2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)最新文献_第4页

Cost-Time Performance of Scaling Applications on the Cloud 云上扩展应用程序的成本-时间性能

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00021

Sunimal Rathnayake, Lavanya Ramapantulu, Y. M. Teo

{"title":"Cost-Time Performance of Scaling Applications on the Cloud","authors":"Sunimal Rathnayake, Lavanya Ramapantulu, Y. M. Teo","doi":"10.1109/CloudCom2018.2018.00021","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00021","url":null,"abstract":"Recent advancements in big data processing and machine learning, among others, increase the resource demand for running applications with larger problem sizes. Elastic cloud computing resources with pay-per-use pricing offers new opportunities where large application execution is constrained only by the cost budget. Given a cost budget and a time deadline, this paper introduces a measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution. We evaluate our approach with a set of representative applications that exhibit a range of resource demand growth patterns on Amazon AWS cloud. We show the existence of cost-time-size Pareto-frontier with multiple sweet spots meeting user constraints. To characterize the cost-performance of cloud resources, we use Performance Cost Ratio (PCR) metric. We extend Gustafson's fixed-time scaling in the context of cloud, and, investigate fixed-cost-time scaling of applications and show that using resources with higher PCR yields better cost-time performance. We discuss a number of useful insights on the trade-off between the execution time and the largest Pareto-optimal problem size, and, show that time deadline could be tightened for a proportionately much smaller reduction of problem size.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969860","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: 3

Trustworthy Cloud Service Level Agreement Enforcement with Blockchain Based Smart Contract 基于区块链的智能合约的可信云服务水平协议执行

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00057

Huan Zhou, C. D. Laat, Zhiming Zhao

{"title":"Trustworthy Cloud Service Level Agreement Enforcement with Blockchain Based Smart Contract","authors":"Huan Zhou, C. D. Laat, Zhiming Zhao","doi":"10.1109/CloudCom2018.2018.00057","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00057","url":null,"abstract":"Cloud Service Level Agreement (SLA) is challengeable due to lacking a trustworthy platform. This paper presents a witness model to credibly enforce the cloud service level agreement. Through introducing the witness role and using the blockchain based smart contract, we solve the trust issues about who can detect the service violation, how the violation is confirmed and the compensation is guaranteed. In this model, a verifiable consensus sortition algorithm proposed by us is firstly leveraged to select independent witnesses to form a witness committee. They are responsible for a specific service level agreement and get paid by monitoring and detecting service violation. Through carefully designing the witness' payoff function in the agreement, we further leverage game theory to analyze and prove that it is not the witness itself is trustworthy. Instead, the witness has to tell the truth because of its greedy nature, which is the desire to maximize its own revenue. As long as the service violation is confirmed by the witness committee, the compensation is automatically transferred to the customer by the smart contract. Finally, we implement a proof-of-concept prototype with the smart contract of Ethereum blockchain. It demonstrates the feasibility of our model.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114627675","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: 25

Phase Annotated Learning for Apache Spark: Workload Recognition and Characterization Apache Spark的阶段标注学习:工作负载识别和表征

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00018

Seyed Ali Jokar Jandaghi, Arnamoy Bhattacharyya, C. Amza

引用次数: 2

EdgeStore: Leveraging Edge Devices for Mobile Storage Offloading EdgeStore:利用边缘设备进行移动存储卸载

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00025

Ali E. Elgazar, Mohammad Aazam, Khaled A. Harras

{"title":"EdgeStore: Leveraging Edge Devices for Mobile Storage Offloading","authors":"Ali E. Elgazar, Mohammad Aazam, Khaled A. Harras","doi":"10.1109/CloudCom2018.2018.00025","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00025","url":null,"abstract":"The recent growth in smart phone adoption coupled with social networking has lead to an increase in user generated content (UGC). The majority of UGC, captured in the form of images, videos, and audio clips, have significantly grown in size as a result of advancements in multimedia technology with higher definition phone cameras. Online clouds are generally the go-to solution in order to accommodate this increase in storage requirements. However, online clouds raise privacy concerns, are not fully automated, and do not adapt to different networking environments. We propose an edge based automated storage management system, EdgeStore, that utilizes user-owned devices at the edge, to automatically offload and retrieve files. EdgeStore determines file popularity based on the user's access patterns and offloads unpopular files. It accounts for different networking infrastructures, ranging from rural areas to metropolitan areas, in order to serve users in different environments. We implement and evaluate EdgeStore showing that users can offload up to 90% of storage to underutilized edge devices, and still have a high percentage of low latency access to offloaded files within 10 seconds.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128350633","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

Game-Theoretic Incentive Model for Improving Mobile Code Offloading Adaptability 提高移动代码卸载适应性的博弈论激励模型

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00040

T. Mir, S. Srirama

引用次数: 1

A Pareto-Efficient Algorithm for Data Stream Processing at Network Edges 网络边缘数据流处理的Pareto-Efficient算法

