Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly ( 20 ) better workload predictions for the future of simulated clouds than random workload selection.
Concerning workload modelling, @cite_0 used data traces obtained from a data centre to characterise and predict workload on VMs. Their goal was to explore cross-VM workload correlations, and predict workload changes due to dependencies among applications running in different VMs -- while we approach the load prediction from the workflow enactment point of view.
@cite_0
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Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy emerging computing requirements. To efficiently provision computing resources in the cloud, system administrators need the capabilities of characterizing and predicting workload on the Virtual Machines (VMs). In this paper, we use data traces obtained from a real data center to develop such capabilities. First, we search for repeatable workload patterns by exploring cross-VM workload correlations resulted from the dependencies among applications running on different VMs. Treating workload data samples as time series, we develop a co-clustering technique to identify groups of VMs that frequently exhibit correlated workload patterns, and also the time periods in which these VM groups are active. Then, we introduce a method based on Hidden Markov Modeling (HMM) to characterize the temporal correlations in the discovered VM clusters and to predict variations of workload patterns. The experimental results show that our method can not only help better understand group-level workload characteristics, but also make more accurate predictions on workload changes in a cloud. There has been a growing effort in decreasing energy consumption of large-scale cloud data centers via maximization of host-level utilization and load balancing techniques. However, with the recent introduction of Container as a Service (CaaS) by cloud providers, maximizing the utilization at virtual machine (VM) level becomes essential. To this end, this paper focuses on finding efficient virtual machine sizes for hosting containers in such a way that the workload is executed with minimum wastage of resources on VM level. Suitable VM sizes for containers are calculated, and application tasks are grouped and clustered based on their usage patterns obtained from historical data. Furthermore, tasks are mapped to containers and containers are hosted on their associated VM types. We analyzed clouds' trace logs from Google cluster and consider the cloud workload variances, which is crucial for testing and validating our proposed solutions. Experimental results showed up to 7.55 improvement in the average energy consumption compared to baseline scenarios where the virtual machine sizes are fixed. In addition, comparing to the baseline scenarios, the total number of VMs instantiated for hosting the containers is also improved by 68 on average. Virtual Machines are becoming increasingly valuable to resource consolidation and management, providing efficient and secure resource containers, along with desired application execution environments. This paper focuses on the VM-based resource reservation problem, that is, the reservations of CPU, memory and network resources for individual VM instances, as well as for VM clusters. In particular, it considers the scenario where one or several physical servers need to be vacated to start a cluster of VMs for dedicated execution of parallel jobs. VMs provide a primitive for transparently vacating workloads through migration; however, the process of migrating several VMs can be time-consuming and needs to be estimated. To achieve this goal, this paper seeks to provide a model that can characterize the VM migration process and predict its performance, based on a comprehensive experimental analysis. The results show that, given a certain VM's migration time, it is feasible to predict the time for a VM with other configurations, as well as the time for migrating a number of VMs. The paper also shows that migration of VMs in parallel results in shorter aggregate migration times, but with higher per-VM migration latencies. Experimental results also quantify the benefits of buffering the state of migrated VMs in main memory without committing to hard disks. This paper proposes and evaluates an approach for power and performance management in virtualized server clusters. The major goal of our approach is to reduce power consumption in the cluster while meeting performance requirements. The contributions of this paper are: (1) a simple but effective way of modeling power consumption and capacity of servers even under heterogeneous and changing workloads, and (2) an optimization strategy based on a mixed integer programming model for achieving improvements on power-efficiency while providing performance guarantees in the virtualized cluster. In the optimization model, we address application workload balancing and the often ignored switching costs due to frequent and undesirable turning servers on off and VM relocations. We show the effectiveness of the approach applied to a server cluster test bed. Our experiments show that our approach conserves about 50 of the energy required by a system designed for peak workload scenario, with little impact on the applications' performance goals. Also, by using prediction in our optimization strategy, further QoS improvement was achieved.