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. 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 increase in scale and complexity of large compute clusters motivates a need for representative workload benchmarks to evaluate the performance impact of system changes, so as to assist in designing better scheduling algorithms and in carrying out management activities. To achieve this goal, it is necessary to construct workload characterizations from which realistic performance benchmarks can be created. In this paper, we focus on characterizing run-time task resource usage for CPU, memory and disk. The goal is to find an accurate characterization that can faithfully reproduce the performance of historical workload traces in terms of key performance metrics, such as task wait time and machine resource utilization. Through experiments using workload traces from Google production clusters, we find that simply using the mean of task usage can generate synthetic workload traces that accurately reproduce resource utilizations and task waiting time. This seemingly surprising result can be justified by the fact that resource usage for CPU, memory and disk are relatively stable over time for the majority of the tasks. Our work not only presents a simple technique for constructing realistic workload benchmarks, but also provides insights into understanding workload performance in production compute clusters. 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. Abstract : In this paper, we analyze seven MapReduce workload traces from production clusters at Facebook and at Cloudera customers in e-commerce, telecommunications media, and retail. Cumulatively, these traces comprise over a year's worth of data logged from over 5000 machines, and contain over two million jobs that perform 1.6 exabytes of I O. Key observations include input data forms up to 77 of all bytes, 90 of jobs access KB to GB sized files that make up less than 16 of stored bytes, up to 60 of jobs re-access data that has been touched within the past 6 hours, peak-to-median job submission rates are 9:1 or greater, an average of 68 of all compute time is spent in map, task-seconds-per-byte is a key metric for balancing compute and data bandwidth task durations range from seconds to hours, and five out of seven workloads contain map-only jobs. We have also deployed a public workload repository with workload replay tools so that the researchers can systematically assess design priorities and compare performance across diverse MapReduce workloads. Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. At the highest level of the power hierarchy, we employ controlled underprovisioning and over-booking of power needs of hosted workloads. At the lower levels, we introduce the novel notion of soft fuses to flexibly distribute provisioned power among hosted workloads based on their needs. Our techniques are built upon a measurement-driven profiling and prediction framework to characterize key statistical properties of the power needs of hosted workloads and their aggregates. We characterize the gains in terms of the amount of computational work (CPU cycles) per provisioned unit of power Computation per Provisioned Watt (CPW). Our technique is able to double the CPWoffered by a Power Distribution Unit (PDU) running the e-commerce benchmark TPC-W compared to conventional provisioning practices. Over-booking the PDU by 10 based on tails of power profiles yields a further improvement of 20 . Reactive techniques implemented on our Xen VMM-based servers dynamically modulate CPU DVFS states to ensure power draw below the limits imposed by soft fuses. Finally, information captured in our profiles also provide ways of controlling application performance degradation despite overbooking. The 95th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec--a degradation of 12 . Abstract In this paper, we present a unifying analysis for redundancy systems with cancel-on-start ( c . o . s . ) and cancel-on-complete ( c . o . c . ) with exponentially distributed service requirements. With c . o . s . ( c . o . c . ) all redundant copies are removed as soon as one of the copies starts (completes) service. As a consequence, c . o . s . does not waste any computing resources, as opposed to c . o . c . We show that the c . o . s . model is equivalent to a queueing system with multi-type jobs and servers, which was analyzed in , (2012), and show that c . o . c . (under the assumption of i.i.d. copies) can be analyzed by a generalization of , (2012) where state-dependent departure rates are permitted. This allows us to show that the stationary distribution for both the c . o . c . and c . o . s . models has a product form. We give a detailed first-time analysis for c . o . s and derive a closed form expression for important metrics like mean number of jobs in the system, and probability of waiting. We also note that the c . o . s . model is equivalent to Join-Shortest-Work queue with power of d (JSW( d )). In the latter, an incoming job is dispatched to the server with smallest workload among d randomly chosen ones. Thus, all our results apply mutatis-mutandis to JSW( d ). Comparing the performance of c . o . s . with that of c . o . c . with i.i.d. copies gives the unexpected conclusion (since c . o . s . does not waste any resources) that c . o . s . is worse in terms of mean number of jobs. As