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00041

Thanasis Loukopoulos, Nikos Tziritas, M. Koziri, G. Stamoulis, S. Khan

{"title":"A Pareto-Efficient Algorithm for Data Stream Processing at Network Edges","authors":"Thanasis Loukopoulos, Nikos Tziritas, M. Koziri, G. Stamoulis, S. Khan","doi":"10.1109/CloudCom2018.2018.00041","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00041","url":null,"abstract":"Data stream processing has received considerable attention from both research community and industry over the last years. Since latency is a key issue in data stream processing environments, the majority of the works existing in the literature focus on minimizing the latency experienced by the users. The aforementioned minimization takes place by assigning the data stream processing components close to data sources. Server consolidation is also a key issue for drastically reducing energy consumption in computing systems. Unfortunately, energy consumption and latency are two objective functions that may be in conflict with each other. Therefore, when the target function is to minimize energy consumption, the delay experienced by users may be considerable high, and the opposite. For the above reason there is a dire need to design strategies such that by targeting the minimization of energy consumption, there is a graceful degradation in latency, as well as the opposite. To achieve the above, we propose a Pareto-efficient algorithm that tackles the problem of data processing tasks placement simultaneously in both dimensions regarding the energy consumption and latency. The proposed algorithm outputs a set of solutions that are not dominated by any solution within the set regarding energy consumption and latency. The experimental results show that the proposed approach is superior against single-solution approaches because by targeting one objective function the other one can be gracefully degraded by choosing the appropriate solution.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126072530","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: 5

DERP: A Deep Reinforcement Learning Cloud System for Elastic Resource Provisioning DERP:一种用于弹性资源供应的深度强化学习云系统

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00020

C. Bitsakos, I. Konstantinou, N. Koziris

{"title":"DERP: A Deep Reinforcement Learning Cloud System for Elastic Resource Provisioning","authors":"C. Bitsakos, I. Konstantinou, N. Koziris","doi":"10.1109/CloudCom2018.2018.00020","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00020","url":null,"abstract":"Modern large scale computer clusters benefit significantly from elasticity. Elasticity allows a cluster to dynamically allocate computer resources, based on the user's fluctuating workload demands. Many cloud providers use threshold-based approaches, which have been proven to be difficult to configure and optimise, while others use reinforcement learning and decision-tree approaches, which struggle when having to handle large multidimensional cluster states. In this work we use Deep Reinforcement learning techniques to achieve automatic elasticity. We use three different approaches of a Deep Reinforcement learning agent, called DERP (Deep Elastic Resource Provisioning), that takes as input the current multi-dimensional state of a cluster and manages to train and converge to the optimal elasticity behaviour after a finite amount of training steps. The system automatically decides and proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster according to user-defined policies/rewards. We compare our agent to state-of-the-art, Reinforcement learning and decision-tree based, approaches in demanding simulation environments and show that it gains rewards up to 1.6 times better on its lifetime. We then test our approach in a real life cluster environment and show that the system resizes clusters in real-time and adapts its performance through a variety of demanding optimisation strategies, input and training loads.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134397972","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: 39

The Vision of a HeterogeneRous Scheduler 异宽容性调度器的远景

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00065

Ioannis Mytilinis, C. Bitsakos, Katerina Doka, I. Konstantinou, N. Koziris

{"title":"The Vision of a HeterogeneRous Scheduler","authors":"Ioannis Mytilinis, C. Bitsakos, Katerina Doka, I. Konstantinou, N. Koziris","doi":"10.1109/CloudCom2018.2018.00065","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00065","url":null,"abstract":"Modern Big Data processing systems, scheduling platforms and cloud infrastructures employ specialized hardware accelerators such as GPUs, FPGAs, TPUs, ASICs, etc. to optimize the execution of resource intensive workloads such as Machine Learning, Artificial Intelligence or generic Data Analytics tasks. Nevertheless, this support is mostly a user-dependent, manual process that requires careful and educated decisions on both the amount and type of required resources to exploit the underlying hardware and achieve any user-defined higher level policies. In this work we present the initial design of the HeterogeneRous Scheduler (HRS), an intelligent scheduler that can make automated decisions on both how and where to map arbitrary data analytics tasks to underlying cloud hardware that may consist of a mix of hardware accelerators and clusters with general purpose CPUs. We experimentally evaluate the performance trade-offs between hardware accelerators and CPUs where we show that there are cases where one technology outperforms the other. We finally present an initial architecture of HRS where we depict its different components and their interactions with the Big Data framework and the cloud infrastructure.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115292282","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: 1

Towards Distributed SLA Management with Smart Contracts and Blockchain 用智能合约和区块链实现分布式SLA管理

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00059

Rafael Brundo Uriarte, R. Nicola, K. Kritikos

引用次数: 25

An Application Framework for Migrating GPGPU Cloud Applications GPGPU云应用迁移的应用框架

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00026

Shoichiro Yuhara, Yusuke Suzuki, K. Kono

引用次数: 1