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.
Many load balancing methods have been suggested to handle this problem in P2P systems. The first work has been done by Chord @cite_16 . They diminish load of overlay nodes by using the concept of virtual servers. They allocate @math virtual servers per physical node and suppose that all overlay nodes are similar. However, their approach does not practically resolve the load balancing problem.
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Load balancing is a critical issue for the efficient operation of peer-to-peer networks. We give new protocols for several scenarios, whose provable performance guarantees are within a constant factor of optimal. First, we give an improved version of consistent hashing, a scheme used for item to node assignments in the Chord system. In its original form, it required every network node to operate O(log n) virtual nodes to achieve a balanced load, causing a corresponding increase in space and bandwidth usage. Our protocol eliminates the necessity of virtual nodes while maintaining a balanced load. Improving on related protocols, our scheme allows for the deletion of nodes and admits a simpler analysis, since the assignments do not depend on the history of the network. We then analyze several simple protocols for load sharing by movements of data from higher loaded to lower loaded nodes. These protocols can be extended to preserve the ordering of data items. As an application, we use the last protocol to give an efficient implementation of a distributed data structure for range searches on ordered data. IP anycast, with its innate ability to find nearby resources in a robust and efficient fashion, has long been considered an important means of service discovery. The growth of P2P applications presents appealing new uses for IP anycast. Unfortunately, IP anycast suffers from serious problems: it is very hard to deploy globally, it scales poorly by the number of anycast groups, and it lacks important features like load-balancing. As a result, its use is limited to a few critical infrastructure services such as DNS root servers. The primary contribution of this paper is a new IP anycast architecture, PIAS, that overcomes these problems while largely maintaining the strengths of IP anycast. PIAS makes use of a proxy overlay that advertises IP anycast addresses on behalf of group members and tunnels anycast packets to those members. The paper presents a detailed design of PIAS and evaluates its scalability and efficiency through simulation. We also present preliminary measurement results on anycasted DNS root servers that suggest that IP anycast provides good affinity. Finally, we describe how PIAS supports two important P2P and overlay applications. Current web server farms have simple resource allocation models. One model used is to dedicate a server or a group of servers for each customer. Another model partitions physical servers into logical servers and assigns one to each customer. Yet another model allows customers to be active on multiple servers using load-balancing techniques. The ability to handle peak loads while minimizing cost of resources required on the farm is a subject of ongoing research.We improve resource utilization through sharing. Customer load is expressed as a multidimensional probability distribution. Each customer is assigned to a server so as to minimize the total number of servers needed to host all the customers. We use the notion of complementarity of customers in simple heuristics for this stochastic vector-packing problem. The proposed method generates a resource allocation plan while guaranteeing a QoS to each customer. Simulation results justify our scheme. We present Araneola, a scalable reliable application-level multicast system for highly dynamic wide-area environments. Araneola supports multi-point to multi-point reliable communication in a fully distributed manner while incurring constant load on each node. For a tunable parameter k spl ges 3, Araneola constructs and dynamically maintains an overlay structure in which each node's degree is either k or k + 1, and roughly 90 of the nodes have degree k. Empirical evaluation shows that Araneola's overlay structure achieves three important mathematical properties of k-regular random graphs (i.e., random graphs in which each node has exactly k neighbors) with N nodes: (i) its diameter grows logarithmically with N; (ii) it is generally k-connected; and (iii) it remains highly connected following random removal of linear-size subsets of edges or nodes. The overlay is constructed at a very low cost: each join, leave, or failure is handled locally, and entails the sending of only about 3k messages in total. Given this overlay, Araneola disseminates multicast messages by gossiping over the overlay's links. We show that compared to a standard gossip-based multicast protocol, Araneola achieves substantial improvements in load, reliability, and latency. Finally, we present an extension to Araneola in which the basic overlay is enhanced with additional links chosen according to geographic proximity and available bandwidth. We show that this approach reduces the number of physical hops messages traverse without hurting the overlay's robustness. This paper presents the design of Mercury, a scalable protocol for supporting multi-attribute range-based searches. Mercury differs from previous range-based query systems in that it supports multiple attributes as well as performs explicit