Smrati Gupta

Risk-Driven Framework for Decision Support in Cloud Service Selection

The growth in the number of cloud computing users has led to the availability of a variety of cloud based services provided by different vendors. This has made the task of selecting suitable set of services quite difficult. There has been a lot of research towards the development of suitable decision support system (DSS) to assist users in making an optimal selection of cloud services. However, existing decision support systems cannot address two crucial issues: firstly, the involvement of both business and technical perspectives in decision making simultaneously and, secondly, the multiple-clouds services based selection using single DSS. In this paper, we tackle these issues in the light of solving the problem of cloud service discovery. In particular, we present the following novel contributions: Firstly, we present critical analysis of the state-of-the-art in decision support systems. Based on our analysis, we identify critical shortcomings in the existent tools and develop the set of requirements which should be met by a potential DSS. Secondly, we present a new holistic framework for the development of DSS which allows a pragmatic description of user requirements. Additionally, the data gathering and analysis is studied as an integral part of the proposedDSS and therefore, we present concrete algorithms to assess the data for an optimal service discovery. Thirdly, we assess our framework for applicability to cloud service selection using an industrial case study. We also demonstrate the implementation and performance of our proposed framework using a prototype which serves as a proof of concept. Overall, this paper provides novel and holistic framework for development of a multiple cloud service discovery based decision support system.

Using machine learning to optimize parallelism in big data applications

Abstract In-memory cluster computing platforms have gained momentum in the last years, due to their ability to analyse big amounts of data in parallel. These platforms are complex and difficult-to-manage environments. In addition, there is a lack of tools to better understand and optimize such platforms that consequently form the backbone of big data infrastructure and technologies. This directly leads to underutilization of available resources and application failures in such environment. One of the key aspects that can address this problem is optimization of the task parallelism of application in such environments. In this paper, we propose a machine learning based method that recommends optimal parameters for task parallelization in big data workloads. By monitoring and gathering metrics at system and application level, we are able to find statistical correlations that allow us to characterize and predict the effect of different parallelism settings on performance. These predictions are used to recommend an optimal configuration to users before launching their workloads in the cluster, avoiding possible failures, performance degradation and wastage of resources. We evaluate our method with a benchmark of 15 Spark applications on the Grid5000 testbed. We observe up to a 51% gain on performance when using the recommended parallelism settings. The model is also interpretable and can give insights to the user into how different metrics and parameters affect the performance.

Physical-layer network coding based on integer-forcing precoded compute and forward

In this paper, a novel precoding technique is proposed for implementing Physical layer Network Coding (PNC) based on Compute and Forward (CF). In particular, an integer forcing precoder (IFP) is proposed that avoids the maximum rate achievability limitation due to channel approximation at the receiver in previous proposals. The probability of error from the proposed in previous proposals. The probability of error from the proposed scheme is shown to have up to 2 dB of gain over the existent lattice network coding based implementation of CF. Further, it is shown that the extra power penalty which is paid due to precoding is upper bounded with finite value. It is also shown that the achievable rate using IFP approaches the upper bound for CF. Our precoder requires Channel State Information (CSI) at the transmitter but only that of the channel between the transmitter and relay, which is a feasible assumption.

Physical Layer Network Coding Based on Integer Forcing Precoded Compute and Forward

In this paper, we address the implementation of physical layer network coding (PNC) based on compute and forward (CF) in relay networks. It is known that the maximum achievable rates in CF-based transmission is limited due to the channel approximations at the relay. In this work, we propose the integer forcing precoder (IFP), which bypasses this maximum rate achievability limitation. Our precoder requires channel state information (CSI) at the transmitter, but only that of the channel between the transmitter and the relay, which is a feasible assumption. The overall contributions of this paper are three-fold. Firstly, we propose an implementation of CF using IFP and prove that this implementation achieves higher rates as compared to traditional relaying schemes. Further, the probability of error from the proposed scheme is shown to have up to 2 dB of gain over the existent lattice network coding-based implementation of CF. Secondly, we analyze the two phases of transmission in the CF scheme, thereby characterizing the end-to-end behavior of the CF and not only one-phase behavior, as in previous proposals. Finally, we develop decoders for both the relay and the destination. We use a generalization of Bezout’s theorem to justify the construction of these decoders. Further, we make an analytical derivation of the end-to-end probability of error for cubic lattices using the proposed scheme.

