The Experts below are selected from a list of 15450 Experts worldwide ranked by ideXlab platform
Douglas C. Schmidt - One of the best experts on this subject based on the ideXlab platform.
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model driven auto scaling of green cloud Computing Infrastructure
Future Generation Computer Systems, 2012Co-Authors: Brian Dougherty, Jules White, Douglas C. SchmidtAbstract:Cloud Computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on demand. Auto-scaling is an important cloud Computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling Infrastructure to create greener Computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling Infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach.
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Model-driven auto-scaling of green cloud Computing Infrastructure
Future Generation Computer Systems, 2012Co-Authors: Brian Dougherty, Jules White, Douglas C. SchmidtAbstract:Cloud Computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on demand. Auto-scaling is an important cloud Computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling Infrastructure to create greener Computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling Infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach. © 2011 Elsevier B.V. All rights reserved.
Mu Qiao - One of the best experts on this subject based on the ideXlab platform.
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ICALT - An E-learning Ecosystem Based on Cloud Computing Infrastructure
2009 Ninth IEEE International Conference on Advanced Learning Technologies, 2009Co-Authors: Bo Dong, Haifei Li, Qinghua Zheng, Jie Yang, Mu QiaoAbstract:Recently the research community has believed that an e-learning ecosystem is the next generation e-learning. However, the current models of e-learning ecosystems lack the support of underlying Infrastructures, which can dynamically allocate the required computation and storage resources for e-learning ecosystems. Cloud Computing is a promising Infrastructure which provides computation and storage resources as services. Hence, this paper introduces Cloud Computing into an e-learning ecosystem as its Infrastructure. In this paper, an e-learning ecosystem based on Cloud Computing Infrastructure is presented. Cloud Computing Infrastructure and related mechanisms allow for the stability, equilibrium, efficient resource use, and sustainability of an e-learning ecosystem.
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an e learning ecosystem based on cloud Computing Infrastructure
International Conference on Advanced Learning Technologies, 2009Co-Authors: Bo Dong, Haifei Li, Qinghua Zheng, Jie Yang, Mu QiaoAbstract:Recently the research community has believed that an e-learning ecosystem is the next generation e-learning. However, the current models of e-learning ecosystems lack the support of underlying Infrastructures, which can dynamically allocate the required computation and storage resources for e-learning ecosystems. Cloud Computing is a promising Infrastructure which provides computation and storage resources as services. Hence, this paper introduces Cloud Computing into an e-learning ecosystem as its Infrastructure. In this paper, an e-learning ecosystem based on Cloud Computing Infrastructure is presented. Cloud Computing Infrastructure and related mechanisms allow for the stability, equilibrium, efficient resource use, and sustainability of an e-learning ecosystem.
Brian Dougherty - One of the best experts on this subject based on the ideXlab platform.
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model driven auto scaling of green cloud Computing Infrastructure
Future Generation Computer Systems, 2012Co-Authors: Brian Dougherty, Jules White, Douglas C. SchmidtAbstract:Cloud Computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on demand. Auto-scaling is an important cloud Computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling Infrastructure to create greener Computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling Infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach.
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Model-driven auto-scaling of green cloud Computing Infrastructure
Future Generation Computer Systems, 2012Co-Authors: Brian Dougherty, Jules White, Douglas C. SchmidtAbstract:Cloud Computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on demand. Auto-scaling is an important cloud Computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling Infrastructure to create greener Computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling Infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach. © 2011 Elsevier B.V. All rights reserved.
Srikanth V. Krishnamurthy - One of the best experts on this subject based on the ideXlab platform.
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cwc a distributed Computing Infrastructure using smartphones
IEEE Transactions on Mobile Computing, 2015Co-Authors: Mustafa Y. Arslan, Krishnakumar Sundaresan, Indrajeet Singh, Harsha V. Madhyastha, Shailendra Singh, Srikanth V. KrishnamurthyAbstract:Every night, many smartphones are plugged into a power source for recharging the battery. Given the increasing Computing capabilities of smartphones, these idle phones constitute a sizeable Computing Infrastructure. Therefore, for an enterprise which supplies its employees with smartphones, we argue that a Computing Infrastructure that leverages idle smartphones being charged overnight is an energy-efficient and cost-effective alternative to running certain tasks on traditional servers. While parallel execution models and schedulers exist for servers, smartphones face a unique set of technical challenges due to the heterogeneity in CPU clock speed, variability in network bandwidth, and lower availability than servers. In this paper, we address many of these challenges to develop CWC—a distributed Computing Infrastructure using smartphones. We implement and evaluate a prototype of CWC that employs a novel scheduling algorithm to minimize the makespan of a set of Computing tasks. Our evaluations using a testbed of $18$ Android phones show that CWC’s scheduler yields a makespan that is 1.6x faster than other simpler approaches.
