is one of the key issues in the wavelength-routed optical networks. Although some RWA algorithms have been well performed to meet the need of certain networks requirement, they usually neglect the performance of the whole networks, especially the load balancing of the whole networks. This is quite likely to lead to some links bearing excessive ligthpaths and traffic load, while other links being at an idle state. In this paper, the load distribution vector ( LDV ) is introduced to describe the links load of the networks firstly. Then by means of minimizing the LDV of the networks, the load balancing of the whole networks is tried to improve. Based on this, a heuristic load balancing (HLB) strategy is presented. Moreover, a novel RWA algorithm adopting the heuristic load balancing strategy is developed, as well as two other RWA algorithms adopting other load balancing strategies. At last, the simulations of the three RWA algorithms with different load balancing strategies are done for comparison on the basis of both the regular topology and the irregular topology networks. The simulation results show that the key performance parameters such as the average variance of links, the maximum link load and the number of established lightpath are improved by means of our novel RWA algorithm with the heuristic load balancing strategy.

Most cluster schemes select cluster head (CH) randomly without considering load balancing and security in Wireless Sensor Networks (WSNs). Our research on relevant literature shows that the existing authentication schemes do not fully consider the load balancing of CHs, while the load balancing schemes ignore the security authentication of CHs. Therefore, this paper effectively combines load balancing and security verification, and proposes a secure load balancing and authentication scheme (SLEB) based on Clustered Wireless Sensor networks. SLEB not only effectively maintains the energy balance of the whole network, but also successfully improves packet forwarding rate and prolongs the network lifetime. The simulation shows that SLEB is energy-saving, safe and sustainable compared with other similar schemes.

Dynamic load balancing is one key adaptation mechanism often deployed in networking and computing systems. Numerous proposals exist for load balancing in peer-to-peer networks. These schemes either focus on good balanced namespace and balanced stream, or work on the minimum balancing overhead. All of them will enhance the availability of P2P system to some extent. However, few attentions have been paid on security threats introduced by the load balancing. In this paper, we build the framework model of two main categories of load balancing schemes in P2P system: ID manipulating and virtual server. Vulnerability analysis is conducted for both solutions and attack tree model is proposed which will help designing countermeasures to thwart attacks toward load balancing system in P2P.

The signal intensity based wireless terminal access mechanism is the key cause of imbalanced load of WLAN. In the environment where users are densely packed, such as banks, office buildings and malls, load imbalance will severely decrease the quality and source utility rate of WLAN, thus the node load balance strategy becomes increasingly in demand to solve the problem. First, this paper studied the mainstream node load balance technologies and proposed an evaluation model which identifies and measures the level of load; next, the load level gain calculation model was designed and the prediction-based load balance and resource adjustment strategy was put forward; at last, the load balance mechanism ST-WLB was enabled and the test result shows that it can improve the performance by 10% without excessive costs.

High-speed routers and high-performance data centers share a common system-level architecture in which multiple processing nodes are connected by an interconnection network for high-speed communications. Load balancing is an important technique for maximizing throughput and minimizing delay of the interconnection network. In this thesis, efficient load balancing schemes are designed and analyzed for next-generation routers and data centers.
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\nIn high-speed router design, two preferred switch architectures are input-queued switch and load-balanced switch. In an input-queued switch, time-domain load balancing can be carried out by an iterative algorithm that schedules packets for sending in different time slots. The complexity of an iterative algorithm increases rapidly with the number of scheduling iterations. To address this problem, a single-iteration scheduling algorithm called D-LQF is designed, in which exhaustive service policy is adopted for reusing the matched input-output pairs in the previous time slots to grow the match size. 
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\nUnlike an input-queued switch, a load-balanced switch consists of two stages of crossbar switch fabrics, where load balancing is carried out in both time and space domains. Among various load-balanced switches, the feedback-based switch gives the best delay-throughput performance. In this thesis, the feedback-based switch is enhanced in three aspects. Firstly, we focus on reducing its switch fabric complexity. Instead of using crossbars, a dual-banyan network is proposed. The complexity of dual-banyan can be further reduced by merging the two banyans to form a Clos network, resulting in a Clos-banyan network. Secondly, we target at improving the delay performance of the feedback-based switch. A Clos-feedback switch architecture is devised where each switch module in the Clos network is a small feedback-based switch. With application-flow based load balancing, packet order is ensured and the average packet delay is reduced from O(N) to O(n), where N and n are the switch and switch module sizes, respectively. Thirdly, we extend the feedback-based switch to support multicast traffic. Based on the notion of pointer-based multicast VOQ, an efficient multicast scheduling algorithm with packet replication at the middle-stage ports only is proposed. In order to provide close-to-100% throughput for any admissible multicast traffic patterns, a three-stage implementation of feedback-based switch is also designed.
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\nIn designing load balancing schemes for data centers, we focus on the most popular fat-tree based data centers. Notably, packet-based load balancing is widely considered infeasible for data centers. This is because the associated packet out-of-order problem will cause unnecessary TCP fast retransmits, and as a result, severely undermine TCP performance. In this thesis, we show that if packet-based load balancing is performed properly, the packet out-of-order problem can be easily addressed by slightly increasing the number of duplicate ACKs required for triggering fast retransmit. Admittedly, in case of a real packet loss, the loss recovery time will be increased. But our simulation results show that such an increase is far less than the reduction in the network queueing delay (due to a better load-balanced network). As compared to a flow-based load balancing scheme, our packet-based scheme consistently provides significantly higher goodput and noticeably smaller delay.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

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.

