Bara Abusalah

The examiner of cloud computing systems in the last few years observes that there is a trend of the emergence of a new Big Data framework almost every year. Since Hadoop was developed in 2007, new frameworks followed it such as Spark, Storm, Heron, Apex, Flink, Samza, Kafka ... etc. Each framework is developed in a certain way to target and achieve certain objectives better than other frameworks do. However, there are few common functionalities and aspects that are shared between these frameworks. One vital aspect all these frameworks strive to achieve is better reliability and faster recovery time in case of failures. This is particularly crucial for streaming systems (compared to batch processing systems) where events are processed and monitored online in real time, and any delay in data delivery will cause a major inconvenience to the users. Another observation is that some reliability implementations are redundant between different frameworks. Encapsulating these implementations into one layer and make it shared between different applications will benefit more than one framework without the burden of reimplementing the same reliability approach in each single framework. These observations motivated us to present Warden, a generic, multi-framework, flexible, customizable, low overhead protocol to ensure the resiliency of streaming applications running on streaming Big Data frameworks. Most reliability protocols carry out one rigid fault tolerance approach targeted towards one system at a time. It is more challenging to provide a reliability approach that is pluggable in multiple Big Data frameworks at a time and can achieve low overheads comparable with single targeted framework approaches, yet is flexible and customizable by its users to make it tailored towards their objectives. The genericity is attained by providing an interface that can be used in different applications from different frameworks. The low overhead is achieved by providing faster application finish times with and without failures. The customizability is fulfilled by providing the users the options to choose between two delivery semantics (Exactly Once / At Most Once) combined with two fault tolerance guarantees (Crash Failures / Byzantine Failures). To the best of our knowledge, such approach was never tried on multiple streaming frameworks before. We built a prototype of Warden on Flink and Samza (with Kafka) streaming frameworks. Our evaluations highlight the effectiveness of our approach in the presence of failures and without failures compared to other fault tolerance techniques (such as checkpointing).

Despite advances in making datacenters dependable, failures still happen. This is particularly onerous for long-running “bigdata” applications, where partial failures can lead to significant losses and lengthy recomputations. Big data processing frameworks like Hadoop MapReduce include fault tolerance (FT) mechanisms, but these are commonly targeted at specific system/failure models, andare often redundant between frameworks. This paper proposes the paradigm of dependable resources: big data processing frameworks are typically built on top of resource management systems (RMSs), and proposing FT support at the level of such an RMS yields generic FT mechanisms, which can be provided with low overhead by leveraging constraints on resources. We demonstrate our concepts through Guardian, a robust RMS based on Mesos and YARN. Guardian allows frameworks to run their applications with individually configurable FT granularity and degree, with only minor changes to their implementation. We demonstrate the benefits of our approach by evaluating Hadoop, Tez, Spark and Pig on a prototype of Guardian running on Amazon-EC2, improving completion time by around 68% in the presence of failures, while maintaining around 6% overhead.