RVEC: Efficient Remote Desktop for the ENGINEERING CLOUD

Diabetic retinopathy is the leading cause of blindness in the adult population. Mass-screening efforts, during which high resolution images of the retina are captured, are therefore underway in order to detect the disease in its early stages. In this paper we evaluate the compression performance of several lossless image compression algorithms that could be employed in a retina Picture Archiving and Communications System to lessen the demand on computing resources. The algorithms we analyse are TIFF PackBits, Lossless JPEG, JPEG-LS, and JPEG2000 all of which are incorporated in the current DICOM standard together with the non-standard CALIC algorithm for benchmark comparison. Compression performance is evaluated in terms of compression ratio, compression speed, and decompression speed. Based on a large dataset of more than 800 colour retinal images, divided into groups according to retinal region (nasal, posterior, and temporal) and image size, JPEG-LS is found to be the most suitable compression algorithm, offering good compression ratios combined with high compression and decompression speed. Compression ratios can be further improved through the application of a reversible colour space transformation prior to compression as a second set of experiments show.

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image file. The main objective of this study was to reduce the physical space on the various storage media and reduce the time of sending data over the Internet with a complete guarantee of encrypting this data and hiding it from intruders. Two techniques are implemented, with data loss (Lossy) and without data loss (Lossless). In the proposed paper a hybrid data compression algorithm increases the input data to be encrypted by RSA (Rivest-Shamir-Adleman) cryptography method to enhance the security level and it can be used in executing lossy and lossless compacting Steganography methods. This technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space on different storage media. The plain text is compressed by the Huffman coding algorithm, and also the cover image is compressed by Discrete wavelet transform DWT based that compacts the cover image through lossy compression in order to reduce the cover image's dimensions. The least significant bit LSB will then be used to implant the encrypted data in the compacted cover image. We evaluated that system on criteria such as percentage Savings percentage, Compression Time, Compression Ratio, Bits per pixel, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Compression Speed. This system shows a high-level performance and system methodology compared to other systems that use the same methodology.

This paper is proposing a novel approach for lossless compression of both gray scale and color images. This approach is exploiting the fact that neighboring pixel values of most of the real world images do not change drastically. A code book of 256 × 256 known at both the compressing and decompressing stations is used as reference for the proposed lossless compression algorithm where a compression ratio dependent on code book design concepts is realized. This code book design is considered in an innovative way so as to further minimize the compression ratio in multiple iterations. Two types of code book designs are discussed in the paper. Level of compression, compressed image and the code book identification number shall be send to the receiver who decompresses the image based on the level and code books available. This method is highly useful for the exact type of images when the code books are designed specifically for them. Simulation results are explained to show how compression ratio and compression speed are improved in both the present method as compared to conventional methods.

INTRODUCTION: Genetic data plays a key role in the healthcare area in specific, but they are typically very large in size. Many research shows that absence of DNA information at the right time is one of the major causes of error in the healthcare area. The more genomics information that analysts secure, the better the prospects for individual and general wellbeing. Persevering and retrieving genetic information in the right form within the given time is a big challenge in the field of Healthcare. Effectively, pre-birth DNA tests screen for formative variations from the norm. Before long, patients will have their blood sequenced to detect any nonhuman DNA that may flag an irresistible illness. Later on, somebody managing malignancy will most likely track the movement of the sickness by having the DNA and RNA of single cells from various tissues sequenced every day. DNA sequencing of whole population will give a complete and better prediction of population wellbeing.OBJECTIVES: Hereditary data is growing exponentially; hence it is hard to deal with the consistently developing hereditary database. The human genome in its base configuration occupies almost thirty terabyte of storage space. Computational assets are constrained. Not just storage, transmission abilities and run time memory is likewise constrained. Data Compression is a test when the hereditary information is exponentially expanding. It is critical to save the integrity of hereditary information while packing it. Hence the main objective of this paper is to develop a lossless DNA compression algorithm that not only gives better compression but also help in retrieval of Information for efficient use in the area of Healthcare.METHODS: In this paper a lossless hereditary data compression method is being proposed. The proposed calculation works in a horizontal mode and utilization a reference based substitution technique for compression. The principle thought of this paper is in the kind of similarity scanned. All the predominant hereditary Compression methods search for similarity within the chromosome. These calculations either pursue flat mode or vertical mode for accomplishing compression. But whichever method the existing genetic compression algorithms use, they are all based on searching similarities within the chromosome i.e. they exploit only inter chromosomal similarities. The current studies focus will show that compression ratio achieved by analyzing individual chromosome is always less than the method in which we analyze and compress intra chromosomal similarities.RESULTS: This study shows that by simply using exactly matching repeats amongst all the chromosomes of the same genome, not only the compression ratio is improving but also a detailed study of all the similarities and differences between two genomes of the same species can be conducted.CONCLUSION: In this study, a new compression algorithm is being proposed for compressing DNA. Along with Inter chromosomal similarities, Intra chromosomal similarities are considered for this method. The results clearly shows that intra chromosomal matches are bigger and more than inter chromosomal matches which helps us to achieve better compression ratio.

