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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Peer-to-peer sharing systems are becoming increasingly popular and an exciting new class of innovative, internet-based data management systems. In these systems, users contribute their own resources (processing units and storage devices) and content (i.e., documents) to the P2P community. We focus on the management of content and resources in such systems. Our goal is to harness all available resources in the P2P network so that the users can access all available content efficiently. Efficiency is taken both from (i) the point of view of the system, in that we strive to ensure fair load distribution among all peer nodes, and (ii) from the point of view of the users, in that we strive to ensure low user-request response times. We propose a novel architecture for this new class of applications, which differs drastically from what is either found currently in existing products or proposed in academia. We contribute and study novel solutions that achieve our goals, while at the same time addressing the formidable challenges due to the autonomy of peers, their heterogeneous processing and storage capacities, their different content contributions, the huge system scale, and the highly dynamic system environment. In this paper we model and study the performance of peer-to-peer (P2P) file sharing systems in terms of their 'service capacity'. We identify two regimes of interest: the transient and stationary regimes. We show that in both regimes, the performance of P2P systems exhibits a favorable scaling with the offered load. P2P systems achieve this by efficiently leveraging the service capacity of other peers, who possibly are concurrently downloading the same file. Therefore to improve the performance, it is important to design mechanisms to give peers incentives for sharing cooperation. One approach is to introduce mechanisms for resource allocation that are 'fair', such that a peer's performance improves with his contributions. We find that some intuitive 'fairness' notions may unexpectedly lead to 'unfair' allocations, which do not provide the right incentives for peers. Thus, implementation of P2P systems may want to compromise the degree of 'fairness' in favor of maintaining system robustness and reducing overheads. 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. 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. An important issue in Peer-to-Peer networks is to encourage users to share with others as they use the resources of the network. However, some nodes may only consume from users without giving anything in return. To fix this problem, we can incorporate trust management systems with network infrastructures. Current trust managements are usually made for unstructured overlays and have several shortcomings. They are made to be very similar to e-commerce scoring websites which may not be the best design for fairness in P2P networks. Several problems may arise with their designs such as difficulties to provide a complete history of freeloaders or lack of an autonomous removal mechanism in case of severe attacks. In this paper, we argue that such systems can be deployed more efficiently by using a structured paradigm. For this purpose, we propose C-Trust, a trust management system which is focused on fairness for P2P networks. This is done by getting help from current circular structured designs. This method is able to mark freeloaders, identify their severity of abusion and punish them accordingly. We are also able to effectively protect both Seeder-to-Leecher and Leecher-to-Leecher transactions. This feature is specially important for fairness which other trust systems have not considered so far. We propose a novel search mechanism for unstructured p2p networks, and show that it is both scalable, i.e., it leads to a bounded query traffic load per peer as the peer population grows, and reliable, i.e., it successfully locates all files that are (sufficiently often) brought into the system. To the best of our knowledge, this is the first time that a search mechanism for unstructured p2p networks has been shown to be both scalable and reliable. We provide both a formal analysis and a numerical case study to illustrate this result. Our analysis is based on a random graph model for the overlay graph topology and uses a mean-field approximation to characterize the evolution of how files are replicated in the network. The emergence of cloud computing has established a trend towards building energy-hungry and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric power grid by significantly increasing the load at locations where they are built. Dynamic energy pricing policies in the recently proposed smart grid technology can incentivize the cloud computing controller to shift their computation load towards data centers in regions with cheaper electricity. On the other hand, distributed data centers also provide opportunities to help the smart grid to improve load balancing and robustness. To shed some light into these opportunities, this paper considers an interaction system of the smart grid, the cloud computing system, and other load devices. A nested two stage game based formulation is proposed based on the location-dependent real-time pricing policy of the smart grid. The leading player in this game is the smart grid controller that announces the relationship between the electricity price at each power bus and the total load demand at that bus. In the second stage, the cloud computing controller performs resource allocation as response to the pricing functions, whereas the other load devices perform demand side management. The objective of the smart grid controller is to maximize its own profit and perform load balancing among power buses, whereas the objective of the cloud computing controller is to maximize its own profit with respect to the location-dependent pricing functions. The optimal strategies are derived based on the backward induction principle for the smart grid controller, the cloud computing controller, and the other load devices, using convex optimization and heuristic