Cloud Service Providers offer various pricing schemes to charge for their computational resources. Cloud Service Users can opt foron-demand,reserved, orspotinstances for their requirements. The overall cost of an application depends on the instances chosen to execute the job. Finding the optimal reservation amount is a significant research problem, and it is more challenging when one uses spot instances with unpredictable prices. We have proposed two algorithms to determine the reservation amount, and for both algorithms, the future spot prices are unknown. But the first algorithm assumes that future demands are known. The second algorithm does not make any such assumption and yet can ensure that the cost of reservation and usage of cloud resources is within a factor$2-\frac{u_h}{e_c}$of the optimal cost where$u_h$is the usage cost per hour of the reserved instance and$e_c$is the average cost per hour for unreserved instances. We have compared our findings with that of some recent works in the literature. We have also given anInteger Linear Programming(ILP) formulation of the problem. Experimental results show that our algorithm differs in cost from ILP by less than 21%.

This article describes an approach to unsupervised learning of morphology from an unannotated corpus for a highly inflectional Indo-European language called Assamese spoken by about 30 million people. Although Assamese is one of Indias national languages, it utterly lacks computational linguistic resources. There exists no prior computational work on this language spoken widely in northeast India. The work presented is pioneering in this respect. In this article, we discuss salient issues in Assamese morphology where the presence of a large number of suffixal determiners, sandhi, samas, and the propensity to use suffix sequences make approximately 50% of the words used in written and spoken text inflected. We implement methods proposed by Gaussier and Goldsmith on acquisition of morphological knowledge, and obtain F-measure performance below 60%. This motivates us to present a method more suitable for handling suffix sequences, enabling us to increase the F-measure performance of morphology acquisition to almost 70%. We describe how we build a morphological dictionary for Assamese from the text corpus. Using the morphological knowledge acquired and the morphological dictionary, we are able to process small chunks of data at a time as well as a large corpus. We achieve approximately 85% precision and recall during the analysis of small chunks of coherent text.

We propose a new multistage interconnection network (MIN) called Generalized Shuffle Exchange (GSE) to connect N processors and N resources where N need not be a power of 2, but only an even number. It uses N/2 switches per stage and the number of stages is equal to [log N]. We show that GSE is a full-access network. Routing between any input-output pair in GSE is simple and can be done by using a routing vector, generated from the input and output addresses. When N is a power of 2, say 2n, GSE reduces to conventional n-stage network with unique path for each input-output pair. But, if 2n-1ngiven a specific input, there are 2[logN]-N outputs for which there exist alternative paths. Therefore, to realize any N×N permutation we are to select a set of N conflict free paths, one for each input-output connection. Now, the problem of determining whether any given permutation is realizable in a single pass by a MIN is known as the permutation admissibility problem. Here, we have presented a scheme for resolving the permutation admissibility problem in a GSE.

We show that by adding eight extra edges, referred to as bridges, to an n-cube (n/spl ges/4) its diameter can be reduced by 2, and by adding sixteen bridges to an n-cube (n/spl ges/6) its diameter can be reduced by 3. We also show that by adding (/sub m+1sup 4m/)+1(m/spl ges/2) bridges to an n-cube (n/spl ges/4m and n/spl ges/8) its diameter can be reduced by 2m and by adding 2(/sub msup 4m-3/)+1, (m>2) to an n-cube (n/spl ges/4m-2 and n/spl ges/10) its diameter can be reduced by 2m-1. We also consider the reduction of diameter of an n-cube by exchanging some independent edges (twisting), where two edges are called independent if they are not incident on a common node. We have shown that by exchanging four pairs of independent edges in a d-cube (d/spl ges/5), we can reduce its diameter by 2. By exchanging sixteen pairs of independent edges, the diameter of a d-cube (d/spl ges/7) can be reduced by 3. By exchanging 57 pairs of independent edges, the diameter can be reduced by 4 for d/spl ges/9. To reduce the diameter by lower bound [d/2], (d/spl ges/10) we need to exchange (/sub r+1sup d-1/) pairs of independent edges, where r=lower bound [d/4]+1.>