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.
PaaS Cloud providers have to manage resources of customer applications to trade-off IaaS costs and QoS. CloudScale @cite_9 adjusts the resources assigned to each VM in a given host using a workload predictor. When the forecasted load for a host exceeds its capacity, the VM is migrated. @cite_6 propose an algorithm that analyzes the negative impact of VM migration on an IaaS provider. An optimization problem is formalized and best solution is provided using a hill climbing search.
@cite_9 @cite_6
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While early emphasis of Infrastructure as a Service (IaaS) clouds was on providing resource elasticity to end users, providers are increasingly interested in over-committing their resources to maximize the utilization and returns of their capital investments. In principle, over-committing resources hedges that users — on average — only need a small portion of their leased resources. When such hedge fails (i.e., resource demand far exceeds available physical capacity), providers must mitigate this provider-induced overload, typically by migrating virtual machines (VMs) to underutilized physical machines. Recent works on VM placement and migration assume the availability of target physical machines [1], [2]. However, in an over-committed cloud data center, this is not the case. VM migration can even trigger cascading overloads if performed haphazardly. In this paper, we design a new VM migration algorithm (called Scattered) that minimizes VM migrations in over-committed data centers. Compared to a traditional implementation, our algorithm can balance host utilization across all time epochs. Using real-world data traces from an enterprise cloud, we show that our migration algorithm reduces the risk of overload, minimizes the number of needed migrations, and has minimal impact on communication cost between VMs. As companies shift from desktop applications to cloud-based software as a service (SaaS) applications deployed on public clouds, the competition for end-users by cloud providers offering similar services grows. In order to survive in such a competitive market, cloud-based companies must achieve good quality of service (QoS) for their users, or risk losing their customers to competitors. However, meeting the QoS with a cost-effective amount of resources is challenging because workloads experience variation over time. This problem can be solved with proactive dynamic provisioning of resources, which can estimate the future need of applications in terms of resources and allocate them in advance, releasing them once they are not required. In this paper, we present the realization of a cloud workload prediction module for SaaS providers based on the autoregressive integrated moving average (ARIMA) model. We introduce the prediction based on the ARIMA model and evaluate its accuracy of future workload prediction using real traces of requests to web servers. We also evaluate the impact of the achieved accuracy in terms of efficiency in resource utilization and QoS. Simulation results show that our model is able to achieve an average accuracy of up to 91 percent, which leads to efficiency in resource utilization with minimal impact on the QoS. Recent years witness the proliferation of Infrastructure-as-a-Service (IaaS) cloud services, which provide on-demand resources (CPU, RAM, disk) in the form of virtual machines (VMs) for hosting applications services of third parties. Given the state-of-the-art IaaS offerings, it is still a problem of fundamental importance how the Application Service Providers (ASPs) should rent VMs from the clouds to serve their application needs, in order to minimize the cost while meeting their job demands over a long run. Cloud providers offer different pricing options to meet computing requirements of a variety of applications. However, the challenge facing an ASP is how these pricing options can be dynamically combined to serve arbitrary demands at the optimal cost. In this paper, we propose an online VM purchasing algorithm based on the Lyapunov optimization technique, for minimizing the long-term-averaged VM rental cost of an ASP with time-varying and delay-tolerant workloads, while bounding the maximum response delay of its jobs. In stark contrast with the existing studies, the proposed algorithm enables an ASP to optimally decide the amount of reserved, on-demand and spot instances to purchase simultaneously. Rigorous analysis shows that our algorithm can achieve a time-averaged resource cost close to the offline optimum. Trace-driven simulations further verify the efficacy of our algorithm. Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments The proposed InterCloud environment supports scaling of applications across multiple vendor clouds We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.