The Experts below are selected from a list of 63 Experts worldwide ranked by ideXlab platform
Bingliang Chen - One of the best experts on this subject based on the ideXlab platform.
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a computation Workload Characteristic study of c ran
International Conference on Distributed Computing Systems, 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
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ICDCS - A Computation Workload Characteristic Study of C-RAN
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
Tao Xie - One of the best experts on this subject based on the ideXlab platform.
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A file assignment strategy independent of Workload Characteristic assumptions
ACM Transactions on Storage, 2009Co-Authors: Tao Xie, Yao SunAbstract:The problem of statically assigning nonpartitioned files in a parallel I/O system has been extensively investigated. A basic Workload Characteristic assumption of most existing solutions to the problem is that there exists a strong inverse correlation between file access frequency and file size. In other words, the most popular files are typically small in size, while the large files are relatively unpopular. Recent studies on the Characteristics of Web proxy traces suggested, however, the correlation, if any, is so weak that it can be ignored. Hence, the following two questions arise naturally. First, can existing algorithms still perform well when the Workload assumption does not holdq Second, if not, can one develop a new file assignment strategy that is immune to the Workload assumptionq To answer these questions, we first evaluate the performance of three well-known file assignment algorithms with and without the Workload assumption, respectively. Next, we develop a novel static nonpartitioned file assignment strategy for parallel I/O systems, called static round-robin (SOR), which is immune to the Workload assumption. Comprehensive experimental results show that SOR consistently improves the performance in terms of mean response time over the existing schemes.
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sor a static file assignment strategy immune to Workload Characteristic assumptions in parallel i o systems
International Conference on Parallel Processing, 2007Co-Authors: Tao XieAbstract:The problem of statically assigning nonpartitioned files in a parallel I/O system has been extensively investigated. A basic Workload Characteristic assumption of existing solutions to the problem is that there exists a strong inverse correlation between file access frequency and file size. In other words, the most popular files are typically small in size, while the large files are relatively unpopular. Recent studies on the Characteristics of web proxy traces suggested, however, the correlation, if any, is so weak that it can be ignored. Hence, the following two questions arise naturally. First, can existing algorithms still perform well when the Workload assumption does not hold? Second, if not, can one develop a new file assignment strategy that is immune to the Workload assumption? To answer these questions, in this paper we first evaluate the performance of three well-known file assignment algorithms with and without the Workload assumption, respectively. Next, we develop a novel static file assignment strategy for parallel I/O systems, called static round-robin (SOR), which is immune to the Workload assumption. Comprehensive experimental results show that SOR consistently and noticeably improves the performance in terms of mean response time over the existing schemes.
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ICPP - SOR: A Static File Assignment Strategy Immune to Workload Characteristic Assumptions in Parallel I/O Systems
2007 International Conference on Parallel Processing (ICPP 2007), 2007Co-Authors: Tao XieAbstract:The problem of statically assigning nonpartitioned files in a parallel I/O system has been extensively investigated. A basic Workload Characteristic assumption of existing solutions to the problem is that there exists a strong inverse correlation between file access frequency and file size. In other words, the most popular files are typically small in size, while the large files are relatively unpopular. Recent studies on the Characteristics of web proxy traces suggested, however, the correlation, if any, is so weak that it can be ignored. Hence, the following two questions arise naturally. First, can existing algorithms still perform well when the Workload assumption does not hold? Second, if not, can one develop a new file assignment strategy that is immune to the Workload assumption? To answer these questions, in this paper we first evaluate the performance of three well-known file assignment algorithms with and without the Workload assumption, respectively. Next, we develop a novel static file assignment strategy for parallel I/O systems, called static round-robin (SOR), which is immune to the Workload assumption. Comprehensive experimental results show that SOR consistently and noticeably improves the performance in terms of mean response time over the existing schemes.
Yucing Luo - One of the best experts on this subject based on the ideXlab platform.
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a computation Workload Characteristic study of c ran
International Conference on Distributed Computing Systems, 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
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ICDCS - A Computation Workload Characteristic Study of C-RAN
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
Jerry Chou - One of the best experts on this subject based on the ideXlab platform.
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a computation Workload Characteristic study of c ran
International Conference on Distributed Computing Systems, 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
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ICDCS - A Computation Workload Characteristic Study of C-RAN
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
Shihchun Huang - One of the best experts on this subject based on the ideXlab platform.
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a computation Workload Characteristic study of c ran
International Conference on Distributed Computing Systems, 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
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ICDCS - A Computation Workload Characteristic Study of C-RAN
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018Co-Authors: Yucing Luo, Shihchun Huang, Jerry Chou, Bingliang ChenAbstract:Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted Workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.
