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%.

Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources’ demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.

With more awareness and growth in the cloud market, demands for computational resources have increased in order to provide services to the cloud users. Sometimes it is difficult for an individual cloud service provider (CSP) to meet the level of promised quality of service (QoS) and to fulfill all types of resource requests dynamically. Cloud federation has become a consolidated paradigm in which group of cooperative CSPs share their unused resources with peers to gain some economic benefit. Hence, the cloud federation overcomes the limitation of each CSP for maintaining QoS during sudden spikes in resource demand. However, the presence of untrusted CSPs degrades the QoS of the services delivered through federation. Trusted CSPs are highly reputed in the federation as they can extend their resources and services to maintain the level of committed QoS by the member CSPs of the federation. Therefore, to guarantee delivery of committed QoS, it will be necessary to form a federation with trusted CSPs only. In this paper, we present a broker based cloud federation architecture. The cloud federation formation is modeled as a hedonic coalitional game. The main objective of this work is to find the most suitable and stable federation of trusted CSPs that will maximize the satisfaction level of each individual CSP on the basis of QoS and profit. The proposed coalitional game inspired cloud federation formation (CGCFF) algorithm has been extensively compared with selected existing techniques. Simulation results show that the set of federation formed by CGCFF is Nash-stable and performs better than these techniques in terms of satisfaction, quality and profit.

MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target interactions are still lacking. This motivated us to explore the in silico target interaction features and incorporate them in predictive modeling. We propose a systematic approach towards developing a sensitive miRNA target prediction model to explore the interplay of target recognition features. In the first step, we have employed a supervised ensemble under-sampling approach to address the problem of imbalance in the training dataset due to a larger number of negative instances. Various feature selection techniques were evaluated to obtain the optimal feature subset that best recognizes the true miRNA-mRNA targets. In the second step, we have built our optimal model, miRTPred, a novel blending ensemble-based approach that combines the predictions of the best performing traditional and classical ensemble models, through a weighted voting classifier, achieving a sensitivity of 87 percent and F1-score of 0.88 for 3'UTR region of the mRNA transcript. miRTPred outperforms popular machine learning (ML) and non-ML approaches to target prediction algorithms. miRTPred is freely available at http://bicresources.jcbose.ac.in/zhumur/mirtpred.

In order to achieve high performance and throughput, most of the computing applications nowadays are developed in a distributed computing environment. Such an environment is easily provided by a cloud computing platform. The distributed applications running on cloud demand huge network resources for transfer of data along with computing resources (Virtual Machines). Efficient allocation of virtual machines on hosts can help in the reduction of active hosts which lead to a decrease in deployment cost as well an energy consumption of data centers. We emphasize that while allocating VMs to hosts the communications among the VMs (in the case of distributed applications) cannot be ignored altogether. A placement algorithm which takes into account the cost of physical hosts as well as the communication among VMs can significantly reduce i) the cost of network resource usages ii) energy spent in communication iii) time to complete data transfer and thus improve performance. We have given an ILP formulation of this VM placement problem and then proposed a novel heuristic algorithm to achieve a near optimal solution. We have also analyzed the performance improvement of the proposed algorithm over a few well-known algorithms.

Cloud service providers (CSPs) adapt different pricing models for their offered services. Some of the models are suitable for short term requirement while others may be suitable for the cloud service user's (CSU) long term requirement. For example, reservation-based pricing model is appropriate for a CSU's long term demand for resources. Finding the optimal amount of resources to be reserved in advance, to minimize the total cost, needs sufficient research effort. Various algorithms were discussed in the last couple of years to solve the resource reservation problem but most of them are based on integer programming problem (IPP) which is NP in nature. In this paper, we derive some heuristic-based polynomial time algorithms to find some near optimal solution to this problem. We show that the cost for CSU using our approach is comparable to the solution obtained using optimal IPP.

Complexity of cloud services acts as a barrier towards adopting cloud to some of the Cloud Service Users. Cloud Service Middleware plays an important role to get rid of such problems. The middleware manages and optimizes the cloud resources to execute various jobs submitted by the users. A middleware can be enhanced to utilize the idle time of the reserved resources in cloud environment by scheduling these resources free of cost to jobs submitted by the same Cloud Service User (CSU) or a different CSU. This enhancement not only makes it possible to utilize the resources to their fullest extent, but also reduces the usage cost of the CSU who reserved the resources (or a different CSU in certain cases). However, finding the mapping between the jobs and available pool of resources is a key challenge to the design of a middleware. This paper proposes some scheduling algorithms to find such mappings that minimizes the job execution cost within public cloud.

BACKGROUND: High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat's serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently. RESULTS: We analysed the SRA dataset (SRX026839 and SRX026838) consisting of single end reads and SRA data SRR1027730 consisting of paired-end reads. We used TopHat v2.0.8 to analyse these datasets and noted the CPU usage, memory footprint and execution time during spliced alignment. With this basic information, we designed PVT, a pipelined version of TopHat that removes the redundant computational steps during 'spliced alignment' and breaks the job into a pipeline of multiple stages (each comprising of different step(s)) to improve its resource utilization, thus reducing the execution time. CONCLUSIONS: PVT provides an improvement over TopHat for spliced alignment of NGS data analysis. PVT thus resulted in the reduction of the execution time to ~23% for the single end read dataset. Further, PVT designed for paired end reads showed an improved performance of ~41% over TopHat (for the chosen data) with respect to execution time. Moreover we propose PVT-Cloud which implements PVT pipeline in cloud computing system.

Amazon's spot instances allow customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current spot price. Customers may expect their services at lower cost with spot instances compared to on-demand or reserved. However, the reliability is compromised since the instances providing the service may become unavailable at any time. In this paper, we study various check pointing schemes that can be used with spot instances. Also we devise some algorithms for check pointing scheme on top of application-centric resource provisioning framework that increase the reliability while reducing the cost significantly.

In recent years most of the cloud computing community adopt an infrastructure centric approach to optimize the cost of cloud resources considering both Cloud Computing Service Users(CCSU) and Cloud Computing Service Providers(CCSP). In this paper we have brought up the concept of Application-centric Cloud. With Application-centric Cloud we propose an architecture to provide a generalized framework for auto deploying, sharing, providing scalability, robustness and availability of any cloud based application. Along with application independence, the framework also considers CCSP independence so that an application can use resources across providers.