Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.81
M. Sedaghat, F. Hernández-Rodriguez, E. Elmroth, Sarunas Girdzijauskas
Efficient resource utilization is one of the main concerns of cloud providers, as it has a direct impact on energy costs and thus their revenue. Virtual machine (VM) consolidation is one the common techniques, used by infrastructure providers to efficiently utilize their resources. However, when it comes to large-scale infrastructures, consolidation decisions become computationally complex, since VMs are multi-dimensional entities with changing demand and unknown lifetime, and users often overestimate their actual demand. These uncertainties urges the system to take consolidation decisions continuously in a real time manner. In this work, we investigate a decentralized approach for VM consolidation using Peer to Peer (P2P) principles. We investigate the opportunities offered by P2P systems, as scalable and robust management structures, to address VM consolidation concerns. We present a P2P consolidation protocol, considering the dimensionality of resources and dynamicity of the environment. The protocol benefits from concurrency and decentralization of control and it uses a dimension aware decision function for efficient consolidation. We evaluate the protocol through simulation of 100,000 physical machines and 200,000 VM requests. Results demonstrate the potentials and advantages of using a P2P structure to make resource management decisions in large scale data centers. They show that the P2P approach is feasible and scalable and produces resource utilization of 75% when the consolidation aim is 90%.
{"title":"Divide the Task, Multiply the Outcome: Cooperative VM Consolidation","authors":"M. Sedaghat, F. Hernández-Rodriguez, E. Elmroth, Sarunas Girdzijauskas","doi":"10.1109/CloudCom.2014.81","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.81","url":null,"abstract":"Efficient resource utilization is one of the main concerns of cloud providers, as it has a direct impact on energy costs and thus their revenue. Virtual machine (VM) consolidation is one the common techniques, used by infrastructure providers to efficiently utilize their resources. However, when it comes to large-scale infrastructures, consolidation decisions become computationally complex, since VMs are multi-dimensional entities with changing demand and unknown lifetime, and users often overestimate their actual demand. These uncertainties urges the system to take consolidation decisions continuously in a real time manner. In this work, we investigate a decentralized approach for VM consolidation using Peer to Peer (P2P) principles. We investigate the opportunities offered by P2P systems, as scalable and robust management structures, to address VM consolidation concerns. We present a P2P consolidation protocol, considering the dimensionality of resources and dynamicity of the environment. The protocol benefits from concurrency and decentralization of control and it uses a dimension aware decision function for efficient consolidation. We evaluate the protocol through simulation of 100,000 physical machines and 200,000 VM requests. Results demonstrate the potentials and advantages of using a P2P structure to make resource management decisions in large scale data centers. They show that the P2P approach is feasible and scalable and produces resource utilization of 75% when the consolidation aim is 90%.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668506","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.73
Tao Zhao, Tao Li, Biao Han, Zhigang Sun, Jinfeng Huang
Software Defined Networking (SDN) provides efficient network and traffic management for data center network. As underlying devices in SDN, SDN switches must maintain a large number of hardware counters. Implementation of these counters faces serious challenges for SDN switches, i.e., High memory consumption and inflexibility. Thus, we previously proposed Software Defined Hardware Counters (SDHC), which decouples definition and implementation of counters to overcome these challenges. However, like traditional hardware counters, SDHC only supports passive statistical mode (i.e., The values of the counters can be only read passively by the controller). Based on the passive mode, most of applications need to send request messages at some frequency to obtain statistics, which causes some critical problems for SDN: i) low statistical accuracy, ii) high network bandwidth consumption. Hyper Software Defined Hardware Counters (Hyper SDHC) is thus proposed by extending SDHC. Through introducing the timer-triggering and updating-triggering statistics-reporting mechanisms, Hyper SDHC can naturally support active statistical mode, i.e., Counters actively report their values according to triggering condition. It can greatly enhance the statistical accuracy and reduce network bandwidth consumption between controller and switch. The demo of Hyper SDHC is implemented based on Net Magic platform. The demo will exhibit how Hyper SDHC works and how it supports a typical video quality monitor application.
