Monitoring the internal state of an application executed within a job is an issue for the integra... more Monitoring the internal state of an application executed within a job is an issue for the integration of legacy applications into grid infrastructures. Commonly used grid middleware stacks are capable to monitor the state of the running job as a closed entity, however they do not provide any information about the internal state of the application program executed by the job. Examples for such desirable information are intermediary iteration results in numerical simulation or the current step reached by an image rendering program. Due to the lack of a convenient monitoring solution most of the grid users thus instrument the application code by writing logging information and intermediate results into separate files and then inspect them from a grid client. This method of adding code to a grid job is not only error-prone due to the risk of adding faulty code; but it is also difficult to reuse it in other jobs. We propose a new grid service called Application Job Monitor, which is buil...
The variety of components and the complexity of technical solutions in factory automation push in... more The variety of components and the complexity of technical solutions in factory automation push information management based on relational databases to it’s limits in terms of maintenance complexity and usage flexibility. Semantic Technologies account for maintainable, comprehensible and rich schema descriptions as well as state-of-the-art reasoning and SPARQL engines claim to deliver compelling performance. In the following we briefly report on applying ontologies and reasoning for managing complex product data in the automation domain.
International Journal of Applied Mathematics and Computer Science, 2014
Scheduling virtual machines is a major research topic for cloud computing, because it directly in... more Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation o...
ABSTRACT Computing Clouds offer a new way of using IT facilities including the hardware, storage,... more ABSTRACT Computing Clouds offer a new way of using IT facilities including the hardware, storage, applications and networks. The huge resource pool on the Cloud forms an appropriate platform for running applications with both computing and data intensity, like the DNA sequencing workflows. This paper studies the topic of running scientific workflows on multiple Clouds, with the DNA sequencing workflow as a driven application. We focus on the problem of matching the workflow functional and non-functional Service Level Agreement (SLA) requirements to the compute and storage services provisioned by underlying Clouds with different service price and quality. We designed an ontological model for a semantic description of the problem and developed a novel utility-based genetic matching algorithm for selecting the Cloud services with respect to the user requirements and the properties of the Clouds. We validated the approach by comparing the performance of the proposed algorithm with other matching algorithms in executing the DNA sequencing application on a realistic simulation platform. The results show the effectiveness of our approach in reducing the total costs and fulfilling the requested service quality even with large-scale service compositions.
Monitoring the internal state of an application executed within a job is an issue for the integra... more Monitoring the internal state of an application executed within a job is an issue for the integration of legacy applications into grid infrastructures. Commonly used grid middleware stacks are capable to monitor the state of the running job as a closed entity, however they do not provide any information about the internal state of the application program executed by the job. Examples for such desirable information are intermediary iteration results in numerical simulation or the current step reached by an image rendering program. Due to the lack of a convenient monitoring solution most of the grid users thus instrument the application code by writing logging information and intermediate results into separate files and then inspect them from a grid client. This method of adding code to a grid job is not only error-prone due to the risk of adding faulty code; but it is also difficult to reuse it in other jobs. We propose a new grid service called Application Job Monitor, which is buil...
The variety of components and the complexity of technical solutions in factory automation push in... more The variety of components and the complexity of technical solutions in factory automation push information management based on relational databases to it’s limits in terms of maintenance complexity and usage flexibility. Semantic Technologies account for maintainable, comprehensible and rich schema descriptions as well as state-of-the-art reasoning and SPARQL engines claim to deliver compelling performance. In the following we briefly report on applying ontologies and reasoning for managing complex product data in the automation domain.
International Journal of Applied Mathematics and Computer Science, 2014
Scheduling virtual machines is a major research topic for cloud computing, because it directly in... more Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation o...
ABSTRACT Computing Clouds offer a new way of using IT facilities including the hardware, storage,... more ABSTRACT Computing Clouds offer a new way of using IT facilities including the hardware, storage, applications and networks. The huge resource pool on the Cloud forms an appropriate platform for running applications with both computing and data intensity, like the DNA sequencing workflows. This paper studies the topic of running scientific workflows on multiple Clouds, with the DNA sequencing workflow as a driven application. We focus on the problem of matching the workflow functional and non-functional Service Level Agreement (SLA) requirements to the compute and storage services provisioned by underlying Clouds with different service price and quality. We designed an ontological model for a semantic description of the problem and developed a novel utility-based genetic matching algorithm for selecting the Cloud services with respect to the user requirements and the properties of the Clouds. We validated the approach by comparing the performance of the proposed algorithm with other matching algorithms in executing the DNA sequencing application on a realistic simulation platform. The results show the effectiveness of our approach in reducing the total costs and fulfilling the requested service quality even with large-scale service compositions.
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