Cache-block compression is a highly effective technique for both reducing accesses to lower levels in the memory hierarchy (cache compression) and minimizing data transfers (link compression). While many effective cache-block compression algorithms have been proposed, the design of these algorithms is largely ad hoc and manual and relies on human recognition of patterns. In this article, we take an entirely different approach. We introduce a class of “byte-select” compression algorithms, as well as an automated methodology for generating compression algorithms in this class. We argue that, based on upper bounds within the class, the study of this class of byte-select algorithms has potential to yield algorithms with better performance than existing cache-block compression algorithms. The upper bound we establish on the compression ratio is 2X that of any existing algorithm. We then offer a generalized representation of a subset of byte-select compression algorithms and search through the resulting space guided by a set of training data traces. Using this automated process, we find efficient and effective algorithms for various hardware applications. We find that the resulting algorithms exploit novel patterns that can inform future algorithm designs. The generated byte-select algorithms are evaluated against a separate set of traces and evaluations show that Byte-Select has a 23% higher compression ratio on average. While no previous algorithm performs best for all our data sets which include CPU and GPU applications, our generated algorithms do. Using an automated hardware generator for these algorithms, we show that their decompression and compression latency is one and two cycles respectively, much lower than any existing algorithm with a competitive compression ratio.

Towards tenant demand-aware bandwidth allocation strategy in cloud datacenter

This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade‐off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.

To review the literature on lossy compression in dental radiography and to discuss the importance and suitability of the methodology used for evaluation of image compression. A search of Medline (from 1966 to October 2004) was undertaken with the search expression "(Radiography, dental) and compression". Inclusion criterion was that the reference should be evaluating the effect of lossy image compression on diagnostic accuracy. For all included studies, information in relation to mode of image acquisition, image content, image compression, image display, and method of image evaluation was extracted. 12 out of 32 papers were included in the review. The design of these 12 studies was found to vary considerably. Parameters used to express the degree of information loss (DIL) were either or both compression ratio (CR) and compression level (CL). The highest acceptable CR reported in the studies ranged from 3.6% to 15.4%. Furthermore, different CR values were proposed even for the same diagnostic task, for example, for caries diagnosis CR ranged from 6.2% to 11.1%. Lossy image compression can be used in clinical radiology if it does not conflict with national law. However, the acceptable DIL is difficult to express and standardize. CR is probably not suitable to express DIL, because it is image content dependent. CL is also probably not suitable to express DIL because of the lack of compression software standardization.

Flat panel displays are electronic displays that are thin and lightweight, making them ideal for use in a wide range of applications, from televisions and computer monitors to mobile devices and digital signage. The Thin-Film Transistor (TFT) layer is responsible for controlling the amount of light that passes through each pixel and is located behind the liquid crystal layer, enabling precise image control and high-quality display. As one of the important parameters to evaluate the display performance, the faster response time provides more frames in a second, which benefits many high-end applications, such as applications for playing games and watching movies. To further improve the response time, the single-pixel charging efficiency is investigated in this paper by optimizing the TFT dimensions in gate driver circuits in active-matrix liquid crystal displays. The accurate circuit simulation model is developed to minimize the signal's fall time (Tf) by optimizing the TFT width-to-length ratio. Our results show that using a driving TFT width of 6790 μm and a reset TFT width of 640 μm resulted in a minimum Tf of 2.6572 μs, corresponding to a maximum pixel charging ratio of 90.61275%. These findings demonstrate the effectiveness of our optimization strategy in enhancing pixel charging efficiency and improving display performance.

