Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework.

IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges

Real-time structured light profilometry: a review

Distributed data processing framework MapReduce is increasingly deployed in Clouds to leverage the pay-per-usage cloud computing model. Popular Hadoop MapReduce environment expects that end users determine the type and amount of Cloud resources for reservation as well as the configuration of Hadoop parameters. However, such resource reservation and job provisioning decisions require in-depth knowledge of system internals and laborious but often ineffective parameter tuning. We propose and develop AROMA, a system that automates the allocation of heterogeneous Cloud resources and configuration of Hadoop parameters for achieving quality of service goals while minimizing the incurred cost. It addresses the significant challenge of provisioning ad-hoc jobs that have performance deadlines in Clouds through a novel two-phase machine learning and optimization framework. Its technical core is a support vector machine based performance model that enables the integration of various aspects of resource provisioning and auto-configuration of Hadoop jobs. It adapts to ad-hoc jobs by robustly matching their resource utilization signature with previously executed jobs and making provisioning decisions accordingly. We implement AROMA as an automated job provisioning system for Hadoop MapReduce hosted in virtualized HP ProLiant blade servers. Experimental results show AROMA's effectiveness in providing performance guarantee of diverse Hadoop benchmark jobs while minimizing the cost of Cloud resource usage.

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AROMA: automated resource allocation and configuration of mapreduce environment in the cloud

Modern resource management frameworks for large-scale analytics leave unresolved the problematic tension between high cluster utilization and job's performance predictability--respectively coveted by operators and users. We address this in Morpheus, a new system that: 1) codifies implicit user expectations as explicit Service Level Objectives (SLOs), inferred from historical data, 2) enforces SLOs using novel scheduling techniques that isolate jobs from sharing-induced performance variability, and 3) mitigates inherent performance variance (e.g., due to failures) by means of dynamic reprovisioning of jobs. We validate these ideas against production traces from a 50k node cluster, and show that Morpheus can lower the number of deadline violations by 5× to 13×, while retaining cluster-utilization, and lowering cluster footprint by 14% to 28%. We demonstrate the scalability and practicality of our implementation by deploying Morpheus on a 2700-node cluster and running it against production-derived workloads.

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Morpheus: towards automated SLOs for enterprise clusters

Autonomic server provisioning for performance assurance is a critical issue in data centers. It is important but challenging to guarantee an important performance metric, percentile-based end-to-end delay of requests flowing through a virtualized multi-tier server cluster. It is mainly due to dynamically varying workload and the lack of an accurate system performance model. In this paper, we propose a novel autonomic server allocation approach based on a model-independent and self-adaptive neural fuzzy control. There are model-independent fuzzy controllers that utilize heuristic knowledge in the form of rule base for performance assurance. Those controllers are designed manually on trial and error basis, often not effective in the face of highly dynamic workloads. We design the neural fuzzy controller as a hybrid of control theoretical and machine learning techniques. It is capable of self-constructing its structure and adapting its parameters through fast online learning. Unlike other supervised machine learning techniques, it does not require off-line training. We further enhance the neural fuzzy controller to compensate for the effect of server switching delays. Extensive simulations demonstrate the effectiveness of our new approach in achieving the percentile-based end-to-end delay guarantees. Compared to a rule-based fuzzy controller enabled server allocation approach, the new approach delivers superior performance in the face of highly dynamic workloads. It is robust to workload variation, change in delay target and server switching delays.

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Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee

Dynamic virtual server provisioning is critical to quality-of-service assurance for multitier Internet applications. In this paper, we address three important challenging problems. First, we propose an efficient server provisioning approach on multitier clusters based on an end-to-end resource allocation optimization model. It is to minimize the number of virtual servers allocated to the system while the average end-to-end response time guarantee is satisfied. Second, we design a model-independent fuzzy controller for bounding an important performance metric, the 90th-percentile response time of requests flowing through the multitier architecture. Third, to compensate for the latency due to the dynamic addition of virtual servers, we design a self-tuning component that adaptively adjusts the output scaling factor of the fuzzy controller according to the transient behavior of the end-to-end response time. Extensive simulation results, using two representative customer behavior models in a typical three-tier web cluster, demonstrate that the provisioning approach is able to significantly reduce the number of virtual servers allocated for the performance guarantee compared to an existing representative approach. The approach integrated with the model-independent self-tuning fuzzy controller can efficiently assure the average and the 90th-percentile end-to-end response time guarantees on multitier clusters.

