Optimal Placement Algorithms for Virtual Machines

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers

Queuing-Inventory Models with MAP Demands and Random Replenishment Opportunities

Combining the study of queuing with inventory is very common and such systems are referred to as queuing-inventory systems in the literature. These systems occur naturally in practice and have been studied extensively in the literature. The inventory systems considered in the literature generally include (s, S)-type. However, in this paper we look at opportunistic-type inventory replenishment in which there is an independent point process that is used to model events that are called opportunistic for replenishing inventory. When an opportunity (to replenish) occurs, a probabilistic rule that depends on the inventory level is used to determine whether to avail it or not. Assuming that the customers arrive according to a Markovian arrival process, the demands for inventory occur in batches of varying size, the demands require random service times that are modeled using a continuous-time phase-type distribution, and the point process for the opportunistic replenishment is a Poisson process, we apply matrix-analytic methods to study two of such models. In one of the models, the customers are lost when at arrivals there is no inventory and in the other model, the customers can enter into the system even if the inventory is zero but the server has to be busy at that moment. However, the customers are lost at arrivals when the server is idle with zero inventory or at service completion epochs that leave the inventory to be zero. Illustrative numerical examples are presented, and some possible future work is highlighted

Plant Identification based on Fractal Refinement Technique (FRT)

AbstractWe propose here a new algorithm for plant classification and identification based on fractal dimension. It is a simple and efficient technique for identifying plants using three levels of fractal refinement on leaf images. Contour, Contour-Nervure and Nervure fractal dimensions are computed and are used in the first, second and third level of refinement respectively. A 50 set species with each set containing 10 samples are used for training the algorithm. The performance of the algorithm was examined with a test set of 500 leaves arbitrarily selected from different groups of species. The fault acceptance rate (FAR), the fault rejection rate (FRR) and the classification accuracy of the algorithm were analyzed experimentally and demonstrated that the proposed method has an accuracy rate of 84%

Data Governance in Data Mesh Infrastructures: The Saxo Bank Case Study

Data governance (DG) is the management of data in a manner that the value of data is maximised and data related risks are minimised. Three aspects of DG are data catalogue, data quality, and data ownership and these aim to provide transparency, foster trust, and manage access and control the data. DG solution involves change management and alignment of incentives and mere technology is not enough to address this. In this paper we aim to provide a holistic view of data governance that is a synthesis of academic and practitioner viewpoints, and conclude by giving an example of a pilot case study (Saxo Bank) where authors worked on tech and cultural interventions to address the data governance challenges

DEVELOPMENT AND VALIDATION OF RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF PARACETAMOL AND LORNOXICAM IN BULK AND PHARMACEUTICAL DOSAGE FORM

A simple, rapid, accurate and precise isocratic reversed phase high performance liquid chromatographic method has been developed and validated for simultaneous estimation of Paracetamol and Lornoxicam in tablet dosage form. The chromatographic separation was carried out on Zorbax C18 column (150 mm x 4.6 mm I.D., 5 µm particle size) with a mixture of 20 mM ammonium acetate pH 3.2 buffer and acetonitrile in the ratio of 60:40 v/v as a mobile phase at a flow rate of 1.0 mL/min. UV detection was performed at 265 nm. The retention times were 2.74 minutes and 5.36 minutes for Paracetamol and Lornoxicam respectively. Calibration plots were linear (r2=0.999 for both Paracetamol and Lornoxicam respectively) over the concentration range of 6.25-250 µg/mL for Paracetamol and 0.1-4 µg/mL for Lornoxicam. The method was validated for linearity, precision, accuracy, ruggedness and robustness. The proposed method was successfully used for simultaneous estimation of Paracetamol and Lornoxicam in tablet dosage form. Validation studies revealed that the proposed method is specific, rapid, reliable and reproducible. The high % recovery and low % RSD confirms the suitability of the proposed method for routine quality control analysis of Paracetamol and Lornoxicam in bulk and tablet dosage form

3,​3-​dichloro-​1,​2-​diphenylcyclopropene (CPICl)​-​mediated synthesis of Nα-​protected amino acid azides and α-​ureidopeptides

Rapid synthesis of acid azides via in situ generation of acid chlorides using CPICl as chlorinating agent from the corresponding Nα -protected amino acids is described. Also the conversion of acid azides into ureidopeptides through the Curtius rearrangement under ultrasonication is delineated. The mildness of the protocol renders the acid-sensitive substrates to afford the corresponding amino acid azides and ureidopeptides in good yields. Diphenylcyclopropenone has also been recovered from the reaction mixture and reused