Haricharan Ramachandra

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Internet companies like LinkedIn handle a large amount of incoming web traffic. Events generated in response to user input or actions are stored in a source database. These database events feature the typical characteristics of Big Data: high volume, high velocity and high variability. Database events are replicated to isolate source database and form a(More)
For enterprise applications that deal with large scale of data, storage IO is oftentimes the performance bottleneck. SSD (Solid State Drive) is increasingly being adopted by companies/applications to alleviate applications' IO bottleneck. However, not every application/product is justified to migrate to SSD from HDD (Hard Disk Drive), as such migration will(More)
Data quality is essential in big data paradigm as poor data can have serious consequences when dealing with large volumes of data. While it is trivial to spot poor data for small-scale and offline use cases, it is challenging to detect and fix data inconsistency in large-scale and online (real-time or near-real time) big data context. An example of such(More)
Cloud Computing promises a cost-effective and administration-effective solution to the traditional needs of computing resources. While bringing efficiency to the users thanks to the shared hardware and software, the multi-tenency characteristics also bring unique challenges to the backend cloud platforms. In particular, the JVM mechanisms used by Java(More)
For PaaS-deployed (Platform as a Service) customer-facing applications (e.g., online gaming and online chatting), ensuring low latencies is not just a preferred feature, but a must-have feature. Given the popularity and powerfulness of Java platforms, a significant portion of today's PaaS platforms run Java. JVM (Java Virtual Machine) manages a heap space(More)
Ensuring low replication latency of database events is business-critical but challenging with big data. A proposed capacity-planning model helps achieve this goal by forecasting future traffic rates, predicting replication latency, and determining required replication capacity. The Web extra at http://youtu.be/ZupPlrS8dGA is a video of in which author(More)
Large-scale web services like LinkedIn serve millions of users across the globe. The user experience depends on high service availability and performance of the services. In such a scenario, capacity measurement is critical for these cloud services. Resources should be provisioned such that the service can easily handle peak traffic without experiencing(More)
Linux kernel feature of Cgroups (Control Groups) is being increasingly adopted for running applications in multi-tenanted environments. Many projects (e.g., Docker) rely on cgroups to isolate resources such as CPU and memory. It is critical to ensure high performance for such deployments. At LinkedIn, we have been using Cgroups and investigated its(More)