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Distributed Placement of Autonomic Internet Services

机译：自主Internet服务的分布式放置

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The optimal placement of service facilities largely determines the capability of a data network to efficiently support its users' service demands. As centralized solutions over large-scale distributed environments are extremely expensive, inefficient or even infeasible, distributed approaches that rely on partial topology and demand information are the only credible approaches to the service placement problem, even at the expense of non-guaranteed optimality. In this paper, we propose a distributed service migration heuristic that iteratively solves instances of the 1-median problem pushing progressively the service to more cost-effective locations. Key to our algorithm is a traffic-aware centrality metric, called weighted conditional betweenness centrality (wCBC), that captures the ability of a node to act as service demand concentrator and is employed in both selecting the nodes and setting their weights for the 1-median problem instance. The assessment of our heuristic proceeds in two steps. First, assuming (ideal) knowledge of the invoked wCBC metric, we carry out a proof-of-concept study that demonstrates the effectiveness of the heuristic over synthetic and real-world topologies as well as its advantages against comparable local-search-like migration schemes. Next, we devise practical protocol implementations that approximate the heuristic using local measurements of transit traffic and preserve the excellent accuracy and fast convergence properties of the algorithm for different routing policies. Our solution applies to a broad range of networking scenarios, and is very relevant to the emerging trends for in-network storage and involvement of the end-user in the creation and distribution of lightweight (autonomic) service facilities.
机译：服务设施的最佳位置在很大程度上决定了数据网络有效支持其用户服务需求的能力。由于大规模分布式环境上的集中式解决方案非常昂贵，效率低下甚至不可行，因此依赖于局部拓扑和需求信息的分布式方法是解决服务放置问题的唯一可靠方法，即使是以非保证的最优性为代价。在本文中，我们提出了一种分布式服务迁移启发式方法，该方法迭代地解决了1-median问题的实例，从而逐步将服务推向更具成本效益的位置。该算法的关键是流量感知的中心度度量标准，称为加权条件中间性中心度（wCBC），它捕获节点充当服务需求集中器的能力，并在选择节点和设置其权重以用于1-中位数问题实例。我们对启发式收益的评估分为两个步骤。首先，假设（理想）了解所调用的wCBC指标，我们进行了概念验证研究，该研究证明了启发式方法相对于合成拓扑和实际拓扑的有效性，以及其在类似的类似本地搜索的迁移中的优势计划。接下来，我们设计实用的协议实现，该协议实现使用本地交通流量的测量值来近似启发式算法，并为不同的路由策略保留算法的出色准确性和快速收敛性。我们的解决方案适用于广泛的网络场景，并且与网络内存储的新兴趋势以及最终用户参与创建和分配轻型（自治）服务设施的趋势非常相关。

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