An Efficient Cloud Scheduling Algorithm for the Conservation of Energy through Broadcasting

Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use

On Improving The Performance And Resource Utilization of Consolidated Virtual Machines: Measurement, Modeling, Analysis, and Prediction

This dissertation addresses the performance related issues of consolidated \emph{Virtual Machines} (VMs). \emph{Virtualization} is an important technology for the \emph{Cloud} and data centers. Essential features of a data center like the fault tolerance, high-availability, and \emph{pay-as-you-go} model of services are implemented with the help of VMs. Cloud had become one of the significant innovations over the past decade. Research has been going on the deployment of newer and diverse set of applications like the \emph{High-Performance Computing} (HPC), and parallel applications on the Cloud. The primary method to increase the server resource utilization is VM consolidation, running as many VMs as possible on a server is the key to improving the resource utilization. On the other hand, consolidating too many VMs on a server can degrade the performance of all VMs. Therefore, it is necessary to measure, analyze and find ways to predict the performance variation of consolidated VMs. This dissertation investigates the causes of performance variation of consolidated VMs; the relationship between the resource contention and consolidation performance, and ways to predict the performance variation. Experiments have been conducted with real virtualized servers without using any simulation. All the results presented here are real system data. In this dissertation, a methodology is introduced to do the experiments with a large number of tasks and VMs; it is called the \emph{Incremental Consolidation Benchmarking Method} (ICBM). The experiments have been done with different types of resource-intensive tasks, parallel workflow, and VMs. Furthermore, to experiment with a large number of VMs and collect the data; a scheduling framework is also designed and implemented. Experimental results are presented to demonstrate the efficiency of the ICBM and framework

PENJADWALAN PEMADAMAN NODE SERVER PADA SERVER CLUSTER BERDASARKAN KLASTERISASI DATA UPTIME

The problem of server clusters is the use of electrical power. The data center, as a place to keep server clusters, in Indonesia consumed 1.5% of the national generating capacity in 2014. This percentage increased in 2017 to 2% of the national generating capacity or equivalent to the consumption of generating capacity for a combination of Jambi, Riau and West Sumatra.To solve this problem is to turn off the server node on the server cluster. With the turn off server nodes on server clusters scheduling, it is expected to determine the time and duration of turning off server nodes so that they can efficiently use electricity and maintain maximum server quality. In this study server cluster will use the least connection load balancing method. When the server cluster is running, data retrieval takes one, five and 15 minutes average load which is done every minute. Then the collected data is clustered using a combination of average linkage hierarchical clustering and K-Means clustering. The results of this clustering produce three cluster load averages that are "low", "medium" and "high". Load averages that included into the "low" category are sorted by the time of data retrieval to get the time and duration to turn off at each node. The results of the research result in scheduling turning off node one is 14.09 - 14.28, at node two is 14.47 - 06.57 and at node three is 06.57 - 07.18. Turn off server node scheduling is reduces the use of electrical power from 2,528 kWh to 2,519 kWh and does not affect availability, throughput and packet loss as server quality parameters.;---Permasalahan server cluster adalah penggunaan daya listrik. Data center, sebagai tempat menyimpan server cluster, di Indonesia mengkonsumsi 1.5% kapasitas pembangkit nasional pada tahun 2014. Presentase ini meningkat pada tahun 2017 menjadi 2% kapasitas pembangkit nasional atau setara dengan konsumsi kapasitas pembangkit untuk gabungan Jambi, Riau dan Sumatera Barat. Salah satu cara menanggulangi masalah ini adalah memadamkan node server pada server cluster. Dengan diadakannya penjadwalan pemadaman node server pada server cluster diharapkan dapat menentukan waktu dan durasi pemadaman sehingga dapat mengefisiensi penggunaan listrik dan menjaga kualitas server secara maksimal. Pada penelitian ini server cluster akan menggunakan metode load balancing least connection. Saat server cluster berjalan, setiap menit dilakukan pengambilan data load average satu, lima dan 15 menit. Kemudian data yang terkumpul diklasterisasi menggunakan gabungan algoritma average linkage hierarchical clustering dan K-Means. Hasil dari klasterisasi tersebut menghasilkan tiga cluster load average yaitu “rendah”, “sedang” dan “tinggi”. Load average yang termasuk ke dalam kategori “rendah” diurutkan berdasarkan waktu pengambilan data sehingga mendapatkan waktu dan durasi pemadaman pada setiap node. Hasil penelitian menghasilkan waktu penjadwalan pemadaman pada node satu adalah 14.09 - 14.28, pada node dua adalah 14.47 - 06.57 dan pada node tiga adalah 06.57 - 07.18. Penjadwalan pemadaman mengurangi penggunaan daya listrik sebesar dari 2.528 kWh menjadi 2.519 kWh dan tidak mempengaruhi availability, throughput dan packet loss sebagai parameter kualitas server

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio