Workload scaling is an approach to accelerating computation and thus improving response times by replicating the exact same request multiple times and processing it in parallel on multiple nodes and accepting the result from the first node to finish. This is not unlike a TV game show, where the same question is given to multiple contestants and the (correct) answer is accepted from the first to respond. This is different than traditional strategies for parallelization as used in, say, MapReduce workloads, where each node runs a subset of the overall workload. There are a variety of strategies that trade off metrics such as cost, utilization, performance, and interprocessor communication requirements. Performance modeling can help determine optimal approaches for different environments and goals. This is important, because poor performance can lead to application and domain-specific losses, such as e-commerce conversions and sales. 1 Performance modeling and analysis plays an important role in designing and driving the selection of resource scaling mechanisms. Such modeling and analysis is complex due to time-varying workload arrival rates and request sizes, and even more complex in cloud environments due to the additional stochastic variation caused by performance interference due to resource sharing across co-located tenants. Moreover, little is known on how to multi-scale, i.e., dynamically and simultaneously
There are also alternative approaches that replicate the same request multiple times, to be processed in parallel by the different nodes, and to accept the result from the first node in order to finish REF .
Holistic Workload Scaling: A New Approach to Compute Acceleration in the Cloud
IEEE Cloud Computing
IEEE Cloud Computing
Computer Science