As computer networks increase in size, become more heterogeneous and span greater geographic distances, applications must be designed to cope with the very large scale, poor reliability, and often, with the extreme dynamism of the underlying network. Aggregation is a key functional building block for such applications: it refers to a set of functions that provide components of a distributed system access to global information including network size, average load, average uptime, location and description of hotspots, and so on. Local access to global information is often very useful, if not indispensable for building applications that are robust and adaptive. For example, in an industrial control application, some aggregate value reaching a threshold may trigger the execution of certain actions; a distributed storage system will want to know the total available free space; load-balancing protocols may benefit from knowing the target average load so as to minimize the load they transfer. We propose a gossip-based protocol for computing aggregate values over network components in a fully decentralized fashion. The class of aggregate functions we can compute is very broad and includes many useful special cases such as counting, averages, sums, products, and extremal values. The protocol is suitable for extremely large and highly dynamic systems due to its proactive structure—all nodes receive the aggregate value continuously, thus being able to track

With the significant advances in Information and Communications Technology (ICT) over the last half century, there is an increasingly perceived vision that computing will one day be the 5th utility (after water, electricity, gas, and telephony). This computing utility, like all other four existing utilities, will provide the basic level of computing service that is considered essential to meet the everyday needs of the general community. To deliver this vision, a number of computing paradigms have been proposed, of which the latest one is known as Cloud computing. Hence, in this paper, we define Cloud computing and provide the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs). We also provide insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA)-oriented resource allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global Cloud exchanges and markets. Then, we present some representative Cloud platforms, especially those developed in industries along with our current work towards realizing market-oriented resource allocation of Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the difference between High Performance Computing (HPC) workload and Internet-based services workload. We also describe a meta-negotiation infrastructure to establish global Cloud

We describe an efficient peer-to-peer information retrieval system, pSearch, that supports state-of-the-art content- and semantic-based full-text searches. pSearch avoids the scalability problem of existing systems that employ centralized indexing, or index/query flooding. It also avoids the nondeterminism that is exhibited by heuristic-based approaches. In pSearch, documents in the network are organized around their vector representations (based on modern document ranking algorithms) such that the search space for a given query is organized around related documents, achieving both eciency and accuracy.

Aggregation---that is, the computation of global properties like average or maximal load, or the number of nodes--- is an important basic functionality in fully distributed environments. In many cases---which include protocols responsible for self-organization in large-scale systems and collaborative environments---it is useful if all nodes know the value of some aggregates continuously. In this paper we present and analyze novel protocols capable of providing this service. The proposed anti-entropy aggregation protocols compute different aggregates of component properties like extremal values, average and counting. Our protocols are inspired by the anti-entropy epidemic protocol where random pairs of databases periodically resolve their differences. In the case of aggregation, resolving difference is generalized to an arbitrary (numeric) computation based on the states of the two communicating peers. The advantage of this approach is that it is proactive and "democratic", which means it has no performance bottlenecks, and the approximation of the aggregates is present continuously at all nodes. These properties make our protocol suitable for implementing e.g. collective decision making or automatic system maintenance based on global information in a fully distributed fashion. As our main contribution we provide fundamental theoretical results on the proposed averaging protocol.

Peer-to-peer (P2P) technology has undergone rapid growth, producing new protocols and applications, many of which enjoy considerable commercial success and academic interest. Yet, P2P applications are often based on complex protocols, whose behavior is not completely understood. We believe that in order to enable an even more widespread adoption of P2P systems in commercial and scientific applications, what is needed is a modular paradigm in which well-understood, predictable components with clean interfaces can be combined to implement arbitrarily complex functions. The goal of this paper is to promote this idea by describing our initial experiences in this direction. Our recent work has resulted in a collection of simple and robust components, which include aggregation and membership management. This paper shows how to combine them to obtain a novel load-balancing algorithm that is close to optimal with respect to load transfer. We also describe briefly our simulation environment, explicitly designed to efficiently support our modular approach to P2P protocol design.

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases.

Peer-to-Peer networks have become a major research topic over the last few years. Object location is a major part in the operation of these distributed systems. In this work, we present an overview of several search methods for unstructured peer-to-peer networks. Popular file-sharing applications, through which enormous amounts of data are daily exchanged, operate on such networks. We analyze the performance of the algorithms relative to their success rates, bandwidth consumption and adaptation to changing topologies. Simulation results are used to empirically evaluate their behavior in direct comparison. 1.

The exponential growth of data demands scalable infrastructures capable of indexing and searching rich content such as text, music, and images. A promising direction is to combine information retrieval with peer-to-peer technology for scalability, fault-tolerance, and low administration cost. One pioneering work along this direction is pSearch [32, 33]. pSearch places documents onto a peerto -peer overlay network according to semantic vectors produced using Latent Semantic Indexing (LSI). The search cost for a query is reduced since documents related to the query are likely to be co-located on a small number of nodes. Unfortunately, because of its reliance on LSI, pSearch also inherits the limitations of LSI. (1) When the corpus is large and heterogeneous, LSI's retrieval quality is inferior to methods such as Okapi. (2) The Singular Value Decomposition (SVD) used in LSI is unscalable in terms of both memory consumption and computation time.

P2P systems inherently have high scalability, robustness and fault tolerance because there is no centralized server and the network self-organizes itself. This is achieved at the cost of higher latency for locating the resources of interest in the P2P overlay network. Internet telephony can be viewed as an application of P2P architecture where the participants form a self-organizing P2P overlay network to locate and communicate with other participants. We propose a pure P2P architecture for the Session Initiation Protocol (SIP)-based IP telephony systems. Our P2P-SIP architecture supports basic user registration and call setup as well as advanced services such as offline message delivery, voice/video mails and multi-party conferencing.

Peer-to-Peer (P2P) file-sharing systems such as Gnutella, Morpheus and Freenet have recently attracted a lot of interest from the internet community because they realized a distributed infrastructure for sharing files. Such systems have shifted the Web's Client-Server model paradigm into a Client-Client model. The tremendous success of such systems has proven that purely distributed search systems are feasible and that they may change the way we interact on the Internet. P2P systems uncover many new exciting features such as robustness, scalability and high fault tolerance but with a price. Most research concentrates on optimizing the communication and data model of such systems but inadequate work has been done in area of analyzing such systems. Most approaches tend to use as their basis simulation models which can lead to wrong observations and solutions.