WebWave: Globally Load Balanced Fully Distributed Caching of Hot Published Documents

Document publication service over such a large network as the Internet challenges us to harness available server and network resources to meet fast growing demand. In this paper, we show that large-scale dynamic caching can be employed to globally minimize server idle time, and hence maximize the aggregate server throughput of the whole service. To be efficient, scalable and robust, a successful caching mechanism must have three properties: (1) maximize the global throughput of the system, (2) find cache copies without recourse to a directory service, or to a discovery protocol, and (3) be completely distributed in the sense of operating only on the basis of local information. In this paper, we develop a precise definition, which we call tree load-balance (TLB), of what it means for a mechanism to satisfy these three goals. We present an algorithm that computes TLB off-line, and a distributed protocol that induces a load distribution that converges quickly to a TLB one. Both algorithms place cache copies of immutable documents, on the routing tree that connects the cached document's home server to its clients, thus enabling requests to stumble on cache copies en route to the home server.Harvard University; The Saudi Cultural Mission to the U.S.A

Maintaining Coherency of Dynamic Data in Cooperating Repositories

In this paper, we consider techniques for disseminating dynamic data—such as stock prices and real-time weather information—from sources to a set of repositories. We focus on the problem of maintaining coherency of dynamic data items in a network of cooperating repositories. We show that cooperation among repositories— where each repository pushes updates of data items to other repositories—helps reduce system-wide communication and computation overheads for coherency maintenance. However, contrary to intuition, we also show that increasing the degree of cooperation beyond a certain point can, in fact, be detrimental to the goal of maintaining coherency at low communication and computational overheads. We present techniques (i) to derive the “optimal” degree of cooperation among repositories, (ii) to construct an efficient dissemination tree for propagating changes from sources to cooperating repositories, and (iii) to determine when to push an update from one repository to another for coherency maintenance. We evaluate the efficacy of our techniques using real-world traces of dynamically changing data items (specifically, stock prices) and show that careful dissemination of updates through a network of cooperating repositories can substantially lower the cost of coherency maintenance

High-performance state-machine replication

Replication, a common approach to protecting applications against failures, refers to maintaining several copies of a service on independent machines (replicas). Unlike a stand-alone service, a replicated service remains available to its clients despite the failure of some of its copies. Consistency among replicas is an immediate concern raised by replication. In effect, an important factor for providing the illusion of an uninterrupted service to clients is to preserve consistency among the multiple copies. State-machine replication is a popular replication technique that ensures consistency by ordering client requests and making all the replicas execute them deterministically and sequentially. The overhead of ordering the requests, and the sequentiality of request execution, the two essential requirements in realizing state-machine replication, are also the two major obstacles that prevent the performance of state-machine replication from scaling. In this thesis we concentrate on the performance of state-machine replication and enhance it by overcoming the two aforementioned bottlenecks, the overhead of ordering and the overhead of sequentially executing commands. To realize a truly scalable system, one must iteratively examine and analyze all the layers and components of a system and avoid or eliminate potential performance obstructions and congestion points. In this dissertation, we iterate between optimizing the ordering of requests and the strategies of replicas at request execution, in order to stretch the performance boundaries of state-machine replication. To eliminate the negative implications of the ordering layer on performance, we devise and implement several novel and highly efficient ordering protocols. Our proposals are based on practical observations we make after closely assessing and identifying the shortcomings of existing approaches. Communication is one of the most important components of any distributed system and thus selecting efficient communication patterns is a must in designing scalable systems. We base our protocols on the most suitable communication patterns and extend their design with additional features that altogether realize our protocol's high efficiency. The outcome of this phase is the design and implementation of the Ring Paxos family of protocols. According to our evaluations these protocols are highly scalable and efficient. We then assess the performance ramifications of sequential execution of requests on the replicas of state-machine replication. We use some known techniques such as state-partitioning and speculative execution, and thoroughly examine their advantages when combined with our ordering protocols. We then exploit the features of multicore hardware and propose our final solution as a parallelized form of state-machine replication, built on top of Ring Paxos protocols, that is capable of accomplishing significantly high performance. Given the popularity of state-machine replication in designing fault-tolerant systems, we hope this thesis provides useful and practical guidelines for the enhancement of the existing and the design of future fault-tolerant systems that share similar performance goals

