An On-demand Monitoring Approach for Cloud Computing Systems
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Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost.
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In the paper we discuss the problem of evaluating disc performance with benchmarks. In particular, we concentrate on assessing benchmark properties. For this purpose we have developed benchmark managing platform which allows us to enhance the benchmark execution process with monitoring performance counters. The developed methodology and tool do not need additional benchmark instrumentation and have a negligible impact on its execution. The usefulness of this approach has been illustrated with experimental results covering a representative set of benchmark programs.
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Education of cloud engineers will be crucial for the continued development of cloud technologies. We have developed an open-source software platform called edubase Cloud for education. The platform has multi-cloud architecture. In this paper, we discuss how edubase Cloud provides an alterable cloud platform and how effective it is for educating cloud engineers.
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Virtual Computing Lab (VCL) environment at Computer communications lab enables students remote access to virtual environments, in which they fulfill their course assignments. It is comprised of 11 physical servers with VMware hardware virtualization that allows provisioning of virtual machines. Most of the time the private cloud in underutilizated, while it gets overutilizated during usage peaks at the end of semester or around deadlines for certain student assignments. In this thesis we suggest a solution for the overutilization problem in form of a hybrid cloud. Current private cloud could still be used as it was untill now, but we would have a possibility to expand available resources with public cloud during usage peaks. To address this issue we have developed a new VCL provisioning module for provisioning Amazon EC2 public cloud resources. Hybrid cloud solution expands capabilites of VCL environment during usage peaks and provides optimal resource utilization. With public cloud's »pay-as-you-go« model we only pay for resources (compute cycles, storage) that we actually use. With hybrid cloud we achieve better cost efficency, than we would have with investments into new physical infrastructure to meet the demand for resources during usage peaks. To really understand how hybrid cloud solution works, we discuss cloud computing, describe open-source VCL environment and EC2 public cloud. In the end we explain how to integrate hybrid cloud with connecting via public cloud's API interface and suggest hybrid cloud's architecture in case of Computer communications lab. The result of this thesis is a working proof-of-concept hybrid cloud solution for VCL environment.
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The process of implementing monetary policy by the Central Bank is that of taking the advantage of the manipulative instruments to reach the goals of policy so the Central Bank should choose the manipulative instruments according to certain benchmark so as to reach its goals better. The benchmark of choosing instruments of monetary policy by the Central Bank include theoretical benchmark and empirical benchmark. The theoretical benchmark consist of external and internal benchmark. Internal benchmark are the most important benchmark for daily operation,which include initiative benchmark,fine-tuning benchmark,signal-functioning benchmark,timeliness benchmark,and operablity benchmark.
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Results of the third benchmark are briefly described. The problem requires solution of a mulirate system. It is found that the benchmark is a difficult problem and many respondees have used it to expand and debug their facilities. A more complete paper on the solutions of this benchmark will be submitted to the Control System Magazine for further dissemination.
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