{"title":"The Demonstration of Hyper Software Defined Hardware Counters","authors":"Tao Zhao, Tao Li, Biao Han, Zhigang Sun, Jinfeng Huang","doi":"10.1109/CloudCom.2014.73","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.73","url":null,"abstract":"Software Defined Networking (SDN) provides efficient network and traffic management for data center network. As underlying devices in SDN, SDN switches must maintain a large number of hardware counters. Implementation of these counters faces serious challenges for SDN switches, i.e., High memory consumption and inflexibility. Thus, we previously proposed Software Defined Hardware Counters (SDHC), which decouples definition and implementation of counters to overcome these challenges. However, like traditional hardware counters, SDHC only supports passive statistical mode (i.e., The values of the counters can be only read passively by the controller). Based on the passive mode, most of applications need to send request messages at some frequency to obtain statistics, which causes some critical problems for SDN: i) low statistical accuracy, ii) high network bandwidth consumption. Hyper Software Defined Hardware Counters (Hyper SDHC) is thus proposed by extending SDHC. Through introducing the timer-triggering and updating-triggering statistics-reporting mechanisms, Hyper SDHC can naturally support active statistical mode, i.e., Counters actively report their values according to triggering condition. It can greatly enhance the statistical accuracy and reduce network bandwidth consumption between controller and switch. The demo of Hyper SDHC is implemented based on Net Magic platform. The demo will exhibit how Hyper SDHC works and how it supports a typical video quality monitor application.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122413383","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.54
Seungwoo Jeon, B. Hong, Byungsoo Kim
To predict future traffic conditions in each road with unique spatiotemporal pattern, it is necessary to analyze the conditions based on historical traffic data and select time series forecasting methods which can be predicting next pattern for each road according to the analyzed results. Our goal is to create a new statistical model and a new system for predictive graphs of traffic times based on big data processing tools. First, we suggest a vertical data arrangement, gathering past traffic times in the same time slot for long-term prediction. Second, we analyze each traffic pattern to select time-series variables because a time-series forecasting method for a location and a time will be selected according to the variables that are available. Third, we suggest a spatiotemporal prediction map, which is a two-dimensional map with time and location. Each element in the map represents a time-series forecasting method and an R-squared value as indicator of prediction accuracy. Finally, we introduce a new system including RHive as a middle point between R and Hadoop clusters for generating predicted data efficiently from big historical data.
{"title":"Big Data Processing for Prediction of Traffic Time Based on Vertical Data Arrangement","authors":"Seungwoo Jeon, B. Hong, Byungsoo Kim","doi":"10.1109/CloudCom.2014.54","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.54","url":null,"abstract":"To predict future traffic conditions in each road with unique spatiotemporal pattern, it is necessary to analyze the conditions based on historical traffic data and select time series forecasting methods which can be predicting next pattern for each road according to the analyzed results. Our goal is to create a new statistical model and a new system for predictive graphs of traffic times based on big data processing tools. First, we suggest a vertical data arrangement, gathering past traffic times in the same time slot for long-term prediction. Second, we analyze each traffic pattern to select time-series variables because a time-series forecasting method for a location and a time will be selected according to the variables that are available. Third, we suggest a spatiotemporal prediction map, which is a two-dimensional map with time and location. Each element in the map represents a time-series forecasting method and an R-squared value as indicator of prediction accuracy. Finally, we introduce a new system including RHive as a middle point between R and Hadoop clusters for generating predicted data efficiently from big historical data.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418587","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.146
Simeon Veloudis, A. Friesen, I. Paraskakis, Giannis Verginadis, Ioannis Patiniotakis
With the pervasion of cloud computing, enterprises increasingly rely on ecosystems of distributed, task-oriented, modular, and collaborative cloud services. In order to effectively manage the complexity inherent in such ecosystems, enterprises are anticipated to depend upon brokerage mechanisms for performing policy-based governance and for recommending optimal services to consumers. Such mechanisms crucially depend upon the existence of a uniform, platform-independent representation of services, consumer preferences, and policies concerning service delivery. In this paper we propose an ontology-based approach to such a representation.
{"title":"Underpinning a Cloud Brokerage Service Framework for Quality Assurance and Optimization","authors":"Simeon Veloudis, A. Friesen, I. Paraskakis, Giannis Verginadis, Ioannis Patiniotakis","doi":"10.1109/CloudCom.2014.146","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.146","url":null,"abstract":"With the pervasion of cloud computing, enterprises increasingly rely on ecosystems of distributed, task-oriented, modular, and collaborative cloud services. In order to effectively manage the complexity inherent in such ecosystems, enterprises are anticipated to depend upon brokerage mechanisms for performing policy-based governance and for recommending optimal services to consumers. Such mechanisms crucially depend upon the existence of a uniform, platform-independent representation of services, consumer preferences, and policies concerning service delivery. In this paper we propose an ontology-based approach to such a representation.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487938","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.142
A. Saleh, M. Fouad, Mervat Abu-Elkheir
Software as a Service (SaaS) providers can serve thousands of customers, which have hundreds of thousands of overlapping requirements, using a single application instance to offer service at a lower price. Even with a potentially large number of customers with varying requirements, a multitenant application should make co-tenancy transparent to the tenants, which means that every tenant must appear to be the sole owner of the application, to achieve this, a highly configurable multitenant solution is needed. In this paper, we analyze variation in multiple tenants' requirements, to propose a classification for multitenant application requirements, and implement variability realization techniques depending on requirement levels. Furthermore, we prioritize the tenants' requirements to satisfy as many customer requirements as possible, and provide key guidelines to software architects and developers to implement a configuration layer in a multi-tenancy architecture.