A taxonomy of web prediction algorithms

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques. This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted. © 2012 Elsevier Ltd. All rights reserved.This work has been partially supported by Spanish Ministry of Science and Innovation under Grant TIN2009-08201, Generalitat Valenciana under Grant GV/2011/002 and Universitat Politecnica de Valencia under Grant PAID-06-10/2424.Domenech, J.; De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Gil Salinas, JA.; Pont Sanjuan, A. (2012). A taxonomy of web prediction algorithms. Expert Systems with Applications. 39(9):8496-8502. https://doi.org/10.1016/j.eswa.2012.01.140S8496850239

Data Structures and Algorithms for Scalable NDN Forwarding

Named Data Networking (NDN) is a recently proposed general-purpose network architecture that aims to address the limitations of the Internet Protocol (IP), while maintaining its strengths. NDN takes an information-centric approach, focusing on named data rather than computer addresses. In NDN, the content is identified by its name, and each NDN packet has a name that specifies the content it is fetching or delivering. Since there are no source and destination addresses in an NDN packet, it is forwarded based on a lookup of its name in the forwarding plane, which consists of the Forwarding Information Base (FIB), Pending Interest Table (PIT), and Content Store (CS). In addition, as an in-network caching element, a scalable Repository (Repo) design is needed to provide large-scale long-term content storage in NDN networks. Scalable NDN forwarding is a challenge. Compared to the well-understood approaches to IP forwarding, NDN forwarding performs lookups on packet names, which have variable and unbounded lengths, increasing the lookup complexity. The lookup tables are larger than in IP, requiring more memory space. Moreover, NDN forwarding has a read-write data plane, requiring per-packet updates at line rates. Designing and evaluating a scalable NDN forwarding node architecture is a major effort within the overall NDN research agenda. The goal of this dissertation is to demonstrate that scalable NDN forwarding is feasible with the proposed data structures and algorithms. First, we propose a FIB lookup design based on the binary search of hash tables that provides a reliable longest name prefix lookup performance baseline for future NDN research. We have demonstrated 10 Gbps forwarding throughput with 256-byte packets and one billion synthetic forwarding rules, each containing up to seven name components. Second, we explore data structures and algorithms to optimize the FIB design based on the specific characteristics of real-world forwarding datasets. Third, we propose a fingerprint-only PIT design that reduces the memory requirements in the core routers. Lastly, we discuss the Content Store design issues and demonstrate that the NDN Repo implementation can leverage many of the existing databases and storage systems to improve performance