load balancing. To guarantee efficient routing and load balancing, Mercury uses novel light-weight sampling mechanisms for uniformly sampling random nodes in a highly dynamic overlay network. Our evaluation shows that Mercury is able to achieve its goals of logarithmic-hop routing and near-uniform load balancing.We also show that Mercury can be used to solve a key problem for an important class of distributed applications: distributed state maintenance for distributed games. We show that the Mercury-based solution is easy to use, and that it reduces the game's messaging overheard significantly compared to a naive approach. Load balanced routing in a network, i.e., minimizing the maximum traffic load any node carries for unsplittable flows, is a well known NP-hard problem. Finding practical algorithms remains a long standing challenge. In this paper we propose greedy routing using virtual coordinates that achieves both small path stretch ratio (compared to shortest path) and small load balancing ratio (compared to optimal load balanced routing), in a large scale wireless sensor network deployed densely inside a geometric domain with complex shape. We first provide a greedy routing scheme on a disk with a stretch ratio of at most 2, and under which the maximum load is a factor 4√2 smaller than the maximum load under shortest path routing. This is the first simple routing scheme with a small stretch that has been proven to outperform shortest path routing in terms of load balancing. Then we transform a network of arbitrary shape to a disk by an area preserving map φ. We show that both the path length and the maximum traffic load in the original network only increases by an additional factor of d2, where d is the maximum length stretch of φ. Combined with the result on a disk we again achieve both bounded stretch and bounded load balancing ratio. Our simulation results evaluated the practical performance on both quality measures. At the Internet of Things (IoT) application layer, a physical phenomenon, which is sensed by a server (i.e., an IoT device), is defined as an IoT resource. In this paper, we propose to cache popular IoT resources in brokers, which are considered as the application layer middleware nodes. Caching popular resources in the brokers is to move the traffic loads (for delivering the up-to-date contents of the resources) from the servers (which host these popular resources) to the brokers, thus reducing the energy consumption of the servers. However, many brokers may be geographically distributed in the network and caching popular resources in nearby brokers may result in unbalanced traffic loads among the brokers, and may thus dramatically increase the average delay of the brokers in delivering the contents of their cached popular resources to clients. To reduce the average delay among the brokers, we propose to re-cache re-allocate the popular resources from heavily loaded brokers into lightly loaded brokers in order to balance the traffic loads among brokers. We formulate the popular resource re-caching problem as an optimization problem, which is proven to be NP-hard. We design the latency aware popular resource re-caching (LEARN) algorithm to efficiently solve the problem, and demonstrate the performance of LEARN via simulations. Scalability is critical to the success of many enterprises currently involved in doing business on the web and in providing information that may vary drastically from one time to another. Maintaining sufficient resources just to meet peak requirements can be costly. Cloud computing provides a powerful computing model that allows users to access resources on-demand. In this paper, we will describe a novel architecture for the dynamic scaling of web applications based on thresholds in a virtualized Cloud Computing environment. We will illustrate our scaling approach with a front-end load-balancer for routing and balancing user requests to web applications deployed on web servers installed in virtual machine instances. A dynamic scaling algorithm for automated provisioning of virtual machine resources based on threshold number of active sessions will be introduced. The on-demand capability of the Cloud to rapidly provision and dynamically allocate resources to users will be discussed. Our work has demonstrated the compelling benefits of the Cloud which is capable of handling sudden load surges, delivering IT resources on-demands to users, and maintaining higher resource utilization, thus reducing infrastructure and management costs. We consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems. In this framework, we investigate the tension between storage resources, communication cost, and load balancing performance. To this end, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process. In contrast to the classical power of two choices setup, since the memory limitation and the proximity constraint cause correlation in the server selection process, we may not benefit from the power of two choices. However, we prove that in certain regimes of problem parameters, our scheme results in the maximum load of order @math (here @math is the network size). This is an exponential improvement compared to the scheme which assigns each request to the nearest available replica. Interestingly, the extra communication cost incurred by our proposed scheme, compared to the nearest replica strategy, is small. Furthermore, our extensive simulations show that the trade-off trend does not depend on the network