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CoNEXT - Computing while charging: building a distributed Computing Infrastructure using smartphones
Proceedings of the 8th international conference on Emerging networking experiments and technologies - CoNEXT '12, 2012Co-Authors: Mustafa Y. Arslan, Krishnakumar Sundaresan, Indrajeet Singh, Harsha V. Madhyastha, Shailendra Singh, Srikanth V. KrishnamurthyAbstract:Every night, a large number of idle smartphones are plugged into a power source for recharging the battery. Given the increasing Computing capabilities of smartphones, these idle phones constitute a sizeable Computing Infrastructure. Therefore, for an enterprise which supplies its employees with smartphones, we argue that a Computing Infrastructure that leverages idle smartphones being charged overnight is an energy-efficient and cost-effective alternative to running tasks on traditional server Infrastructure. While parallel execution and scheduling models exist for servers (e.g., MapReduce), smartphones present a unique set of technical challenges due to the heterogeneity in CPU clock speed, variability in network bandwidth, and lower availability compared to servers. In this paper, we address many of these challenges to develop CWC---a distributed Computing Infrastructure using smartphones. Specifically, our contributions are: (i) we profile the charging behaviors of real phone owners to show the viability of our approach, (ii) we enable programmers to execute parallelizable tasks on smartphones with little effort, (iii) we develop a simple task migration model to resume interrupted task executions, and (iv) we implement and evaluate a prototype of CWC (with 18 Android smartphones) that employs an underlying novel scheduling algorithm to minimize the makespan of a set of tasks. Our extensive evaluations demonstrate that the performance of our approach makes our vision viable. Further, we explicitly evaluate the performance of CWC's scheduling component to demonstrate its efficacy compared to other possible approaches.
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Computing while charging: Building a Distributed Computing Infrastructure Using Smartphones
8th international conference on Emerging networking experiments and technologies, 2012Co-Authors: Mustafa Y. Arslan, Krishnakumar Sundaresan, Indrajeet Singh, Harsha V. Madhyastha, Shailendra Singh, Srikanth V. KrishnamurthyAbstract:Every night, a large number of idle smartphones are plugged into a power source for recharging the battery. Given the increasing Computing capabilities of smartphones, these idle phones consti- tute a sizeable Computing Infrastructure. Therefore, for an enter- prise which supplies its employees with smartphones, we argue that a Computing Infrastructure that leverages idle smartphones be- ing charged overnight is an energy-efficient and cost-effective alter- native to running tasks on traditional server Infrastructure. W`hile parallel execution and scheduling models exist for servers (e.g., MapReduce), smartphones present a unique set of technical chal- lenges due to the heterogeneity in CPU clock speed, variability in network bandwidth, and lower availability compared to servers. In this paper, we address many of these challenges to develop CWC—a distributed Computing Infrastructure using smartphones. Specifically, our contributions are: (i) we profile the charging be- haviors of real phone owners to show the viability of our approach, (ii)we enable programmers to execute parallelizable tasks on smart- phones with little effort, (iii) we develop a simple task migration model to resume interrupted task executions, and (iv) we imple- ment and evaluate a prototype of CWC (with 18 Android smart- phones) that employs an underlying novel scheduling algorithm to minimize the makespan of a set of tasks. Our extensive eval- uations demonstrate that the performance of our approach makes our vision viable. Further, we explicitly evaluate the performance of CWC’s scheduling component to demonstrate its efficacy com- pared to other possible approaches.
S. Sekiguchi - One of the best experts on this subject based on the ideXlab platform.
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design and implementations of ninf towards a global Computing Infrastructure
Future Generation Computer Systems, 1999Co-Authors: Hidemoto Nakada, M. Sato, S. SekiguchiAbstract:Abstract The world-wide Computing Infrastructure on the growing computer network technology is a leading technology to make a variety of information services accessible through the Internet for every user from the high-performance Computing users through many of personal Computing users. The important feature of such services is location transparency; information can be obtained irrespective of time or location in virtually shared manner. In this article, we overview Ninf, an ongoing global network-wide Computing Infrastructure project which allows users to access computational resources including hardware, software and scientific data distributed across a wide area network. Preliminary performance result on measuring software and network overhead is shown, and that promises the future reality of world-wide network Computing.
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ninf a network based information library for global world wide Computing Infrastructure
IEEE International Conference on High Performance Computing Data and Analytics, 1997Co-Authors: M. Sato, S. Sekiguchi, Hidemoto Nakada, Satoshi Matsuoka, Umpei Nagashima, Hiromitsu TakagiAbstract:Ninf is an ongoing global network-wide Computing Infrastructure project which allows users to access computational resources including hardware, software and scientific data distributed across a wide area network. Ninf is intended not only to exploit high performance in network parallel Computing, but also to provide high quality numerical computation services and accesses to scientific database published by other researchers. Computational resources are shared as Ninf remote libraries executable at a remote Ninf server. Users can build an application by calling the libraries with the Ninf Remote Procedure Call, which is designed to provide a programming interface similar to conventional function calls in existing languages, and is tailored for scientific computation. In order to facilitate location transparency and network-wide parallelism, Ninf metaserver maintains global resource information regarding computational server and databases, allocating and scheduling coarse-grained computation for global load balancing. Ninf also interfaces with the WWW browsers for easy accessibility.
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World-wide Computing Infrastructure: global and local partnership
Proceedings of IEEE International Symposium on Parallel Algorithms Architecture Synthesis, 1997Co-Authors: S. Sekiguchi, M. SatoAbstract:The world wide Computing Infrastructure on the growing computer network technology is a leading technology to make a variety of information services accessible through the Internet for all types of users: from the high end, high performance Computing users through to many personal Computing users. One important feature of such services is location transparency; information can be obtained irrespective of time or location in a virtually shared manner. We introduce Ninj, an ongoing global network wide Computing Infrastructure project which allows users to access computational resources including hardware, software and scientific data distributed across a wide area network. Preliminary performance results on measuring software and network overhead are shown which promise the future reality of world wide network Computing.