On the Cost of Participating in a Peer-to-Peer Network

In this paper, we model the cost incurred by each peer participating in a peer-to-peer network. Such a cost model allows to gauge potential disincentives for peers to collaborate, and provides a measure of the ``total cost'' of a network, which is a possible benchmark to distinguish between proposals. We characterize the cost imposed on a node as a function of the experienced load and the node connectivity, and show how our model applies to a few proposed routing geometries for distributed hash tables (DHTs). We further outline a number of open questions this research has raised.Comment: 17 pages, 4 figures. Short version to be published in the Proceedings of the Third International Workshop on Peer-to-Peer Systems (IPTPS'04). San Diego, CA. February 200

An Improved Scheme for Interest Mining Based on a Reconfiguration of the Peer-to-Peer Overlay

Tan et al. proposed a scheme to improve the quality of a file search in unstructured Peer-to-Peer systems by focusing on the similarity of interest of the participating peers. Although it certainly improves the cost/performance ratio of a simple flooding-based scheme used in conventional systems, the Tan's method has a serious drawback such that a query cannot reach a target peer if a requesting peer is not connected with the target peer through a path consisting of peers to have similar interest to the given query. In order to overcome such drawback of the Tan's method, we propose a scheme to reconfigure the underlying network in such a way that a requesting peer has a neighbor interested in the given query, before transmitting a query to its neighbors. The performance of the proposed scheme is evaluated by simulation. The result of simulation indicates that it certainly overcomes the drawback of the Tan's method

EFFICIENT LOAD BALANCING IN PEER-TO-PEER SYSTEMS USING VIRTUAL SERVERS

Load balancing is a critical issue for the efficient operation of peer-to- peer networks. With the notion of virtual servers, peers participating in a heterogeneous, structured peer-to-peer (P2P) network may host different numbers of virtual servers, and by migrating virtual servers, peers can balance their loads proportional to their capacities. Peers participating in a Distributed Hash Table (DHT) are often heterogeneous. The existing and decentralized load balance algorithms designed for the heterogeneous, structured P2P networks either explicitly construct auxiliary networks to manipulate global information or implicitly demand the P2P substrates organized in a hierarchical fashion. Without relying on any auxiliary networks and independent of the geometry of the P2P substrates, this paper present ,a novel efficient, proximity-aware load balancing algorithm by using the concept of common virtual servers, that is unique in that each participating peer is based on the partial knowledge of the system to estimate the probability distributions of the capacities of peers and the loads of virtual servers. The movement cost can be reduced by using common virtual serve

Study of the Topology Mismatch Problem in Peer-to-Peer Networks

The advantages of peer-to-peer (P2P) technology are innumerable when compared to other systems like Distributed Messaging System, Client-Server model, Cloud based systems. The vital advantages are not limited to high scalability and low cost. On the other hand the p2p system suffers from a bottle-neck problem caused by topology mismatch. Topology mismatch occurs in an unstructured peer-to-peer (P2P) network when the peers participating in the communication choose their neighbors in random fashion, such that the resultant P2P network mismatches its underlying physical network, resulting in a lengthy communication between the peers and redundant network traffics generated in the underlying network[1] However, most P2P system performance suffers from the mismatch between the overlays topology and the underlying physical network topology, causing a large volume of redundant traffic in the Internet slowing the performance. This paper surveys the P2P topology mismatch problems and the solutions adapted for different applications

Multi-commodity optimization of peer-to-peer energy trading resources in smart grid

Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing (P2P-ETS). However, as more distributed energy resources integrate into the distribution network, the impact of the communication link becomes significant. We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS. On one hand, the proposed algorithm minimizes the cost of energy generation and communication delay. On the other hand, it also maximizes the global utility of prosumers with fair resource allocation. We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss. The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms. It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others

Peer Participation, Spillover Effects, and Evaluation of the Medicare Shared Savings Program

In the transition to value-based care, several alternative payment models (APM) have been developed to provide financial incentives for providers to deliver coordinated, high-quality care at lower costs. The Medicare Shared Savings Program (MSSP) is by far the largest voluntary APM in Medicare and continues to play a significant role in moving away from traditional fee-for-service incentives. However, evaluating the causal effect of the MSSP is challenging in the presence of practice interactions. Practices may be more likely to participate and perform well in the MSSP when their peer practices, with whom they share a lot of patients, engage in similar efforts. Practice interactions also likely provide channels for the effects of MSSP to extend to non-MSSP beneficiaries. Little is known about the role of practice interactions in the participation and outcomes of the MSSP. The overall objective of this study is to leverage a network analytic approach based on patient sharing patterns across practices, combined with quasi-experimental designs, to examine the relationships between peer participation, spillover effects, and the performance of the MSSP. In Aim 1, I estimate the magnitude of peer effects in MSSP participation. In Aim 2, I examine whether peer participation modifies the effect of ACO on beneficiary-level outcomes. In Aim 3, I leverage patient geographic migration to estimate the effect of market-level MSSP penetration on non-MSSP beneficiaries' outcomes. Results from this study show that at the practice level, more peers participating in the MSSP increase the probability of an index practice participating in the program. At the beneficiary level, the effect of ACO on cost savings is more pronounced for beneficiaries attributed to the primary care practices with higher peer ACO participation rates. At the market level, we find evidence of spillover effects on outpatient service utilization, and the direction of spillover effects depends on outpatient care settings. These findings suggest peer effects are an important mechanism aligning voluntary participation across practices and can be better leveraged to achieve cost savings and better patient outcomes under the MSSP. The evidence of spillover effects suggests that the MSSP-induced change in outpatient care patterns has extended to non-MSSP beneficiaries.Doctor of Philosoph

Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints

In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable energy generation and consumption, (ii) difficulties in developing an accurate and efficient energy trading model, and (iii) the need to satisfy distribution network constraints, it is challenging for prosumers to obtain optimal energy trading decisions that minimize their individual energy costs. To address the challenge, we first formulate the above problem as a Markov decision process and propose a multi-agent deep deterministic policy gradient algorithm to learn optimal energy trading decisions. To satisfy the distribution network constraints, we propose distribution network tariffs which we incorporate in the algorithm as incentives to incentivize energy trading decisions that help to satisfy the constraints and penalize the decisions that violate them. The proposed algorithm is model-free and allows the agents to learn the optimal energy trading decisions without having prior information about other agents in the network. Simulation results based on real-world datasets show the effectiveness and robustness of the proposed algorithm

Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints

In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable energy generation and consumption, (ii) difficulties in developing an accurate and efficient energy trading model, and (iii) the need to satisfy distribution network constraints, it is challenging for prosumers to obtain optimal energy trading decisions that minimize their individual energy costs. To address the challenge, we first formulate the above problem as a Markov decision process and propose a multi-agent deep deterministic policy gradient algorithm to learn optimal energy trading decisions. To satisfy the distribution network constraints, we propose distribution network tariffs which we incorporate in the algorithm as incentives to incentivize energy trading decisions that help to satisfy the constraints and penalize the decisions that violate them. The proposed algorithm is model-free and allows the agents to learn the optimal energy trading decisions without having prior information about other agents in the network. Simulation results based on real-world datasets show the effectiveness and robustness of the proposed algorithm