# More on Pipelined Dynamic Scheduling of Big Data Streams
## 2 Related Work and Discussion
Based on the topology, it captures the percentage of the application throughput directly impacted by an operator and provides more resources to a congested operator with higher ETP to improve the overall application throughput. Based on the new execution speed achieved, Stela selects the next operator and repeats the process. For scale-in operations, Stela assumes that the user simply specifies the number of machines to be removed and chooses the most suitable machines for removal based on the ETPSum. The ETPSum for a machine is the sum of all ETP of instances of all operators that currently reside on the machine. This is an indicator of the machine's contribution to the overall throughput. Thus, machines with lower ETPSum are preferred for removal during scale-in. In [22], the authors propose two task migration approaches that allow a running streaming dataflow to migrate, without any loss of in-flight messages or their internal tasks states: (i) the Drain, Checkpoint, and Restore (DCR) policy pauses the source tasks'execution and allows the in-flight messages to execute across the dataflow until completion, thus draining the dataflow without any loss of messages. (ii) The Capture, Checkpoint, and Resume (CCR) directly broadcasts checkpoint events from the source task to each task in the dataflow and processes only one event, that is, the event that a task may currently be executing. In this way, extra latencies that incur from incremental flows of checkpoint messages and their full processing are reduced. [PERSON] et al. [23] introduced a scheme that performs live, progressive migrations without resorting to expensive synchronization barriers. In addition, they introduced algorithms for finding the optimal task assignment for the migration. The main components of the proposed migration mechanism include a migration manager (MM), a retriever of operator states from remote nodes, a re-router for misrouted tuples, and a file server that serves operator states to remote retrievers. When a migration is triggered, the master node notifies all the workers. Upon receiving such a notification, a worker updates its local task assignment. Then, the worker restarts the executors whose assigned tasks have changed. Finally, they introduced an algorithm to maintain statistics from past workloads to predict future migration costs. N-Storm [24] is a thread-level task migration scheme for Storm. N-Storm adds a key/value store on each worker node to make workers be aware of the changes of the overall task scheduling. In this regard, a worker node can manage its executors during run time (kill or start executors). With this mechanism, unnecessary stopping of executors and workers during a task migration is avoided and the performance of task migration is improved. Further they optimized N-Storm to make it efficient for multiple task migrations. [PERSON] et al. [25] modeled the elasticity problem for data stream processing as a linear programming problem, which is used to optimize different QoS metrics. For the operator migration, they proposed a general formulation of the reconfiguration costs and they considered that system performs the state migrations leveraging on a storage system, called DataStore which stores the operators code and allows replicas to save and restore their state when reconfigurations occur. In addition, the work of [PERSON] et al. [26] presented a migration cost model and a balance model as metrics to evaluate their migration strategy. Based on these models, a live data migration strategy with particle swarm optimization (PSO) was proposed, with two improvement measures, the loop context, and particle grouping. As an improvement of stream processing framework, the nested loop context structure supports the iterative optimization algorithm. Grouping particles before in-stream processing speeds up the convergence rate of the PSO policy. A different, however interesting approach called E-Storm [27] is a replication-based state management that actively maintains multiple state backups on different nodes.