Due to the high sampling rate, the recorded Electrocardiograms (ECG) data are huge. For storing and transmitting ECG data, wide spaces and more bandwidth are therefore needed. The ECG data are also very important to preprocessing and compress so that it is distributed and processed with less bandwidth and less space effectively. This manuscript is aimed at creating an effective ECG compression method. The reported ECG data are processed first in the pre-processing unit (ProUnit) in this method. In this unit, ECG data have been standardized and segmented. The resulting ECG data would then be sent to the Compression Unit (CompUnit). The unit consists of an algorithm for lossy compression (LosyComp), with a lossless algorithm for compression (LossComp). The randomness ECG data is transformed into high randomness data by the failure compression algorithm. The data's high redundancy is then used with the LosyComp algorithm to reach a high compression ratio (CR) with no degradation. The LossComp algorithms recommended in this manuscript are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). LossComp algorithms such as Arithmetic Encoding (Arithm) and Run Length Encoding (RLE) are also suggested. To evaluate the proposed method, we measure the Compression Time (CompTime), and Reconstruction Time (RecTime) (T), RMSE and CR. Simulation results suggest the highest output in compression ratio and in complexity by adding RLE after the DCT algorithm. The simulation findings indicate that the inclusion of RLE following the DCT algorithm increases performance in terms of CR and complexity. With CR = 55% with RMSE = 0:14 and above 94% with RMSE = 0:2, DCT as a LossComp algorithm was utilized initially, followed by RLE as a LossComp algorithm.

Services redeployment is one of the critical techniques for energy-efficiency in cloud data centres. In recent years, cloud providers have been providing local storage for cloud services, since it offers a better performance with identified price. Nevertheless, most existing work did not consider the problems introduced by utilising local storage, e.g., migrating much more data, and therefore consuming much more migration time and network bandwidth. Meanwhile, instance migration is a costly operation, the number of migrated instances must be considered. However, the data size and the number of instances on servers are not often accordant, and therefore a tradeoff should be made. To address this problem, this paper proposes S3R, a storage-sensitive services redeployment approach. S3R firstly builds a tradeoff model to estimate the release cost for each server, and then adopts a FFD-based heuristic algorithm to migrate/redeploy instances. Evaluation results on production traces demonstrate the effectiveness of S3R.

Network virtualization is being regarded as a promising technology to create an ecosystem for cloud computing applications. One critical issue in network virtualization technology is power-efficient virtual network embedding (PE-VNE), which deals with the physical resource allocation to virtual nodes and links of a virtual network while minimizing the energy consumption in the cloud data center. When the node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VN embedding problem is NP-hard, even in the offline case. This paper aims to investigate the ability of the Ant-Colony-Optimization (ACO) technique in handling PE-VNE problem. We propose an ACO-based heuristic PE-VNE algorithm, called E-ACO. E-ACO minimizes the energy consumption by considering the embedding power consumption in the node mapping phase and by making an implicit coordination between the node and link mapping phases. Extensive simulations are conducted to evaluate the performance of the proposed algorithm and investigate different energy-aware link embedding algorithms on the ability of E-ACO.

Server consolidation through virtualization improves resource utilization significantly in Cloud Data Centres (CDCs). We study the case of a CDC hosting heterogeneous Physical Machines (PMs) as a variable size vector bin-packing problem. The PMs have different configurations of multiple resources like CPU, RAM, Disk Storage and Network Bandwidth. In this paper, we propose PMNeAR-vector heuristic for PM selection in PM-heterogeneity aware Virtual Machine (VM) initial placement. The proposed heuristic is compared with well-known heterogeneity aware FFD-DRR and BFD bin centric heuristics using a dataset with random instances of both VMs and PMs of heterogeneous configurations. Fifty rounds of VM initial placement simulation experiments were conducted to validate the average resource wastage. The results show that on average FFD-DRR and BFD bin centric heuristics are wasting 22.62% and 37.27% more resource units compared to the proposed PMNeAR-vector heuristic.

Over-committing computing resources is a widely adopted strategy for increased cluster utilization in Infrastructure as a Service (IaaS) cloud data centers. A potential consequence of over-committing computing resources is memory overload of physical machines (PMs). Memory overload occurs if memory usage exceeds a defined alarm threshold, exposing running computation tasks at a risk of being terminated by the operating system. A prevailing measure to handle memory overload of a PM is live migration of virtual machines (VMs). However, this not only consumes network bandwidth, CPU, and other resources, but also compels a temporary unavailability of the VMs being migrated. To handle memory overload, we present a memory sharing system in this paper for PMs in cloud data centers. With memory sharing, a PM automatically borrows memory from a remote PM when necessary, and releases the borrowed memory when memory overload disappears. This is implemented through swapping inactive memory pages to remote memory resource. Experimental studies conducted on InfiniBand-networked PMs show that the memory sharing system is fully functional. The measured throughput and latency are around 929 Mbps and 1.3 mus, respectively, on average for remote memory access. They are similar to those from accessing a local-volatile memory express solid-state drive, and thus are promising in real applications.