{"title":"Classifying Requirements for Variability Optimization in Multitenant Applications","authors":"A. Saleh, M. Fouad, Mervat Abu-Elkheir","doi":"10.1109/CloudCom.2014.142","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.142","url":null,"abstract":"Software as a Service (SaaS) providers can serve thousands of customers, which have hundreds of thousands of overlapping requirements, using a single application instance to offer service at a lower price. Even with a potentially large number of customers with varying requirements, a multitenant application should make co-tenancy transparent to the tenants, which means that every tenant must appear to be the sole owner of the application, to achieve this, a highly configurable multitenant solution is needed. In this paper, we analyze variation in multiple tenants' requirements, to propose a classification for multitenant application requirements, and implement variability realization techniques depending on requirement levels. Furthermore, we prioritize the tenants' requirements to satisfy as many customer requirements as possible, and provide key guidelines to software architects and developers to implement a configuration layer in a multi-tenancy architecture.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121342242","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}
With the wide adoption of cloud computing, the scientific applications are migrated to cloud for execution. The complex structure of scientific applications bring challenges to optimization scheduling scientific applications across multiple heterogeneous clouds. In this paper, the directed acyclic graphs (DAGs) are adopted to represent scientific applications, which have different priorities in the process of scheduling. We propose a dynamic multi-cloud priority list scheduling algorithm (DMPLS), combining with workloads preemptive strategy and feedback mechanism to schedule scientific applications in time. Our algorithm regulates the workloads scheduling dynamically based on the updated information about the actual workloads execution time. The experimental results show that the proposed algorithm reduces the average time to complete the applications compared with First-Come-First-Service and Round-Robin algorithm. Moreover, the advantage of the DMPLS algorithm is more significant under the severe resources confliction situations.
{"title":"Optimization Scheduling for Scientific Applications with Different Priorities across Multiple Clouds","authors":"Bing Lin, Wenzhong Guo, Xianghan Zheng, Hong Zhang, Chunming Rong, Guolong Chen","doi":"10.1109/CloudCom.2014.15","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.15","url":null,"abstract":"With the wide adoption of cloud computing, the scientific applications are migrated to cloud for execution. The complex structure of scientific applications bring challenges to optimization scheduling scientific applications across multiple heterogeneous clouds. In this paper, the directed acyclic graphs (DAGs) are adopted to represent scientific applications, which have different priorities in the process of scheduling. We propose a dynamic multi-cloud priority list scheduling algorithm (DMPLS), combining with workloads preemptive strategy and feedback mechanism to schedule scientific applications in time. Our algorithm regulates the workloads scheduling dynamically based on the updated information about the actual workloads execution time. The experimental results show that the proposed algorithm reduces the average time to complete the applications compared with First-Come-First-Service and Round-Robin algorithm. Moreover, the advantage of the DMPLS algorithm is more significant under the severe resources confliction situations.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121206778","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.87
Rongqi Zhang, Yanlei Shang, Siyue Zhang
The technology of Cloud Computing has shown its great power in consolidating and integrating compute resources for higher utilizing efficiency. With the cloud computing, the organizations or companies can own their hardware and software infrastructures easily. Because of the diversity of the operating systems and applications, it is very difficult or even impossible for administrator to deploy a large number of virtual machines within a short time manually. In this paper, we propose an automatic deployment mechanism on Open Stack, a popular Cloud Computing platform. This proposed system supports the automatic deployment service at both operating system level and application level. We also develop a dashboard to facilitate users operations. Without professional knowledge of cloud, Users also can deploy their systems and the applications expediently.
