2015 International Conference on Cloud and Autonomic Computing最新文献_第3页

Enhancing Cloud Energy Models for Optimizing Datacenters Efficiency 增强云能量模型以优化数据中心效率

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.10

E. Outin, Jean-Emile Dartois, Olivier Barais, Jean-Louis Pazat

{"title":"Enhancing Cloud Energy Models for Optimizing Datacenters Efficiency","authors":"E. Outin, Jean-Emile Dartois, Olivier Barais, Jean-Louis Pazat","doi":"10.1109/ICCAC.2015.10","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.10","url":null,"abstract":"Due to high electricity consumption in the Cloud datacenters, providers aim at maximizing energy efficiency through VM consolidation, accurate resource allocation or adjusting VM usage. More generally, the provider attempts to optimize resource utilization. However, while minimizing expenses, the Cloud operator still needs to conform to SLA constraints negotiated with customers (such as latency, downtime, affinity, placement, response time or duplication). Consequently, optimizing a Cloud configuration is a multi-objective problem. As a nontrivial multi-objective optimization problem, there does not exist a single solution that simultaneously optimizes each objective. There exists a (possibly infinite) number of Pareto optimal solutions. Evolutionary algorithms are popular approaches for generating Pareto optimal solutions to a multi-objective optimization problem. Most of these solutions use a fitness function to assess the quality of the candidates. However, regarding the energy consumption estimation, the fitness function can be approximative and lead to some imprecisions compared to the real observed data. This paper presents a system that uses a genetic algorithm to optimize Cloud energy consumption and machine learning techniques to improve the fitness function regarding a real distributed cluster of server. We have carried out experiments on the OpenStack platform to validate our solution. This experimentation shows that the machine learning produces an accurate energy model, predicting precise values for the simulation.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121315039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

A Job Dispatch Optimization Method on Cluster and Cloud for Large-Scale High-Throughput Computing Service 大规模高吞吐量计算服务的集群和云作业调度优化方法

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.42

Jieun Choi, Seoyoung Kim, Theodora Adufu, Soonwook Hwang, Yoonhee Kim

{"title":"A Job Dispatch Optimization Method on Cluster and Cloud for Large-Scale High-Throughput Computing Service","authors":"Jieun Choi, Seoyoung Kim, Theodora Adufu, Soonwook Hwang, Yoonhee Kim","doi":"10.1109/ICCAC.2015.42","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.42","url":null,"abstract":"Cloud technologies, clusters and grids have actively supported large-scale scientific computing over the years. Whereas these technologies provide unlimited computing resources, combining them with the existing infrastructures to effectively support demanding scientific applications is more and more laborious. In this paper, we design a service architecture and propose an algorithm to optimize job distribution on a cluster and a cloud using HTCaaS. HTCaaS is a pilot job-based multilevel scheduling system for large-scale scientific computing in Korea. In addition, we present a newly added cloud module on HTCaaS which is based on OpenStack. We implement and validate the algorithm in HTCaaS. A preliminary experiment is also conducted to find an optimal distribution ratio for CPU-intensive jobs and I/O-intensive jobs in our cloud and cluster environments. We compare our method to a baseline approach which distributes tasks in proportions of the number of cores each resource has in order to validate the proposed job dispatch optimization method. Experimental results show that the proposed method can improve throughput and match tasks to appropriate resources using adaptive job distribution ratio in cloud and cluster environments.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints 协调CPU和内存弹性控制器以满足业务响应时间约束

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.20

Soodeh Farokhi, Ewnetu Bayuh Lakew, C. Klein, I. Brandić, E. Elmroth

{"title":"Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints","authors":"Soodeh Farokhi, Ewnetu Bayuh Lakew, C. Klein, I. Brandić, E. Elmroth","doi":"10.1109/ICCAC.2015.20","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.20","url":null,"abstract":"Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under-or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126469345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38

Model-Based Autonomic and Performance-Aware System Adaptation in Heterogeneous Resource Environments: A Case Study 异构资源环境中基于模型的自主和性能感知系统适应:一个案例研究

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.27

Nikolaus Huber, J. Walter, Manuel Bahr, Samuel Kounev

{"title":"Model-Based Autonomic and Performance-Aware System Adaptation in Heterogeneous Resource Environments: A Case Study","authors":"Nikolaus Huber, J. Walter, Manuel Bahr, Samuel Kounev","doi":"10.1109/ICCAC.2015.27","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.27","url":null,"abstract":"Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. In this paper, we apply a holistic model-based approach to engineering performance-aware system adaptation. More specifically, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. The conducted case study evaluates the applicability and effectiveness of our approach and demonstrates that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management in heterogeneous environments. We apply a holistic model-based approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. The application of DML enables to automatically adapt service infrastructures to changing customer workloads and service-level agreements in heterogeneous environments.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122002652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Evaluating Degrees of Multitenancy Isolation: A Case Study of Cloud-Hosted GSD Tools 评估多租户隔离程度:云托管GSD工具的案例研究

