Increasing the Availability of Multi-object Tasks on Multi-region Distributed System

In a cloud distributed system, machine failure or region failure is a very common scenario. Data replication is a key technique for ensuring data availability. However, Objects are usually assumed independently by distributed systems, despite, a user-level task typically requests multiple data objects. This paper studies the effect of data placement on the availability of user-level tasks from a theoretical perspective, and finds the best and the worst placements which can provide the highest and the lowest availability for user-level tasks in a cloud distributed system. This paper also gives a novel algorithm called SPOverlap (S Parts Overlap), which provides a tradeoff between task availability and other system performance.