1,553 research outputs found

    A Holistic Approach to Lowering Latency in Geo-distributed Web Applications

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    User perceived end-to-end latency of web applications have a huge impact on the revenue for many businesses. The end-to-end latency of web applications is impacted by: (i) User to Application server (front-end) latency which includes downloading and parsing web pages, retrieving further objects requested by javascript executions; and (ii) Application and storage server(back-end) latency which includes retrieving meta-data required for an initial rendering, and subsequent content based on user actions. Improving the user-perceived performance of web applications is challenging, given their complex operating environments involving user-facing web servers, content distribution network (CDN) servers, multi-tiered application servers, and storage servers. Further, the application and storage servers are often deployed on multi-tenant cloud platforms that show high performance variability. While many novel approaches like SPDY and geo-replicated datastores have been developed to improve their performance, many of these solutions are specific to certain layers, and may have different impact on user-perceived performance. The primary goal of this thesis is to address the above challenges in a holistic manner, focusing specifically on improving the end-to-end latency of geo-distributed multi-tiered web applications. This thesis makes the following contributions: (i) First, it reduces user-facing latency by helping CDNs identify and map objects that are more critical for page-load latency to the faster CDN cache layers. Through controlled experiments on real-world web pages, we show the potential of our approach to reduce hundreds of milliseconds in latency without affecting overall CDN miss rates. (ii) Next, it reduces back-end latency by optimally adapting the datastore replication policies (including number and location of replicas) to the heterogeneity in workloads. We show the benefits of our replication models using real-world traces of Twitter, Wikipedia and Gowalla on a 8 datacenter Cassandra cluster deployed on EC2. (iii) Finally, it makes multi-tier applications resilient to the inherent performance variability in the cloud through fine-grained request redirection. We highlight the benefits of our approach by deploying three real-world applications on commercial cloud platforms

    DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

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    Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments

    Web Replica Hosting Systems

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