Developing SPMD applications with load balancing

Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker's idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.

In recent years, the adoption of multi-cloud environments has increased due to its ability to provide better performance, cost-effectiveness, and higher availability. However, managing resources in such environments is a challenging task. Load balancing and virtual machine (VM) migration are two critical techniques for resource management in multi-cloud environments. Load balancing helps to distribute the workload evenly among the cloud resources, while VM migration enables the dynamic allocation of resources based on the changing demands. This systematic review paper presents a comprehensive survey of load balancing and VM migration strategies in multi-cloud environments. The paper first introduces the concept of multicloud environments and the challenges of resource management in such environments. Then, the paper discusses the different load balancing and VM migration techniques that have been proposed for multi-cloud environments, including their advantages and limitations.

Structured peer-to-peer (P2P) systems address the load balancing issue in a rather naive way, by simply resorting to the uniformity of the hash function utilized to generate object IDs. Such a random choice of object IDs could result in O(log N) load imbalance. We propose an efficient, proximity-aware load balancing algorithm for such systems. To our knowledge, this is the first work to utilize the proximity information to guide load balancing. In particular, our main contributions are: (1) A self-organized, fully distributed K-nary tree structure is constructed on top of a DHT for load balancing information collection/dissemination and load reassignment. (2) Load balancing is achieved by aligning those two skews in both load distribution and node capacity inhere in P2P systems - that is, have higher capacity nodes carry more loads. (3) Proximity information is utilized to guide load balancing such that virtual servers are assigned and transferred between physically close heavy nodes and light nodes, thereby minimizing the load transferring overhead and making load balancing fast and efficient.

Recent advances in programmable data plans, software-defined networks and the adoption of IPv6 support new and more complex load balancing strategies. In this work, we introduce the Multipath Classification Algorithm (MCA), a probing algorithm that extends traceroute to identify and classify load balancing on Internet routes. We generalize the current formalism to describe load balancing and extend existing measurement techniques to consider that load balancing can use arbitrary combinations of packet header fields. We propose optimizations to reduce the probing cost, applicable to both MCA and existing load balancing measurement techniques. Through large-scale measurement campaigns, we characterize and study the evolution of load balancing on the IPv4 and IPv6 Internet, using various transport protocols. Our results show that load balancing is more prevalent and that load balancing strategies currently in use are more mature than the previous characterizations have found. We share our measurement and analysis software as well as datasets with the Internet measurement community.

Structured peer-to-peer systems are popular solutions for large scale distributed computing and query processing. Heterogeneity among peers calls for peer virtualization to maintain a simple, yet powerful peer-to-peer overlay network. Nevertheless, peer virtualization generates a huge number of virtual peers and causes the unnecessary communication overhead in the routing process. In this paper, we propose a new peer-to-peer routing algorithm that reduces the number of hops of message forwarding and improves the performance of routing. We study the new and previous algorithms from the analytical perspective and through simulations. It shows that the average number of hops per query is improved by 15% to 25% in our algorithm. In addition, we propose a Top-k peer selection algorithm for load balancing to find out the top k best available nodes in the P2P network with 2(N-1) messages within 2O(logN) hops. (N is the number of physical nodes.) The load balancing scheme is based on multiple factors which could be optimized on cost, proximity, reputation and other factors.

Bandwidth load balancing is a critical technique for improving the quality of service (QoS) in local area networks (LANs). Insufficient load balancing and QoS utilization can lead to network congestion, reduced performance, latency, packet loss, and an overall degradation of network performance. This paper presents an overview of load balancing techniques, load balancing algorithms, QoS parameters, load balancing strategies, and considerations for calculating bandwidth. The challenges of implementing load balancing algorithms and preferences for their implementation are discussed. Accurate network measurements and monitoring, coordination between network devices, security and privacy considerations, and careful evaluation of load balancing algorithms are among the challenges faced. The paper also discusses optimization and monitoring of bandwidth use, including options such as increasing bandwidth, bandwidth throttling, and data transfer throttling. The findings presented in this paper provide insights into the importance of load balancing and QoS in LANs and offer guidance for addressing related challenges.

In recent years, Radio Frequency Identification (RFID) industries have taken a great interest in utilizing the benefits of RFID for supply chain management, inventory control and various other applications. This paper proposed an adaptive load balancing technique for RFID middleware systems to meet the demands of scalability and heterogeneity. First, we explored five basic load balancing policies, namely, information policy, job selection policy, transfer policy, initiation policy and location policy. Eighteen load balancing schemes were then proposed for RFID middleware systems that were combinations of various types of the five basic load balancing policies. Our empirical study suggested that these load balancing strategies performed differently under different workload statuses. Finally, an adaptive load balancing strategy was proposed. The load balancing schemes and the proposed adaptive load balancing strategy have been implemented in the RFID Middleware Load Management System (RM-LMS). Copyright © 2010 John Wiley & Sons, Ltd.