Protocols for the efficient dissemination of context-aware messages

Context-aware applications are able to react and adapt to the context of their users. This context includes, for instance, location, properties of the user or their surroundings, nearby devices, etc. Over the last years, powerful mobile devices, i.e., smartphones or tablet computers, have become an important part in many people's computing life. Most of these devices maintain a continuous high-speed network connection, allowing to provide distributed applications with an uninterrupted stream of data. Additionally, a huge number of sensors, both in these mobile devices and deployed in our surroundings, enable the creation of comprehensive context models. Such large-scale context models open up new possibilities for the development of context-aware applications by providing access to relevant context information from providers all over the world. However, until now, applications need to query the context model for relevant information or register for events or messages; it is not possible to "push" information to the mobile devices, neither from the infrastructure nor from other mobile devices. To support application developers, we propose Contextcast, a novel communication paradigm that allows for the dissemination of context-aware (or contextual) messages in a system of context-aware routers. This includes the fundamental semantics to address clients using context constraints and a reference dissemination scheme for such messages. To enable Contextcast to grow to scales similar to the context-aware systems that it is intended to be used with, we also propose a couple of optimized routing approaches. They are designed to reduce the number of maintenance messages that are necessary for the dissemination of contextual messages. One optimized routing algorithm uses coarse context information to reduce the amount of context updates propagated to routers. To this end, routers use the similarity of contexts to automatically find groups of similar clients, whose information can then be propagated as a single, coarse context. While this reduces the amount of context information to be propagated, the resulting information loss causes more messages to be forwarded, since routers no longer possess exact information to match against the constraints in contextual messages. A configurable similarity threshold allows for various trade-offs between the coarseness of the context information and the resulting additional message load. The second orthogonal routing approach relies on statistics to determine the characteristics of contexts and messages in the system. Without context knowledge, routers must assume the presence of a matching recipient and forward a message speculatively to disseminate it to all recipients. Using statistics, routers can determine how often certain messages occur and then calculate the benefit of propagating contexts corresponding to these messages. Several parameters enable an administrator to adjust how fast the system reacts to changes, depending on the observed messages and context updates. Additionally, temporal support extends Contextcast with a powerful mechanism that allows application developers and clients to address messages to certain contexts in the past or future. This includes an additional context attribute \cattr{time} and a constraint with various, easy to use temporal operators. We also propose efficient routing approaches for historical and future messages. Routing historical messages focuses on efficient routing while effectively protecting the clients' privacy, i.e., their respective context history. The routing approach for future messages delays forwarding messages until a matching context is registered, thus preventing needlessly forwarded messages

Live Streaming in P2P and Hybrid P2P-Cloud Environments for the Open Internet

Peer-to-Peer (P2P) live media streaming is an emerging technology that reduces the barrier to stream live events over the Internet. However, providing a high quality media stream using P2P overlay networks is challenging and gives raise to a number of issues: (i) how to guarantee quality of the service (QoS) in the presence of dynamism, (ii) how to incentivize nodes to participate in media distribution, (iii) how to avoid bottlenecks in the overlay, and (iv) how to deal with nodes that reside behind Network Address Translators gateways (NATs). In this thesis, we answer the above research questions in form of new algorithms and systems. First of all, we address problems (i) and (ii) by presenting our P2P live media streaming solutions: Sepidar, which is a multiple-tree overlay, and GLive, which is a mesh overlay. In both models, nodes with higher upload bandwidth are positioned closer to the media source. This structure reduces the playback latency and increases the playback continuity at nodes, and also incentivizes the nodes to provide more upload bandwidth. We use a reputation model to improve participating nodes in media distribution in Sepidar and GLive. In both systems, nodes audit the behaviour of their directly connected nodes by getting feedback from other nodes. Nodes who upload more of the stream get a relatively higher reputation, and proportionally higher quality streams. To construct our streaming overlay, we present a distributed market model inspired by Bertsekas auction algorithm, although our model does not rely on a central server with global knowledge. In our model, each node has only partial information about the system. Nodes acquire knowledge of the system by sampling nodes using the Gradient overlay, where it facilitates the discovery of nodes with similar upload bandwidth. We address the bottlenecks problem, problem (iii), by presenting CLive that satisfies real-time constraints on delay between the generation of the stream and its actual delivery to users. We resolve this problem by borrowing some resources (helpers) from the cloud, upon need. In our approach, helpers are added on demand to the overlay, to increase the amount of total available bandwidth, thus increasing the probability of receiving the video on time. As the use of cloud resources costs money, we model the problem as the minimization of the economical cost, provided that a set of constraints on QoS is satisfied. Finally, we solve the NAT problem, problem (iv), by presenting two NAT-aware peer sampling services (PSS): Gozar and Croupier. Traditional gossip-based PSS breaks down, where a high percentage of nodes are behind NATs. We overcome this problem in Gozar using one-hop relaying to communicate with the nodes behind NATs. Croupier similarly implements a gossip-based PSS, but without the use of relaying