Understanding and modelling resources of Internet end hosts is essential for the design of desktop software and Internet-distributed applications. In this paper we develop a correlated resource model of Internet end hosts based on real trace data taken from the SETI@home project. This data covers a 5-year period with statistics for 2.7 million hosts. The resource model is based on statistical analysis of host computational power, memory, and storage as well as how these resources change over time and the correlations between them. We find that resources with few discrete values (core count, memory) are well modeled by exponential laws governing the change of relative resource quantities over time. Resources with a continuous range of values are well modeled with either correlated normal distributions (processor speed for integer operations and floating point operations) or log-normal distributions (available disk space). We validate and show the utility of the models by applying them to a resource allocation problem for Internet-distributed applications, and demonstrate their value over other models. We also make our trace data and tool for automatically generating realistic Internet end hosts publicly available.
The closest work described in @cite_15 gives a general characterization of Internet host resources. However, statistical models are not provided, and the evolution and dynamics of Internet resources are not investigated. Also, certain hardware attributes (such as cores) are not characterized or modeled due to the technology available at that time.
@cite_15
101653124 2134684361 137863291 2064359039
The Web is characterized by an extremely dynamic nature, as it is proved by the rapid and significant growth it has experimented in the last decade and by its continuous evolution through creation or deletion of pages and hyperlinks. Consequently, analyzing the temporal evolution of the Web has become a crucial task that can provide search engines with valuable information for refining crawling policies, improving ranking models or detecting spam. Understanding how the Web evolves over time is a delicate challenge that requires to integrate theoretical efforts of modelization and empirical results. Obtaining such findings is very expensive in terms of bandwidth, computation time and human intervention. Robust software is required to gather the data and provide easy access to the collected information. Apart for commercial engines, there have been only a few attempts to perform such task and to make the data available. Several previous works (e.g., ntoulas2004what,toyoda2006what ) analyzed a sequence of crawls, studying degree and frequency of change both in the content of Web pages and in the hyperlink structure to propose measures of their novelty. In this paper we study a temporal dataset BSVLTAG that has recently been made public. It is made of twelve 100M pages snapshots of the .uk domain. The Web graphs of the single snapshots have been merged into a global graph with labels that provide constant time access to temporal information. bordino2008temporal describes the work done to assess the data and to study some aspects of its evolution at the level of Web pages. We now analyze the structure of this huge time-aware graph at the level of interconnection between hosts. We study the , i.e., the graph in which every node corresponds to a site, whereas a directed edge represents the existence of hyperlinks between pages belonging to two different hosts. Understanding the graph structure of the Web at this macroscopic level can provide valuable insights for improving Web site accessibility and navigation, or discovering related hosts. The notion of hostgraph was proposed by bharat2001who . A few studies analyzed hostgraphs ( baeza2005spain,liu2005china,toyoda2008thai ). baeza2007domain presents a comparison among the results of twelve characterization studies of several national domains. In this paper, we investigate the suitability of embedding Internet hosts into a Euclidean space given their pairwise distances (as measured by round-trip time). Using the classical scaling and matrix perturbation theories, we first establish the (sum of the) magnitude of negative eigenvalues of the (doubly centered, squared) distance matrix as a measure of suitability of Euclidean embedding. We then show that the distance matrix among Internet hosts contains negative eigenvalues of large magnitude, implying that embedding the Internet hosts in a Euclidean space would incur relatively large errors. Motivated by earlier studies, we demonstrate that the inaccuracy of Euclidean embedding is caused by a large degree of triangle inequality violation (TIV) in the Internet distances, which leads to negative eigenvalues of large magnitude. Moreover, we show that the TIVs are likely to occur locally; hence the distances among these close-by hosts cannot be estimated accurately using a global Euclidean embedding. In addition, increasing the dimension of embedding does not reduce the embedding errors. Based on these insights, we propose a new hybrid model for embedding the network nodes using only a two-dimensional Euclidean coordinate system and small error adjustment terms. We show that the accuracy of the proposed embedding technique is as good as, if not better than, that of a seven-dimensional Euclidean embedding. Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable. Internet hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center operating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provision server resources for co-hosted services in a way that automatically adapts to offered load, improve the energy efficiency of server clusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs).Our system is based on an economic approach to managing shared server resources, in which services "bid" for resources as a function of delivered performance. The system continuously monitors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource availability, and can reduce server energy usage by 29 or more for a typical Web workload.