Efficient Server Provisioning with Control for End-to-End Response Time Guarantee on Multitier Clusters

While MapReduce is inherently designed for batch and high throughput processing workloads, there is an increasing demand for non-batch processes on big data, e.g., interactive jobs, real-time queries, and stream computations. Emerging Apache Spark fills in this gap, which can run on an established Hadoop cluster and take advantages of existing HDFS. As a result, the deployment model of Spark-on-YARN is widely applied by many industry leaders. However, we identify three key challenges to deploy Spark on YARN, inflexible reservation-based resource management, inter-task dependency blind scheduling, and the locality interference between Spark and MapReduce applications. The three challenges cause inefficient resource utilization and significant performance deterioration. We propose and develop a cross-platform resource scheduling middleware, iKayak , which aims to improve the resource utilization and application performance in multi-tenant Spark-on-YARN clusters. iKayak relies on three key mechanisms: reservation-aware executor placement to avoid long waiting for resource reservation, dependency-aware resource adjustment to exploit under-utilized resource occupied by reduce tasks, and cross-platform locality-aware task assignment to coordinate locality competition between Spark and MapReduce applications. We implement iKayak in YARN. Experimental results on a testbed show that iKayak can achieve 50 percent performance improvement for Spark applications and 19 percent performance improvement for MapReduce applications, compared to two popular Spark-on-YARN deployment models, i.e., YARN-client model and YARN-cluster model.

Cross-Platform Resource Scheduling for Spark and MapReduce on YARN

While Hadoop ecosystems become increasingly important for practitioners of large-scale data analysis, they also incur tremendous energy cost. This trend is driving up the need for designing energy-efficient Hadoop clusters in order to reduce the operational costs and the carbon emission associated with its energy consumption. However, despite extensive studies of the problem, existing approaches for energy efficiency have not fully considered the heterogeneity of both workload and machine hardware found in production environments. In this paper, we find that heterogeneity-oblivious task assignment approaches are detrimental to both performance and energy efficiency of Hadoop clusters. Our observation shows that even heterogeneity-aware techniques that aim to reduce the job completion time do not guarantee a reduction in energy consumption of heterogeneous machines. We propose a heterogeneity-aware task assignment approach, E-Ant, that aims to improve the overall energy consumption in a heterogeneous Hadoop cluster without sacrificing job performance. It adaptively schedules heterogeneous workloads on energy-efficient machines, without a priori knowledge of the workload properties. E-Ant employs an ant colony optimization approach that generates task assignment solutions based on the feedback of each task’s energy consumption reported by Hadoop TaskTrackers in an agile way. Furthermore, we integrate DVFS technique with E-Ant to further improve the energy efficiency of heterogeneous Hadoop clusters. It relies on a DVFS controller to dynamically scale the CPU frequency of each slave machine in response to time-varying resource demands. Experimental results on a heterogeneous cluster with varying hardware capabilities show that E-Ant with DVFS improves the overall energy savings for a synthetic workload from Microsoft by 23 and 17 percent compared to Fair Scheduler and Tarazu, respectively.

Palden Lama

Papers

AROMA: automated resource allocation and configuration of mapreduce environment in the cloud

Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee

Efficient Server Provisioning with Control for End-to-End Response Time Guarantee on Multitier Clusters

Cross-Platform Resource Scheduling for Spark and MapReduce on YARN

Energy Efficiency Aware Task Assignment with DVFS in Heterogeneous Hadoop Clusters