The pervasive availability of streaming data is driving Fast Data platforms for low-latency streaming applications. Such applications need to respond to dynamism in the input rates and task behavior using scale-in and -out on elastic Cloud resources. Platforms like Apache Storm do not provide robust means to respond to such dynamism and for rapid task migration across VMs. We propose several dataflow checkpoint and migration approaches that allow a running streaming dataflow to migrate, without any loss of in-flight messages or their internal tasks states, while reducing the time to recover and stabilize. We implement these strategies on Storm and evaluate them using micro and application dataflows for scaling in and out on 2 – 21 Cloud VMs. Our results show that we can migrate large dataflows and catchup with their processing 75 faster than Storm, which takes over 140 secs. We also find that our approaches stabilize the application up to 42 faster, and there is no failure and re-processing of messages.
Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead ( The elasticity afforded by cloud computing allows consumers to dynamically request and relinquish computing and storage resources and pay for them on a pay-per-use basis. Cloud computing providers rely on virtualization techniques to manage the dynamic nature of their infrastructure allowing consumers to dynamically allocate and deallocate virtual machines of different capacities. Cloud providers need to optimally decide the best allocation of virtual machines to physical machines as the demand varies dynamically. When making such decisions, cloud providers can migrate VMs already allocated and or use external cloud providers. This paper considers the problem in which the cloud provider wants to maximize its revenue, subject to capacity, availability SLA, and VM migration constraints. The paper presents a heuristic solution, called Near Optimal (NOPT), to this NP-hard problem and discusses the results of its experimental evaluation in comparison with a best fit (BF) allocation strategy. The results show that NOPT provides a 45 improvement in average revenue when compared with BF for the parameters used in the experiment. Moreover, the NOPT algorithm maintained the availability close to one for all classes of users while BF exhibited a lower availability and even failed to meet the availability SLA at times.