Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021

Cloud computing is new technology that has considerably changed human life at different aspect over the last decade. Especially after the COVID-19 pandemic, almost all life activity shifted into cloud base. Cloud computing is a utility where different hardware and software resources are accessed on pay per user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization.VM used in data center for distribution of resource and application according to benefactor demand. Cloud data center faces different issue in respect of performance and efficiency for improvement of these issues different approaches are used. Virtual machine play important role for improvement of data center performance therefore different approach are used for improvement of virtual machine efficiency (i-e) load balancing of resource and task. For the improvement of this section different parameter of VM improve like makespan, quality of service, energy, data accuracy and network utilization. Improvement of different parameter in VM directly improve the performance of cloud computing. Therefore, we conducting this review paper that we can discuss about various improvements that took place in VM from 2015 to 20,201. This review paper also contain information about various parameter of cloud computing and final section of paper present the role of machine learning algorithm in VM as well load balancing approach along with the future direction of VM in cloud data center.

     

Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

Electricity price forecasting (EPF) is important for energy system operations and management which include strategic bidding, generation scheduling, optimum storage reserves scheduling and systems analysis. Moreover, accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Nevertheless, accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price. This work proposes a mid-term forecasting model based on the demand and price data, renewable and non-renewable energy supplies, the seasonality and peak and off-peak hours of working and non-working days. An optimized Gated Recurrent Unit (GRU) which incorporates Bagged Regression Tree (BTE) is developed in the Recurrent Neural Network (RNN) architecture for the mid-term EPF. Tanh layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model’s performance, error reduction and predict the spikes. In this work, the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May 2021. The results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.     

Time forward observer based adaptive controller for a teleoperation system

This paper presents a design of a teleoperation system using time forward observer-based adaptive controller. The controller is robust to the time-variant delays and the environmental uncertainties while assuring the stability and the transparent performance. A novel theoretical framework and algorithms for this teleoperation system have been built up with neural network-based multiple model control and time forward state observer. Conditions for stability and transparency performance are also investigated.     

Tracking setpoint robust model predictive control for input saturated and softened state constraints

This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes.     

Propagation of non-linear waves in a non-ideal relaxing gas

We have derived an evolution equation governing the far-field behaviour of small amplitude waves in a non-ideal relaxing gas for planar and converging flow. Asymptotic expansions of the flow variables for small amplitude waves have been used to derive the evolution equation. This equation turns out to be a generalized Burger's equation. The numerical solution of this equation is obtained by using the homotopy analysis method (HAM) proposed by Liao with two different initial conditions. Using the HAM, we have studied the effect of relaxation and nonlinearity. The convergence control parameter enables us to find a good approximate solution for such a complex flow problem. This method also confirms the capabilities and usefulness of convergence control parameter and HAM for complex and highly non-linear problems.     

A text representation model using Sequential Pattern-Growth method

Text representation is an essential task in transforming the input from text into features that can be later used for further Text Mining and Information Retrieval tasks. The commonly used text representation model is Bags-of-Words (BOW) and the N-gram model. Nevertheless, some known issues of these models, which are inaccurate semantic representation of text and high dimensionality of word size combination, should be investigated. A pattern-based model named Frequent Adjacent Sequential Pattern (FASP) is introduced to represent the text using a set of sequence adjacent words that are frequently used across the document collection. The purpose of this study is to discover the similarity of textual pattern between documents that can be later converted to a set of rules to describe the main news event. The FASP is based on the Pattern-Growth’s divide-and-conquer strategy where the main difference between FASP and the prior technique is in the Pattern Generation phase. This approach is tested against the BOW and N-gram text representation model using Malay and English language news dataset with different term weightings in the Vector Space Model (VSM). The findings demonstrate that the FASP model has a promising performance in finding similarities between documents with the average vector size reduction of 34% against the BOW and 77% against the N-gram model using the Malay dataset. Results using the English dataset is also consistent, indicating that the FASP approach is also language independent.     

