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.
@cite_15 offers cloud workload prediction based on autoregressive integrated moving average. They argue that proactive dynamic provisioning of resources could achieve good quality of service. Their model's accuracy is evaluated by predicting future workloads of real request traces to web servers. Additionally, @cite_3 developed a workload model for the CloudSim simulator using generalised extreme value lambda distributions. This model captures user behavioural patterns and supports the simulation of resource utilisation in clouds. They argue that user behaviour must be considered in workload modelling to reflect realistic conditions. Our approach share this view: we apply a runtime behaviour analysis to find a workflow enactment plan that best matches the infrastructure load including user activities.
@cite_15 @cite_3
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We model a web application to support analysis and simulation of cloud environments.We implement the model as an extension of the CloudSim simulator.We found that the user behavior has a strong influence on the resource utilization.We found Generalized Extreme Value and Generalized Lambda distributions instead of Exponential distribution to represent session time. Display Omitted Workload modeling enables performance analysis and simulation of cloud resource management policies, which allows cloud providers to improve their systems' Quality of Service (QoS) and researchers to evaluate new policies without deploying expensive large scale environments. However, workload modeling is challenging in the context of cloud computing due to the virtualization layer overhead, insufficient tracelogs available for analysis, and complex workloads. These factors contribute to a lack of methodologies and models to characterize applications hosted in the cloud. To tackle the above issues, we propose a web application model to capture the behavioral patterns of different user profiles and to support analysis and simulation of resources utilization in cloud environments. A model validation was performed using graphic and statistical hypothesis methods. An implementation of our model is provided as an extension of the CloudSim simulator. Workload variations on Internet platforms such as YouTube, Flickr, LastFM require novel approaches to dynamic resource provisioning in order to meet QoS requirements, while reducing the Total Cost of Ownership (TCO) of the infrastructures. The economy of scale promise of cloud computing is a great opportunity to approach this problem, by developing elastic large scale server infrastructures. However, a proactive approach to dynamic resource provisioning requires prediction models forecasting future load patterns. On the other hand, unexpected volume and data spikes require reactive provisioning for serving unexpected surges in workloads. When workload can not be predicted, adequate data grouping and placement algorithms may facilitate agile scaling up and down of an infrastructure. In this paper, we analyze a dynamic workload of an on-line music portal and present an elastic Web infrastructure that adapts to workload variations by dynamically scaling up and down servers. The workload is predicted by an autoregressive model capturing trends and seasonal patterns. Further, for enhancing data locality, we propose a predictive data grouping based on the history of content access of a user community. Finally, in order to facilitate agile elasticity, we present a data placement based on workload and access pattern prediction. The experimental results demonstrate that our forecasting model predicts workload with a high precision. Further, the predictive data grouping and placement methods provide high locality, load balance and high utilization of resources, allowing a server infrastructure to scale up and down depending on workload. The recent surge in the popularity and usage of Cloud Computing services by both the enterprise and individual consumers has necessitated efficient and proactive management of data center resources which host services having varied characteristics. One of the major issues concerning both the cloud service providers and consumers is the automatic scalability of resources (i.e., compute, storage and bandwidth) in response to the highly unpredictable demands. To this end, an opportunity exists to harness the predictive and diagnostic capabilities of machine learning approaches to incorporate dynamic scaling up and scaling down of resources without violating the Service Level Agreements (SLA) and simultaneously ensuring adequate revenue to the providers. This paper proposes, implements and evaluates a Bayesian Networks based predictive modeling framework to provide for an autonomic scaling of utility computing resources in the Cloud Computing scenario. In essence, the BN-based model captures the historical behavior of the system involving various performance metrics (indicators) and predicts the desired unknown metric (e.g. SLA parameter). Initial simulated experiments involving random demand scenarios provide insights into the feasibility and applicability of the proposed approach for improving the management of present data center facilities. 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. We consider a market-based resource allocation model for batch jobs in cloud computing clusters. In our model, we incorporate the importance of the due date of a job rather than the number of servers allocated to it at any given time. Each batch job is characterized by the work volume of total computing units (e.g., CPU hours) along with a bound on maximum degree of parallelism. Users specify, along with these job characteristics, their desired due date and a value for finishing the job by its deadline. Given this specification, the primary goal is to determine the scheduling of cloud computing instances under capacity constraints in order to maximize the social welfare (i.e., sum of values gained by allocated users). Our main result is a new ( C (C-k) ⋅ s (s-1))-approximation algorithm for this objective, where C denotes cloud capacity, k is the maximal bound on parallelized execution (in practical settings, k l C) and s is the slackness on the job completion time i.e., the minimal ratio between a specified deadline and the earliest finish time of a job. Our algorithm is based on utilizing dual fitting arguments over a strengthened linear program to the problem. Based on the new approximation algorithm, we construct truthful allocation and pricing mechanisms, in which reporting the job true value and properties (deadline, work volume and the parallelism bound) is a dominant strategy for all users. To that end, we provide a general framework for transforming allocation algorithms into truthful mechanisms in domains of single-value and multi-properties. We then show that the basic mechanism can be extended under proper Bayesian assumptions to the objective of maximizing revenues, which is important for public clouds. We empirically evaluate the benefits of our approach through simulations on data-center job traces, and show that the revenues obtained under our mechanism are comparable with an ideal fixed-price mechanism, which sets an on-demand price using oracle knowledge of users' valuations. Finally, we discuss how our model can be extended to accommodate uncertainties in job work volumes, which is a practical challenge in cloud settings. 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. Prediction of host load in Cloud systems is critical for achieving service-level agreements. However, accurate prediction of host load in Clouds is extremely challenging because it fluctuates drastically at small timescales. We design a prediction method based on Bayes model to predict the mean load over a long-term time interval, as well as the mean load in consecutive future time intervals. We identify novel predictive features of host load that capture the expectation, predictability, trends and patterns of host load. We also determine the most effective combinations of these features for prediction. We evaluate our method using a detailed one-month trace of a Google data center with thousands of machines. Experiments show that the Bayes method achieves high accuracy with a mean squared error of 0.0014. Moreover, the Bayes method improves the load prediction accuracy by 5.6 -- 50 compared to other state-of-the-art methods based on moving averages, auto-regression, and or noise filters. Scheduling workflows in cloud service environment has attracted great enthusiasm, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the scheduling environment. Ignoring these uncertain factors often leads to the violation of workflow deadlines and increases service renting costs of executing workflows. This study devotes to improving the performance for cloud service platforms by minimizing uncertainty propagation in scheduling workflow applications that have both uncertain task execution time and data transfer time. To be specific, a novel scheduling architecture is designed to control the count of workflow tasks directly waiting on each service instance (e.g., virtual machine). Based on this architecture, we develop an unceRtainty-aware Online Scheduling Algorithm (ROSA) to schedule dynamic and multiple workflows with deadlines. The proposed ROSA skillfully integrates both the proactive and reactive strategies. Then, on the basis of real-world workflow traces, five groups of simulation experiments are carried out to compare ROSA with five typical algorithms. The comparison results reveal that ROSA performs better than the five compared algorithms with respect to costs (up to 56 ), deviation (up to 70 ), resource utilization (up to 37 ) and fairness (up to 37). The advent of cloud computing promises highly available, efficient, and flexible computing services for applications such as web search, email, voice over IP, and web search alerts. Our experience at Google is that realizing the promises of cloud computing requires an extremely scalable backend consisting of many large compute clusters that are shared by application tasks with diverse service level requirements for throughput, latency, and jitter. These considerations impact (a) capacity planning to determine which machine resources must grow and by how much and (b) task scheduling to achieve high machine utilization and to meet service level objectives. Both capacity planning and task scheduling require a good understanding of task resource consumption (e.g., CPU and memory usage). This in turn demands simple and accurate approaches to workload classification-determining how to form groups of tasks (workloads) with similar resource demands. One approach to workload classification is to make each task its own workload. However, this approach scales poorly since tens of thousands of tasks execute daily on Google compute clusters. Another approach to workload classification is to view all tasks as belonging to a single workload. Unfortunately, applying such a coarse-grain workload classification to the diversity of tasks running on Google compute clusters results in large variances in predicted resource consumptions. This paper describes an approach to workload classification and its application to the Google Cloud Backend, arguably the largest cloud backend on the planet. Our methodology for workload classification consists of: (1) identifying the workload dimensions; (2) constructing task classes using an off-the-shelf algorithm such as k-means; (3) determining the break points for qualitative coordinates within the workload dimensions; and (4) merging adjacent task classes to reduce the number of workloads. We use the foregoing, especially the notion of qualitative coordinates, to glean several insights about the Google Cloud Backend: (a) the duration of task executions is bimodal in that tasks either have a short duration or a long duration; (b) most tasks have short durations; and (c) most resources are consumed by a few tasks with long duration that have large demands for CPU and memory. Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings' indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models. The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world traces from Cloud and Grid platforms. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources. Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are virtually isolated, performance fluctuations do occur because of resource sharing, and software or hardware failures (e.g. unstable virtual machines, power outages, etc.). In this paper, we propose a decentralized economic approach for dynamically adapting the cloud resources of various applications, so as to statistically meet their SLA performance and availability goals in the presence of varying loads or failures. According to our approach, the dynamic economic fitness of a Web service determines whether it is replicated or migrated to another server, or deleted. The economic fitness of a Web service depends on its individual performance constraints, its load, and the utilization of the resources where it resides. Cascading performance objectives are dynamically calculated for individual tasks in the application workflow according to the user requirements. By fully implementing our framework, we experimentally proved that our adaptive approach statistically meets the performance objectives under peak load periods or failures, as opposed to static resource settings. There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies. Estimating future runtime performance and cost is an essential task for Chief Information Officers in deciding whether to adopt a Cloud-based system. Benchmarking and simulation are two techniques that have long been practiced towards reliable estimation. Benchmarking involves (potentially) high cost and time consumption, but oftentimes yields more reliable estimates than simulation, while the simulation is much cheaper and faster than benchmarking, but less reliable. In order to deal with this dichotomy, we propose a complementary approach to estimating the performance of Cloud-based systems, whereby performance estimates can be obtained in a fast, inexpensive, and also reliable way. In this approach, the ontological concepts of a benchmark model, whose benchmark results have already been obtained, are mapped into those of a simulation model, while the mismatches and similarities between the two models are taken care of, through measures of similarity between the two. This ontology-driven construction of simulation models is intended not only to yield more reliable simulation results but also to help better explain why the simulation results may, or may not, be reliable. To validate our complementary approach, simulation models are constructed using CloudSim, and the simulation results are compared against the corresponding benchmark results, by using our prototype tool, collected from Amazon Web Service (AWS) and Google Compute Engine (GCE) by using the Yahoo! Cloud Serving Benchmark (YCSB) tool. These experiments show that the simulation results show about 90 accuracy with respect to the benchmark results, and additionally we feel we could better explain why this happens. Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms. This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices. The aim of Green Cloud Computing is to achieve a balance between the resource consumption and quality of service. In order to achieve this objective and to maintain the flexibility of the cloud, dynamic provisioning and allocation strategies are needed to regulate the internal settings of the cloud to address oscillatory peaks of workload. In this context, we propose strategies to optimize the use of the cloud resources without decreasing the availability. This work introduces two hybrid strategies based on a distributed system management model, describes the base strategies, operation principles, tests, and presents the results. We combine existing strategies to search their benefits. To test them, we extended CloudSim to simulate the organization model upon which we were based and to implement the strategies, using this improved version to validate our solution. Achieving a consumption reduction up to 87 comparing Standard Clouds with Green Clouds, and up to 52 comparing the proposed strategy with other Green Cloud Strategy. Since many Internet applications employ a multitier architecture, in this article, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, tier replication, load imbalances across replicas, and caching at intermediate tiers. We validate our model using real multitier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. Furthermore, our model successfully handles a comprehensive range of resource utilization---from 0 to near saturation for the CPU---for two separate tiers. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95p confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests minute, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the tiers by factors of 2 and 3.5, respectively. Execution of workflow applications in Cloud environments involves many uncertainties because of elastic resource provisioning and unstable performance of multitenant virtual machines (VM) instances over time. These uncertainties are usually either neglected by existing researches, or modeled with some probability distribution function. To address this gap, we extend a multi-objective workflow scheduling algorithm (MOHEFT) in two directions: (1) to deal with the dynamic nature of Cloud environments offering a potentially infinite amount of on-demand resources, and (2) to consider robustness as an objective that mitigates the variability in VM performance over time. Our new robust model, called R-MOHEFT, considers uncertainty in processing times of workflow activities without a precise estimation or known distribution function within an uncertainty interval. We approach this scheduling problem as a three-objective optimisation that considers makespan, monetary cost, and robustness as simultaneous objectives of a commercial Cloud environment. Our new algorithm is able to estimate the Pareto optimal set of scheduling solutions that resist against fluctuations in processing times three times better than its MOHEFT predecessor, with a tradeoff of only 15 worse Pareto frontier. R-MOHEFT's hypervolume suffers by only 5 to 16 , compared to the MOHEFT's drawback of 38 to surprisingly 87 , when the processing time fluctuates up to its double value. Current trends in data management systems, such as cloud and multi-tenant databases, are leading to data processing environments that concurrently execute heterogeneous query workloads. At the same time, these systems need to satisfy diverse performance expectations. In these newly-emerging settings, avoiding potential Quality-of-Service (QoS) violations heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a modeling approach to estimate the impact of concurrency on query performance for analytical workloads. Our solution relies on the analysis of query behavior in isolation, pairwise query interactions and sampling techniques to predict resource contention under various query mixes and concurrency levels. We introduce a simple yet powerful metric that accurately captures the joint effects of disk and memory contention on query performance in a single value. We also discuss predicting the execution behavior of a time-varying query workload through query-interaction timelines, i.e., a fine-grained estimation of the time segments during which discrete mixes will be executed concurrently. Our experimental evaluation on top of PostgreSQL TPC-H demonstrates that our models can provide query latency predictions within approximately 20 of the actual values in the average case.