search. Distributed computer architectures labeled "peer-to-peer" are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and accommodate transient populations of nodes while maintaining acceptable connectivity and performance.Content distribution is an important peer-to-peer application on the Internet that has received considerable research attention. Content distribution applications typically allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content.In this survey, we propose a framework for analyzing peer-to-peer content distribution technologies. Our approach focuses on nonfunctional characteristics such as security, scalability, performance, fairness, and resource management potential, and examines the way in which these characteristics are reflected in---and affected by---the architectural design decisions adopted by current peer-to-peer systems.We study current peer-to-peer systems and infrastructure technologies in terms of their distributed object location and routing mechanisms, their approach to content replication, caching and migration, their support for encryption, access control, authentication and identity, anonymity, deniability, accountability and reputation, and their use of resource trading and management schemes. A new era of Cloud Computing has emerged, but the characteristics of Cloud load in data centers is not perfectly clear. Yet this characterization is critical for the design of novel Cloud job and resource management systems. In this paper, we comprehensively characterize the job task load and host load in a real-world production data center at Google Inc. We use a detailed trace of over 25 million tasks across over 12,500 hosts. We study the differences between a Google data center and other Grid HPC systems, from the perspective of both work load (w.r.t. jobs and tasks) and host load (w.r.t. machines). In particular, we study the job length, job submission frequency, and the resource utilization of jobs in the different systems, and also investigate valuable statistics of machine's maximum load, queue state and relative usage levels, with different job priorities and resource attributes. We find that the Google data center exhibits finer resource allocation with respect to CPU and memory than that of Grid HPC systems. Google jobs are always submitted with much higher frequency and they are much shorter than Grid jobs. As such, Google host load exhibits higher variance and noise. In peer-to-peer (P2P) systems, computers from around the globe share data and can participate in distributed computation. P2P became famous, and infamous, due to file-sharing systems like Napster. However, the scalability and robustness of these systems make them appealing to a wide range of applications. This article introduces P-Ring, a new peer-to-peer index structure. P-Ring is fully distributed, fault tolerant, and provides load balancing and logarithmic search performance while supporting both equality and range queries. Our theoretical analysis as well as experimental results, obtained both in a simulated environment and on PlanetLab, show the performance of our system. Energy management is becoming a crucial issue in the future power grid system as more controllable energy resources and responsive loads with communications abilities are being introduced into the smart grid. This paper proposes a novel distributed approach to deal with energy management in the smart grid under dispatchable distributed generators and responsive loads using real-time pricing (RTP) and consensus networks to maximize the social welfare. In our algorithm, each distributed generation consumer unit, in response to the local price of energy, decides on its optimal power generation consumption level to maximize its benefit at the device level. However, the consensus-based coordination of price among local retailers drives the behavior of the overall system toward the global optimum, despite the greedy behavior of the generation and consumer units. The main features of our algorithm are computational and communicational scalability, as well as privacy of information. The objective of this paper is to introduce a model to guide the analysis of the impact of churn in P2P networks. Using this model, a variety of node membership scenarios is created. These scenarios are used to capture and analyze the performance trends of chord, a distributed hash table (DHT) based resource lookup protocol for peer-to-peer overlay networks. The performance study focuses both on the performance of routing and content retrieval. This study also identifies the limitations of various churn-alleviating mechanisms, frequently proposed in the literature. The study highlights the importance of the content nature and access pattern on the performance of P2P, DHT-based overlay networks. The results show that the type of content being accessed and the way the content is accessed has a significant impact on the performance of P2P networks We address the problem of resource management for a large-scale cloud environment that hosts sites. Our contribution centers around outlining a distributed middleware architecture and presenting one of its key elements, a gossip protocol that meets our design goals: fairness of resource allocation with respect to hosted sites, efficient adaptation to load changes and scalability in terms of both the number of machines and sites. We formalize the resource allocation problem as that of dynamically maximizing the cloud utility under CPU and memory constraints. While we can show that an optimal solution without considering memory constraints is straightforward (but not useful), we provide an efficient heuristic solution for the complete problem instead. We evaluate the protocol through simulation and find its performance to be well-aligned with our design goals. Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and loss) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads. We propose CLEAR (Context and Location-based Efficient Allocation of Replicas), a dynamic replica allocation scheme for improving data availability in mobile ad-hoc peer-to-peer (M-P2P) networks. To manage replica allocation efficiently, CLEAR exploits user mobility patterns and deploys a super-peer architecture, which avoids both broadcast storm during replica allocation as well as broadcast-based querying. CLEAR considers different levels of replica consistency and load as replica allocation criteria. Our performance study indicates CLEAR's overall effectiveness in improving data availability in M-P2P networks. For a peer-to-peer (P2P) system holding massive amount of data, efficient semantic based search for resources (such as data or services) is a key determinant to its scalability. This work presents the design of an overlay network, namely semantic small world (SSW), that facilitates efficient semantic based search in P2P systems. SSW is based on three innovative ideas: 1) small world network; 2) semantic clustering; 3) dimension reduction. Peers in SSW are clustered according to the semantics of their local data and self-organized as a small world overlay network. To address the maintenance issue of high dimensional overlay networks, a dynamic dimension reduction method, called adaptive space linearization, is used to construct a one-dimensional SSW that supports operations in the high dimensional semantic space. SSW achieves a very competitive trade-off between the search latencies traffic and maintenance overheads. Through extensive simulations, we show that SSW is much more scalable to very large network sizes and very large numbers of data objects compared to pSearch, the state-of-the-art semantic-based search technique for P2P systems. In addition, SSW is adaptive to distribution of data and locality of interest; is very resilient to failures; and has good load balancing property. Most wireless local area network performance estimations are done with the assumption of uniformly distributed (UD) users. In practice, however, stations (STAs) are distributed unevenly among access points (APs) in an extended service area, causing congested hot-spots (HS) and under-utilized APs. Considering a typical network is made up of multiple APs, having some nodes carrying excessive loads degrades the overall network performance. The system performance can be improved by associating STAs efficiently throughout the network, in a sense sharing the network resources fairly among APs and thus relieving congestion. The association algorithm currently employed in IEEE 802.11 systems, that is specifically designed for residential and small office environments, takes into account signal strength as the only parameter and associates STAs to the closest (in signal strength sense) AP, ignoring its load. Novel user association algorithms are required to solve the problems commonly seen in corporate network environments spanning multiple APs, namely congestion relief and resource sharing. In this work, a distributed and online association algorithm is proposed that demonstrates improved average throughput performance, a balanced load distribution as well as fairness across the network compared to the conventional algorithm. Copyright © 2008 John Wiley & Sons, Ltd. 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. The issue of controlled and reliable integration of distributed energy resources into microgrids and large power grids has recently gained considerable attention. The microgrid concept, which basically corresponds to the coordinated operation of a cluster of loads, distributed generators and energy storage systems, is quite appealing due to its flexibility, controllability and energy management capabilities. In order to provide uninterruptible power supply to the loads, microgrids are expected to operate in both grid-connected and stand-alone modes, and economically meet the demand on an instantaneous basis. The problem of optimal management of the resources in a microgrid is being widely investigated and recent studies have proposed the application of both centralized and distributed control schemes by using multi-agent systems, heuristic methods and optimization algorithms. This paper elaborates on the conceptual design of a centralized energy management system (EMS) and its desirable attributes for a microgrid in stand-alone mode of operation. A number of test protocols are proposed to analyze the performance of the system, as well as the impacts of relevant parameters. In the near future, all our everyday things and objects will be both connected to the Internet and equipped with enough sensing, acting and processing capabilities to exploit the full potential benefits of the so called Internet of Things (IoT) paradigm. Even the simplest objects will become smart because they will be interconnected to other objects to share and collect data from the environments in which they are placed thus paving the way to novel application services, computing and communication scenarios.In this context, interoperability among different standards and communication technologies is still a significant challenge that we have started to address by proposing a smartphone-based mobile gateway acting as a flexible and transparent interface between different IoT devices and the Internet. The presented unified, high-level and extendible software architecture supports opportunistic IoT devices discovery, control and management coupled with data processing, collection and diffusion functionalities.A specific testbed on common smartphones with different hardware and software capabilities was deployed to evaluate the real feasibility of the designed solution measuring the system performance in terms of energy consumption, memory and CPU usage in high and low load scenarios. According to the obtained results, the implemented software architecture for multi-standard and multi-technology interoperation presents a reduced use of hardware resources in front of a relatively high energy consumption value, mostly due to the simultaneously active radio interfaces combined with a small battery capacity, that limits the smartphone lifetime. Nevertheless, the presented general approach is still remarkable because this latter aspect will most likely be exceeded, in a short time, thanks to daily technological advancements in both batteries and radio interfaces.