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.17

L. Ochei, J. Bass, Andrei V. Petrovski

{"title":"Evaluating Degrees of Multitenancy Isolation: A Case Study of Cloud-Hosted GSD Tools","authors":"L. Ochei, J. Bass, Andrei V. Petrovski","doi":"10.1109/ICCAC.2015.17","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.17","url":null,"abstract":"Multitenancy is an essential cloud computing property where a single instance of an application serves multiple tenants. Multitenancy introduces significant challenges when deploying application components to the cloud due to the demand for different degrees of isolation between tenants. At the very basic degree of isolation, tenants still share application components as much as possible. However, while some components may benefit from low degree of isolation between tenants, others may need a higher degree of isolation, for instance, in a situation where a component is too critical to be shared, or needs to be configured specifically for individual tenants. This paper describes COMITRE (COmponent-based approach to Multitenancy Isolation Through request RE-routing) to empirically evaluate the degree of isolation between tenants enabled by three multitenancy patterns (i.e., shared component, tenant-isolated component, and dedicated component) for cloud-hosted Global Software Development (GSD) tools. We developed a multitenant component for each multitenancy pattern, integrated it within Hudson, and then compared their impact on different tenants. The study revealed among other things that a component deployed based on shared component offers a lower degree of tenant isolation (than tenant-isolated component and dedicated component) when one of the tenants is exposed to a demanding deployment condition (e.g, large instant loads). We also provide some recommendations to guide an architect in implementing multitenancy isolation on a set of GSD tools: Hudson, Subversion and Bugzilla.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"26 Pt 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Near-Optimal Allocation of VMs from IaaS Providers by SaaS Providers SaaS提供商从IaaS提供商处获得近乎最优的虚拟机分配

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.16

Arwa Aldhalaan, D. Menascé

引用次数: 13

Teaching and Training Cybersecurity as a Cloud Service 作为云服务的网络安全教学和培训

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.47

Cihan Tunc, S. Hariri, Fabian De La Peña Montero, Farah Fargo, P. Satam, Y. Al-Nashif

{"title":"Teaching and Training Cybersecurity as a Cloud Service","authors":"Cihan Tunc, S. Hariri, Fabian De La Peña Montero, Farah Fargo, P. Satam, Y. Al-Nashif","doi":"10.1109/ICCAC.2015.47","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.47","url":null,"abstract":"The explosive growth of IT infrastructures, cloud systems, and Internet of Things (IoT) have resulted in complex systems that are extremely difficult to secure and protect against cyberattacks which are growing exponentially in complexity and in number. Overcoming the cybersecurity challenges is even more complicated due to the lack of training and widely available cybersecurity environments to experiment with and evaluate new cybersecurity methods. The goal of our research is to address these challenges by exploiting cloud services. In this paper, we present the design, analysis, and evaluation of a cloud service that we refer to as Cybersecurity Lab as a Service (CLaaS) which offers virtual cybersecurity experiments that can be accessed from anywhere and from any device (desktop, laptop, tablet, smart mobile device, etc.) with Internet connectivity. In CLaaS, we exploit cloud computing systems and virtualization technologies to provide virtual cybersecurity experiments and hands-on experiences on how vulnerabilities are exploited to launch cyberattacks, how they can be removed, and how cyber resources and services can be hardened or better protected. We also present our experimental results and evaluation of CLaaS virtual cybersecurity experiments that have been used by graduate students taking our cybersecurity class as well as by high school students participating in GenCyber camps.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

An Autonomic Legal-Rule Aware Cloud Service Broker 一个自主的法律规则感知云服务代理

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.24

E. Casalicchio

引用次数: 2

Event-Driven Application Brownout: Reconciling High Utilization and Low Tail Response Times 事件驱动的应用程序限电:协调高利用率和低尾响应时间

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.25

David Desmeurs, C. Klein, A. Papadopoulos, Johan Tordsson

{"title":"Event-Driven Application Brownout: Reconciling High Utilization and Low Tail Response Times","authors":"David Desmeurs, C. Klein, A. Papadopoulos, Johan Tordsson","doi":"10.1109/ICCAC.2015.25","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.25","url":null,"abstract":"Data centers currently waste a lot of energy, due to lack of energy proportionality and low resource utilization, the latter currently being necessary to ensure application responsiveness. To address the second concern we propose a novel application-level technique that we call event-driven Brownout. For each request, i.e., in an event-driven manner, the application can execute some optional code that is not required for correct operation but desirable for user experience, and does so only if the number of pending client requests is below a given threshold. We propose several autonomic algorithms, based on control theory and machine learning, to automatically tune this threshold based on measured application 95th percentile response times. We evaluate our approach using the RUBiS benchmark which shows a 11-fold improvement in maintaining response-time close to a set-point at high utilization compared to competing approaches. Our contribution is opening the path to more energy efficient data-centers, by allowing applications to keep response times close to a set-point even at high resource utilization.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114483411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Proactive Management of Systems via Hybrid Analytic Techniques 通过混合分析技术的系统主动管理

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.31

Ji Xue, Feng Yan, Alma Riska, E. Smirni

{"title":"Proactive Management of Systems via Hybrid Analytic Techniques","authors":"Ji Xue, Feng Yan, Alma Riska, E. Smirni","doi":"10.1109/ICCAC.2015.31","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.31","url":null,"abstract":"In today's scaled out systems, co-scheduling data analytics work with high priority user workloads is common as it utilizes better the vast hardware availability. User workloads are dominated by periodic patterns, with alternating periods of high and low utilization, creating promising conditions to schedule data analytics work during low activity periods. To this end, we show the effectiveness of machine learning models in accurately predicting user workload intensities, essentially by suggesting the most opportune time to co-schedule data analytics work. Yet, machine learning models cannot predict the effects of performance interference when co-scheduling is employed, as this constitutes a \"new\" observation. Specifically, in tiered storage systems, their hierarchical design makes performance interference even more complex, thus accurate performance prediction is more challenging. Here, we quantify the unknown performance effects of workload co-scheduling by enhancing machine learning models with queuing theory ones to develop a hybrid approach that can accurately predict performance and guide scheduling decisions in a tiered storage system. Using traces from commercial systems we illustrate that queuing theory and machine learning models can be used in synergy to surpass their respective weaknesses and deliver robust co-scheduling solutions that achieve high performance.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126775956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4