topology and library popularity profile details. Multiplayer games played over the Internet have become very popular in the lastfew years. An interesting subcategory are the so-called massively multiplayeronline games (MMOGs) that allow thousands of player characters to share asingle game world. Such a world is usually run on a high-performance and high-availability server cluster. However, even with games that have been extensivelybeta-tested, downtimes of several hours because of hard- or software failures arenot uncommon. Downtimes, especially in the ﬁrst few weeks after the release,can negatively aﬀect the image of the game and the company that created it.Traditionally, a cluster of servers contains one virtual world of a MMOG.Such infrastructure is inﬂexible and error-prone. One would rather like to havea system that allows disconnecting a server at runtime while others take overits tasks. Server-based MMOGs can have performance problems if players areconcentrated in certain parts of the game world or some worlds are overpopu-lated. Thus, there is also a need for load balancing mechanisms. Peer-to-Peer(P2P) systems quite naturally support the use of load balancing.In this paper we use a structured P2P technology for the organization of theinfrastructure and thus for the reduction of downtimes in MMOGs. We splitthe game world in disjunctive rectangular zones and distribute them on diﬀerentnodes of the P2P network.Online games are an interesting challenge and chance for the future devel-opment of the P2P paradigm. A wide variety of aspects of only theoreticallysolved and especially yet completely unsolved problems are covered by this ap-plication. Security and trust problems appear as well as the need to preventcheating. The application is not as tolerant to faults as instant messaging or ﬁlesharing. Consistent data storage is a problem, decisions and transactions haveto be performed in a decentralized way. Moreover, the P2P network is not usedas pure lookup service, but more as a communication and application-speciﬁcsocial structure.The rest of this paper is organized as follows: First we discuss related work inSection 2 and give a brief introduction to P2P and MMOGs and their challengesin Section 3. Section 4 shows our approach to use structured P2P Systems forMMOGs and section 5 the evaluation with player traces from a real MMOG.Finally, Section 6 provides conclusions. This paper "peeks under the covers" at the subsystems that provide the basic functionality of a leading content delivery network. Based on our experiences in building one of the largest distributed systems in the world, we illustrate how sophisticated algorithmic research has been adapted to balance the load between and within server clusters, manage the caches on servers, select paths through an overlay routing network, and elect leaders in various contexts. In each instance, we first explain the theory underlying the algorithms, then introduce practical considerations not captured by the theoretical models, and finally describe what is implemented in practice. Through these examples, we highlight the role of algorithmic research in the design of complex networked systems. The paper also illustrates the close synergy that exists between research and industry where research ideas cross over into products and product requirements drive future research. Load balancing for distributed servers is a common issue in many applications and has been extensively studied. Several distributed load balancing schemes have been proposed that proactively route individual requests to appropriate servers to best balance the load and shorten request response time. These schemes do not require a centralized load balancer. Instead, each server is responsible for determining, for each request it receives from a client, to which server in the pool the request should be forwarded for processing. We propose a new request routing scheme that is more scalable to increasing number of servers and request load than the existing schemes. The method combines random server selection and next-neighbor load sharing techniques that together prevent the staleness of load information from building up when the number of servers increases. Our simulation shows that it outperforms existing schemes under a piggyback-based load update model. Virtualization can deliver significant benefits for cloud computing by enabling VM migration to improve utilization, balance load and alleviate hotspots. While several mechanisms exist to migrate VMs, few efforts have focused on optimizing migration policies in a multi-rooted tree datacenter network. The general problem has multiple facets, two of which map to generalizations of well-studied problems: (1) Migration of VMs in a bandwidth-oversubscribed tree network generalizes the maximum multicommodity flow problem in a tree, and (2) Migrations must meet load constraints at the servers, mapping to variants of the matching problem --- generalized assignment and demand matching. While these problems have been individually studied, a new fundamental challenge is to simultaneously handle the packing constraints of server load and tree edge capacities. We give approximation algorithms for several versions of this problem, where the objective is to alleviate a maximal number of hot servers. In the full version of this work [5], we empirically demonstrate the effectiveness of these algorithms through large scale simulations on real data. In a peer-to-peer (P2P) system, nodes typically