Lakes of Malaysia: Water quality,eutrophication and management

The present study was undertaken to evaluate and verify the water quality status and trophic state of 15 major lakes and reservoirs in Malaysia. The lake water quality assessments were based on the National Water Quality Index (NWQI), while the trophic state assessments were based on Carlson's Trophic State Index (TSI). The findings of this water quality assessment, based on data collected between September and October 2012, indicated that a majority of the lakes were classified as Class II (Clean) waters suitable for recreational use. The results of the trophic state assessments, however, indicated that all of the lakes were eutrophic, meaning they were nutrient‐rich, they could experience algae blooms or macrophyte problems, and they were likely to exhibit poor water quality. Sustainable management measures and strategies are suggested to address the eutrophication problems of Malaysian lakes and reservoirs, with the national responses on lake and reservoir management also being discussed.     

Architecture design of a user-orientated electronic laboratory notebook: A case study within an atmospheric chemistry community

Data is particularly valuable to scientists when details of its provenance are known. This research concerned deploying a user-orientated electronic laboratory notebook (ELN) system within a scientific community. The ELN system supported the capture and retrieval of semantic metadata describing the provenance of the modelling activities of scientists within that community. The research was grounded within the atmospheric chemistry community but has applicability to other communities using an iterative model development process. The ELN system involved the automatic capture of metadata concerning the modelling process together with inline annotations added by the modeller explaining the reasoning for modelling decisions at each step of the process. A full realisation of the ELN system was built and evaluated by members of the atmospheric chemistry community. In order to promote reusability the ELN system architecture had domain-independent as well as domain-dependent elements. An ontology (in OWL) was used to ensure that the specific terminology of the community was used within the provenance metadata and also that it was used consistently. Other domain-independent elements of the architecture included a dynamic graphic interface that allowed the modeller to view his/her modelling history. This was recorded as a set of nodes each pointing to the stored provenance metadata associated with a specific simulation run. In addition, there was an innovative mechanism that enabled the modeller to navigate through the various nodes. The navigation process supported making comparisons between different nodes: a facility that users found particularly valuable. Members of the atmospheric chemistry community took part in a two-day summative evaluation of the ELN system. This confirmed its value to the modellers and it is now being introduced more widely across the modelling community. In addition, the research proposes a methodology for transferring this ELN system to other modelling communities making use of the domain-independent elements of the architecture.     

Satellite remote sensing of deforestation in the Sungai Buloh Forest Reserve,Peninsular Malaysia

Deforestation has been a very critical environmental problem during the past few decades. Monitoring the Earth's surface conditions and their changes are essential to the management of this global environmental problem. Remote sensing can provide an effective 1001 for monitoring environmental changes on a global scale. This has focused attention on developing more effective and efficient techniques for the management and survey of forest areas. This study was designed to assess the feasibility of utilizing Landsat TM data in monitoring deforestation in the Sungai Buloh Forest Reserve. Detection of land cover change due to urbanization was performed using rnulti-tempora! data taken in 1988and 1991. An IDRISI image processing system was used to analyse the satellite data. Multi-temporal images were registered and spectral signatures of each point were directly compared. Correlation analysis was used to evaluate the similarity of two spectral signatures. A false colour composite image of bands 4, 5 and 3 (Red-Green-Blue) was used in unsupervised classification. For detection of land cover change, both multi-temporal images were overlaid and the area was calculated. The study revealed that the rate of deforestation in the Sungai Buloh Forest Reserve is about 182ha yr^-1 with an accuracy of 90·0 per cent.     

Controlling parameters of focused ion beam (FIB) on high aspect ratio micro holes milling

Focused ion beam (FIB) direct milling is now recognized as a new method of fabrication, due to high flexibility in milling dimensions, the possible geometries and the material selectivity. This paper discusses the fabrication of micro holes using FIB direct milling in terms of high aspect ratio structures, including FIB parameters and the major effects of FIB milling. It is deduced that sputter yield of material gives a major impact to the depth of milling. Optimization parameters coupled control of FIB direct milling will provide a precise means of fabricating of high aspect ratio micro holes whilst resolving the problem of re-deposition and amorphisation which is common in micro milling.