part of ancillary results, we illustrate that this is primarily due to the assumption of i.i.d. copies in case of c . o . c . (together with exponentially distributed requirements) and that such assumptions might lead to conclusions that are qualitatively different from that observed in practice. In this paper, we study the performance characteristics of simple load sharing algorithms for heterogeneous distributed systems. We assume that nonnegligible delays are encountered in transferring jobs from one node to another. We analyze the effects of these delays on the performance of two threshold-based algorithms called Forward and Reverse. We formulate queuing theoretic models for each of the algorithms operating in heterogeneous systems under the assumption that the job arrival process at each node in Poisson and the service times and job transfer times are exponentially distributed. The models are solved using the Matrix-Geometric solution technique. These models are used to study the effects of different parameters and algorithm variations on the mean job response time: e.g., the effects of varying the thresholds, the impact of changing the probe limit, the impact of biasing the probing, and the optimal response times over a large range of loads and delays. Wherever relevant, the results of the models are compared with the M M 1 model, representing no load balancing (hereafter referred to as NLB), and the M M K model, which is an achievable lower bound (hereafter referred to as LB). Scientific workflows are increasingly containerised, which requires rethinking central processing unit (CPU) sharing policies to accommodate different workload types. However, container engines running scientific workflows struggle to share the CPU fairly, as workload characteristics are not taken into account. This paper proposes a sharing policy called the Adaptive Completely Fair Scheduling policy (adCFS), which considers the future state of CPU usage and proactively shares CPU cycles between various containers based on their corresponding workload metrics (e.g., CPU usage, task runtime, #tasks). adCFS estimates the weight of workload characteristics and redistributes the CPU based on the corresponding weights. The Markov chain model is used to predict CPU state use, and the adCFS policy is triggered to dynamically allocate containers to the proper CPU portions. Experimental results show enhanced container CPU response time for those containers that run heavy and large jobs: these display 12 faster response time compared with the default CFS (Completely Fair Scheduler). adCFS therefore enhances CFS by considering workload metrics, which leads to the CPU being shared fairly when it is fully used. Infrastructure-as-a-Service providers are offering their unused resources in the form of variable-priced virtual machines (VMs), known as "spot instances", at prices significantly lower than their standard fixed-priced resources. To lease spot instances, users specify a maximum price they are willing to pay per hour and VMs will run only when the current price is lower than the user's bid. This paper proposes a resource allocation policy that addresses the problem of running deadlineconstrained compute-intensive jobs on a pool of composed solely of spot instances, while exploiting variations in price and performance to run applications in a fast and economical way. Our policy relies on job runtime estimations to decide what are the best types of VMs to run each job and when jobs should run. Several estimation methods are evaluated and compared, using trace-based simulations, which take real price variation traces obtained from Amazon Web Services as input, as well as an application trace from the Parallel Workload Archive. Results demonstrate the effectiveness of running computational jobs on spot instances, at a fraction (up to 60 lower) of the price that would normally cost on fixed priced resources. 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. We consider MapReduce workloads that are produced by analytics applications. In contrast to ad hoc query workloads, analytics applications are comprised of fixed data flows that are run over newly arriving data sets or on different portions of an existing data set. Examples of such workloads include document analysis indexing, social media analytics, and ETL (Extract Transform Load). Motivated by these workloads, we propose a technique that predicts the runtime performance for a fixed set of queries running over varying input data sets. Our prediction technique splits each query into several segments where each segment’s performance is estimated using machine learning models. These per-segment estimates are plugged into a global analytical model to predict the overall query runtime. Our approach uses minimal statistics about the input data sets (e.g., tuple size, cardinality), which are complemented with historical information about prior query executions (e.g., execution time). We analyze the accuracy of predictions for several segment granularities on both standard analytical benchmarks such as TPC-DS [17], and on several real workloads. We obtain less than 25 prediction errors for 90 of predictions. Modern disk drives read-ahead data and reorder incoming requests in a workload-dependent fashion. This improves their performance, but makes simple analytical models of them inadequate for performance