Data load balancing is a challenging task in the P2P systems. Distributed hash table (DHT) abstraction, heterogeneous nodes, and non uniform distribution of objects are the reasons to cause load imbalance in structured P2P overlay networks. Previous works solved the load balancing problem by assuming the homogeneous capabilities of nodes, unawareness of the link latency during transferring load, and imposing logical structures to collect and reassign load. We propose a distributed load balancing algorithm with the topology awareness feature by using the concept of virtual servers. In our approach, each node collects neighborhood load information from physically close nodes and reassigns virtual servers to overlay nodes based upon the topology of underlying network. Consequently, our approach converges data load balancing quickly and it also reduces the load transfer cost between nodes. Moreover, our approach increases the quality of load balancing among close nodes of overlay and it also introduces a new tradeoff between the quality of load balancing and load transfer cost among all overlay nodes. Our simulations show that our approach reduces the load transfer cost and it saves network bandwidth respectively. Finally, we show that the in-degree imbalance of nodes, as a consequence of topology awareness, cannot lead to a remarkable problem in topology aware overlays.
@cite_21 introduce the novel design to perform fair load distribution in the context of content and resource management in unstructured P2P systems. They collect load objects by the meta-data and after that they compute a reassignment of objects by using that information.
@cite_21
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In this paper we examine the problem of balancing load in a large-scale distributed system when information about server loads may be stale. It is well known that sending each request to the machine with the apparent lowest load can behave badly in such systems, yet this technique is common in practice. Other systems use round-robin or random selection algorithms that entirely ignore load information or that only use a small subset of the load information. Rather than risk extremely bad performance on one hand or ignore the chance to use load information to improve performance on the other, we develop strategies that interpret load information based on its age. Through simulation, we examine several simple algorithms that use such load interpretation strategies under a range of workloads. Our experiments suggest that by properly interpreting load information, systems can (1) match the performance of the most aggressive algorithms when load information is fresh relative to the job arrival rate, (2) outperform the best of the other algorithms we examine by as much as 60 when information is moderately old, (3) significantly outperform random load distribution when information is older still, and (4) avoid pathological behavior even when information is extremely old. We present a novel approach for distributed load balancing in heterogeneous networks that use cell range expansion (CRE) for user association and almost blank subframe (ABS) for interference management. First, we formulate the problem as a minimization of an @math -fairness objective function with load and outage constraints. Depending on @math , different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a near-potential game, in which the potential function is the @math -fairness function. The optimal pure Nash equilibrium (PNE) of the game is found by using distributed learning algorithms. We propose log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. We give a detailed proof of convergence of learning algorithms for a near-potential game. We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS. The problem of judiciously and transparently redistributing the load of the system among its nodes so that overall performance is maximized is discussed. Several key issues in load distributing for general-purpose systems, including the motivations and design trade-offs for load-distributing algorithms, are reviewed. In addition, several load-distributing algorithms are described and their performances are compared. These algorithms are sender-initiated algorithms, receiver-initiated algorithms, symmetrically initiated algorithms, and adaptive algorithms. Load-distributing policies used in existing systems are examined, and conclusions about which algorithm might help in realizing the most benefits of load distributing are drawn. > It is becoming increasingly common to construct network services using redundant resources geographically distributed across the Internet. Content Distribution Networks are a prime example. Such systems distribute client requests to an appropriate server based on a variety of factors---e.g., server load, network proximity, cache locality---in an effort to reduce response time and increase the system capacity under load. This paper explores the design space of strategies employed to redirect requests, and defines a class of new algorithms that carefully balance load, locality, and proximity. We use large-scale detailed simulations to evaluate the various strategies. These simulations clearly demonstrate the effectiveness of our new algorithms, which yield a 60--91 improvement in system capacity when compared with the best published CDN technology, yet user-perceived response latency remains low and the system scales well with the number of servers.