The Accuracy Of Task Completion In The Implementation Of PATEN

This study aims to determine the accuracy of task completion in the implementation of PATEN in Candi District. This type of research is descriptive qualitative research. Data collection techniques were carried out by observation, interviews, documentation, and literature studies. Data analysis techniques are data collection, data reduction, data presentation and conclusion drawing. The results showed that the accuracy of the completion of tasks in the implementation of PATEN in Candi District, namely the completion of a PATEN application in the KTP service, could be completed within 7 days with a note that the file requirements were complete so that it could be processed immediately. In the management of ID cards, Candi Subdistrict employees have carried out their duties to the maximum. However, in terms of managing E-KTP, there are obstacles in terms of coordination with the Ministry of Home Affairs located in Jakarta where the server at the Ministry of Home Affairs is turned off every 3 hours for server maintenance purposes, then after that 5 minutes later it is turned on again. So this causes the processing of E-KTP takes a long time

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing.

ANALYSIS OF A CLEARING QUEUEING SYSTEM WITH SERVER MAINTENANCE AFTER N NEGATIVE FEEDBACKS

This paper is devoted to the study of a clearing queueing system with a special discipline. As soon as the server receives N negative feedbacks from customers, all present customers are forced to leave the system and the server undergoes a maintenance procedure. After an exponential maintenance time, the system resumes its service immediately. Using the matrix analytic method, we derive the steady-state distributions, which are then used for the computation of other performance measures. Furthermore, using first step analysis, we obtain the Laplace–Stieltjes transform of the sojourn time of an arbitrary customer. We also study the busy period of the system and derive the generating function of the total number of lost customers in a busy period. Finally, we investigate a long-run rate of cost and explore the optimal N value that minimizes the total cost per unit time. We also present some numerical examples to illustrate the impact of several model parameters to the performance measures.

Current Issues in Ciphertext Policy-Attribute Based Scheme for Cloud Computing: A Survey

The use of cloud computing has increased exponentially in data resources storage over the past few years. Cloud storage reduces the overall costs of server maintenance, whereby companies only pay for the resources they actually use in the cloud storage. Despite this, security concerns in cloud computing must be a top priority. One of the common encryption methods in cloud security is Attribute Based Encryption (ABE). ABE contains two types, namely, Ciphertext Policy-Attribute Based Encryption (CP-ABE) and Key Policy- Attribute based Encryption (KP-ABE). CP-ABE is better than KP-ABE, especially in reduplication issues and fine-grained access. However, issues in CP_ABE need further improvement. Improvement for the CP-ABE scheme has been growing rapidly since 2010 to date, and five main issues need improvement. This paper reviews the proposed CP-ABE schemes during the past three years. These schemes focus on solving the five issues identified inherent in the CP-ABE scheme.

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.

Big Data Processing

This chapter is a description of MapReduce, which serves as a programming algorithm for distributed computing in a parallel manner on huge chunks of data that can easily execute on commodity servers thus reducing the costs for server maintenance and removal of requirement of having dedicated servers towards for running these processes. This chapter is all about the various approaches towards MapReduce programming model and how to use it in an efficient manner for scalable text-based analysis in various domains like machine learning, data analytics, and data science. Hence, it deals with various approaches of using MapReduce in these fields and how to apply various techniques of MapReduce in these fields effectively and fitting the MapReduce programming model into any text mining application.

Cloud Computing

The population is living in a complex world at information explosion age. Today in Enterprises, the size and complexity of managing information have been increasing significantly and the demand for cost efficient information storage and processing grows higher. Enterprises need to optimize their IT management and minimize server maintenance costs become greater as usage demands prove to be increasingly unpredictable. Cloud computing offers a promising solution. Cloud computing is a computing model that relies on a large, centralized data center to store and process a great wealth of information. Computing power and storage space are provided on-demand to Enterprises that outsource their IT management to the cloud service provider. The immediate advantage to this computing model is a lower infrastructure maintenance cost. Since Enterprises that use cloud no longer require on-site servers, they eliminate the associated cost in IT management and electrical power.

A Practical Approach on Virtual Machine Live Migration

Virtualization technology enables organizations to take the benefit of different services, operating systems, and software without increasing their IT infrastructure liabilities. Live migration of virtual machine is the key features of the virtualization. It allows the administrator to move the virtual machine from one physical machine to another physical machine without any interruption. This technique is widely used for load balancing, server maintenance, and resource consolidation. The virtual machine migration problem consists of four distinct steps. The first step is to select the host from where VM migrated. After selecting, the host next step is to select the VM, which is migrated. The third step is to select the host where the migrated VM will be placed, and the last step is to decide the method, which is used to transfer the VM. This chapter covers all the basic information related to VM migration.