prediction, capacity planning, workload balancing, and so on. To address this problem we have developed a new analytic model for disk drives that do readahead and request reordering. We did so by developing performance models of the disk drive components (queues, caches, and the disk mechanism) and a workload transformation technique for composing them. Our model includes the effects of workload-specific parameters such as request size and spatial locality. The result is capable of predicting the behavior of a variety of real-world devices to within 17 across a variety of workloads and disk drives. In large-scale data enters, software and hardware failures are frequent, resulting in failures of job executions that may cause significant resource waste and performance deterioration. To proactively minimize the resource inefficiency due to job failures, it is important to identify them in advance using key job attributes. However, so far, prevailing research on datacenter workload characterization has overlooked job failures, including their patterns, root causes, and impact. In this paper, we aim to develop prediction models and mitigation policies for unsuccessful jobs, so as to reduce the resource waste in big data enters. In particular, we base our analysis on Google cluster traces, consisting of a large number of big-data jobs with a high task fan-out. We first identify the time-varying patterns of failed jobs and the contributing system features. Based on our characterization study, we develop an on-line predictive model for job failures by applying various statistical learning techniques, namely Linear Discriminate Analysis (LDA), Quadratic Discriminate Analysis (QDA), and Logistic Regression (LR). Furthermore, we propose a delay-based mitigation policy which, after a certain grace period, proactively terminates the execution of jobs that are predicted to fail. The particular objective of postponing job terminations is to strike a good tradeoffs between resource waste and false prediction of successful jobs. Our evaluation results show that the proposed method is able to significantly reduce the resource waste by 41.9 on average, and keep false terminations of jobs low, i.e., only 1 . The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd. 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. In the coming big data era, stress test to IT systems under extreme data volume is crucial to the adoption of computing technologies in every corner of the cyber world. Appropriately generated benchmark datasets provide the possibility for administrators to evaluate the capacity of the systems when real datasets hard obtained have not extreme cases. Traditional benchmark data generators, however, mainly target at producing relation tables of arbitrary size following fixed distributions. The output of such generators are insufficient when it is used to measure the stability of the architecture with extremely dynamic and heavy workloads, caused by complicated hiden factors in the generation mechanism of real world, e.g. dependency between stocks in the trading market and collaborative human behaviors on the social network. In this paper, we present a new framework, called Chronos, to support new demands on streaming data benchmarking, by generating and simulating realistic and fast data streams in an elastic manner. Given a small group of samples with timestamps, Chronos reproduces new data streams with similar characteristics of the samples, preserving column-wise correlations, temporal dependency and order statistics of the snapshot distributions at the same time. To achieve such realistic requirements, we propose 1) a column decomposition optimization technique to partition the original relation table into small sub-tables with minimal correlation information loss, 2) a generative and extensible model based on Latent Dirichlet Allocation to capture temporal dependency while preserving order statistics of the snapshot distribution, and 3) a new generation and assembling method to efficiently build tuples following the expected distribution on the snapshots. To fulfill the vision of elasticity, we also present a new parallel stream data generation mechanism, facilitating distributed nodes to collaboratively generate tuples with minimal synchronization overhead and excellent load balancing. Our extensive experimental studies on real world data domains confirm the efficiency and effectiveness of Chronos on stream benchmark generation and simulation. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. One role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web workload generation tool which mimics a set of real users accessing a server. The tool, called Surge (Scalable URL Reference Generator) generates references matching empirical measurements of 1) server file size distribution; 2) request size distribution; 3) relative file popularity; 4) embedded file references; 5) temporal locality of reference; and 6) idle periods of individual users. This paper reviews the essential elements required in the generation of a representative Web workload. It also addresses the technical challenges to satisfying this large set of simultaneous constraints on the properties of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner significantly different from other Web server benchmarks. 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.