connect to a small set of random nodes (their neighbors), and queries are propagated along these connections. Such query flooding tends to be very expensive. We propose that node connections be influenced by content, so that for example, nodes having many Jazz files will connect to other similar nodes. Thus, semantically related nodes form a Semantic Overlay Network (SON). Queries are routed to the appropriate SONs, increasing the chances that matching files will be found quickly, and reducing the search load on nodes that have unrelated content. We have evaluated SONs by using an actual snapshot of music-sharing clients. Our results show that SONs can significantly improve query performance while at the same time allowing users to decide what content to put in their computers and to whom to connect. In order to achieve dynamic load balancing based on data fl ow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the Open Flow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What’s more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput. In a Peer-to-Peer (P2P) file-sharing system, a node finds and retrieves its desired file. If multiple nodes cache the same file to provide others, we can achieve a dependable file-sharing system with low latency and high file availability. However, a node has to spend costs, e.g., processing load or storage capacity, on caching a file. Consequently, a node may selfishly behave and hesitate to cache a file. In such a case, unpopular files are likely to disappear from the system. In this paper, we aim to reveal whether effective caching in the whole system emerges from autonomous and selfish node behavior. We discuss relationship between selfish node behavior and system dynamics by using evolutionary game theory. Through theoretic analysis, we show that a file-sharing system can be robust to file disappearance depending on a cost and demand model for caching even if nodes behave selfishly. Furthermore, we also conduct several simulation-based analysis in terms of network structures, evolving network, load balancing, and system stability. As a result, we demonstrate that a file-sharing system with good properties, i.e., robustness to file disappearance, low search latency, well load-balancing, and high stability, can be achieved independent of network structures and dynamics. Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high-performance computing systems in the heterogeneity of the computing nodes, as well as the communication links that connect the different nodes together. There is a need to develop algorithms that can capture this complexity yet can be easily implemented and used to solve a wide range of load-balancing scenarios. In this paper, we propose a game-theoretic solution to the grid load-balancing problem. The algorithm developed combines the inherent efficiency of the centralized approach and the fault-tolerant nature of the distributed, decentralized approach. We model the grid load-balancing problem as a noncooperative game, whereby the objective is to reach the Nash equilibrium. Experiments were conducted to show the applicability of the proposed approaches. One advantage of our scheme is the relatively low overhead and robust performance against inaccuracies in performance prediction information. The capacity of wireless networks can be increased via dynamic load balancing sharing by employing overlay networks on top of the existing cellular networks. One such recently proposed system is the integrated cellular and ad hoc relay (iCAR) system, where an overlay ad hoc network is employed to use the resources efficiently by dynamically balancing the load of the hot spots in the cellular network, and to provide quality-of-service to subscribers, no matter where they are located and when the request is made. It is assumed that this overlay network operates in the 2.4-GHz Industrial, Scientific, and Medical (ISM) band and, hence, the number of available ISM-band relay channels used for load balancing will be limited due to other users' interference at a given point in time. In this paper, the impact of ISM-band interference on the performance of iCAR systems, which is a representative hybrid wireless network, is studied, and it is shown that dynamic load balancing and sharing capabilities of iCAR systems are strictly dependent on the availability of the ISM-band relay channels. In addition to quantifying the impact of the number of available relay channels on the performance of iCAR systems, a simple channel assignment scheme to reduce the performance degradation due to other users' interference is also provided. Results show that this interference avoidance technique can improve the realistic performance of iCAR-like hybrid wireless networks by 12 -23 when the interferers are uniformly distributed in the ISM-band. Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution, and application-level multicast applications. These P2P overlay networks attempt to provide a long list of features, such as: selection of nearby peers, redundant storage, efficient search location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing, and explicit notion of locality. In this article we present a survey and comparison of various Structured and Unstructured P2P overlay networks. We categorize the various schemes into these two groups in the design spectrum, and discuss the application-level network performance of each group. In this paper, we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.