{"title":"An Automatic Deployment Mechanism on Cloud Computing Platform","authors":"Rongqi Zhang, Yanlei Shang, Siyue Zhang","doi":"10.1109/CloudCom.2014.87","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.87","url":null,"abstract":"The technology of Cloud Computing has shown its great power in consolidating and integrating compute resources for higher utilizing efficiency. With the cloud computing, the organizations or companies can own their hardware and software infrastructures easily. Because of the diversity of the operating systems and applications, it is very difficult or even impossible for administrator to deploy a large number of virtual machines within a short time manually. In this paper, we propose an automatic deployment mechanism on Open Stack, a popular Cloud Computing platform. This proposed system supports the automatic deployment service at both operating system level and application level. We also develop a dashboard to facilitate users operations. Without professional knowledge of cloud, Users also can deploy their systems and the applications expediently.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122376173","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.19
Wei Tang, John Jenkins, Folker Meyer, R. Ross, R. Kettimuthu, L. Winkler, Xi Yang, T. Lehman, N. Desai
Cloud infrastructures have seen increasing popularity for addressing the growing computational needs of today's scientific and engineering applications. However, resource management challenges exist in the elastic cloud environment, such as resource provisioning and task allocation, especially when data movement between multiple domains plays an important role. In this work, we study the impact of data-aware resource management and scheduling on scientific workflows in multicloud environments. We develop a workflow simulator based on a network simulation framework for fine-grained simulation for workflow computation and data movement. Using the workload traces from a production metagenomic data analysis service, we evaluate different resource scheduling mechanisms, including proposed data-aware scheduling policies under various resource and bandwidth configurations. The results of this work are expected to answer questions about how to provision computing resources for certain workloads efficiently and how to place tasks across multidomain clouds in order to reduce data movement costs for overall improved system performance.
{"title":"Data-Aware Resource Scheduling for Multicloud Workflows: A Fine-Grained Simulation Approach","authors":"Wei Tang, John Jenkins, Folker Meyer, R. Ross, R. Kettimuthu, L. Winkler, Xi Yang, T. Lehman, N. Desai","doi":"10.1109/CloudCom.2014.19","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.19","url":null,"abstract":"Cloud infrastructures have seen increasing popularity for addressing the growing computational needs of today's scientific and engineering applications. However, resource management challenges exist in the elastic cloud environment, such as resource provisioning and task allocation, especially when data movement between multiple domains plays an important role. In this work, we study the impact of data-aware resource management and scheduling on scientific workflows in multicloud environments. We develop a workflow simulator based on a network simulation framework for fine-grained simulation for workflow computation and data movement. Using the workload traces from a production metagenomic data analysis service, we evaluate different resource scheduling mechanisms, including proposed data-aware scheduling policies under various resource and bandwidth configurations. The results of this work are expected to answer questions about how to provision computing resources for certain workloads efficiently and how to place tasks across multidomain clouds in order to reduce data movement costs for overall improved system performance.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116299570","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.150
Fotis Gonidis, I. Paraskakis, A. Simons
Cloud application platforms gain popularity and have the potential to alter the way service based cloud applications are developed involving utilisation of platform basic services. A platform basic service is considered as a piece of software, which provides certain functionality and is usually offered via a web API, e.g. e-mail, payment, authentication service. However, the proliferation and diversification of platform basic services and the available providers increase the challenge for the application developers to integrate them and deal with the heterogeneous providers' web APIs. Therefore, a new approach of developing applications should be adopted in which developers leverage multiple platform basic services independently from the target application platforms. To this end, this paper presents a development framework whose objective is to enable the consistent integration of the platform services, and to allow the seamless use of the concrete providers by alleviating the heterogeneities among them.
{"title":"Leveraging Platform Basic Services in Cloud Application Platforms for the Development of Cloud Applications","authors":"Fotis Gonidis, I. Paraskakis, A. Simons","doi":"10.1109/CloudCom.2014.150","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.150","url":null,"abstract":"Cloud application platforms gain popularity and have the potential to alter the way service based cloud applications are developed involving utilisation of platform basic services. A platform basic service is considered as a piece of software, which provides certain functionality and is usually offered via a web API, e.g. e-mail, payment, authentication service. However, the proliferation and diversification of platform basic services and the available providers increase the challenge for the application developers to integrate them and deal with the heterogeneous providers' web APIs. Therefore, a new approach of developing applications should be adopted in which developers leverage multiple platform basic services independently from the target application platforms. To this end, this paper presents a development framework whose objective is to enable the consistent integration of the platform services, and to allow the seamless use of the concrete providers by alleviating the heterogeneities among them.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125994210","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.106
Chunsheng Zhu, Xiuhua Li, Victor C. M. Leung, Xiping Hu, L. Yang
The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.
{"title":"Job Scheduling for Cloud Computing Integrated with Wireless Sensor Network","authors":"Chunsheng Zhu, Xiuhua Li, Victor C. M. Leung, Xiping Hu, L. Yang","doi":"10.1109/CloudCom.2014.106","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.106","url":null,"abstract":"The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875413","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}
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