Scoring Cloud Services Through Digital Ecosystem Community Analysis

Cloud service selection is a complex process that requires assessment of not only individual features of a cloud service but also its ability to interoperate with an ecosystem of cloud services. In this position paper, we address the problem by devising metrics to measure the impact of interoperability among the cloud services to guide the cloud service selection process. We introduce concrete definitions and metrics to contribute to measuring the level of interoperability between cloud services. We also demonstrate a methodology to evaluate the metrics via a use case example. Our contributions prove that the proposed metrics cover critical aspects related to interoperability in multi-cloud arena and therefore form a robust baseline to compare cloud services in systematic decision making environments.

Game theoretical analysis of the tradeoff between QoE and QoS over satellite channels

In this work, a game-theoretic analysis of a video exchange application in which two users exchange their video streams over a satellite channel using Quality of Experience (QoE) driven rate adaptation is studied. In such an interaction, users aim at maximizing the Quality of Service (QoS) and QoE of their received video while minimizing their individual cost incurred by their video transmission, which is modeled as a repeated game. Given the payoff model of the users, it is shown that adaptive video exchange between selfish autonomous nodes for a deterministic time will not be sustained. However, if video is exchanged over an unlimited or indeterminate period, the nodes have an incentive to cooperate and exchange video streams with QoE-driven rate adaptation based on the trust they build among themselves. Our simulation results show that a tradeoff exists between the QoS and the QoE of the perceived video. Furthermore, it is shown that the expected interaction length has a high impact on such tradeoff.

On Supporting Service Selection for Collaborative Multi-cloud Ecosystems in Community Networks

Internet and communication technologies have lowered the costs for communities to collaborate, leading to new services and collectively built infrastructures like community networks. Community networks get formed when individuals and local organisations from a geographic area team up to create and run a community-owned IP network to satisfy the communitys demand for ICT, such as facilitating Internet access and providing services of local interest. To address the limitation and enhance utility of community networks, we deploy collaborative clouds in community networks that allow interesting applications to be developed for serving local needs of communities. Such collaborative clouds employ resources contributed by the members of the community network for provisioning infrastructure and software services, and adapt to the specific social, economic and technical characteristics of the community networks. We need to support mechanisms that provide assistance in cloud service selection while taking into account different aspects pertaining to associated risks in community clouds, quality concerns of the users and cost limitations specifically in multi-clouds ecosystems. This paper proposes a risk-cost-quality based decision support system to assist the community cloud users to select the most appropriate cloud services meeting their needs. The proposed framework not only increases the ease of adoption of community clouds by providing assistance to users in cloud service selection, but also provides insights into the improvement of community clouds based on user behaviour.

Security-Centric Evaluation Framework for IT Services.

Tremendous growth and adoption of cloud based services within IT enterprises has generated important requirements for security provisioning. Users need to evaluate the security characteristics of different providers and their offered services. This generates an additional requirement for methods to compare cloud service providers on the basis of their capabilities to meet security requirements. This paper proposes a novel framework to assess and compare cloud services on the basis of their security offerings, leveraging existing best practices and standards to develop new relevant metrics. We provide comparison yardsticks related to security to evaluate cloud services such that the security robustness of cloud services can be computed using easy to evaluate deconstructed metrics. This paper provides a framework that can be leveraged to provide security enhancement plans both for users and providers.

Cloud Service Offer Selection

In the application economy, digital business initiatives are at the forefront of the growth strategy of many companies. Cloud based solutions offer a significant competitive advantage for both large companies and SMEs, leading to a rapid increase in the number of Cloud Service Providers (CSP). An important CSP driver is the improvement of consumers’ experience through digital platforms that allow users to access data and services from any location and through multiple channels with assured performance and availability.

Cognitive dual satellite systems

In this chapter, we explore the existing trends in the field of cognitive dual satellite systems (DSS). The cognition in DSS is studied via a proposed cognitive cycle to emphasis upon “brain-empowered” communication. A concrete mapping between the phases of cognitive cycle and the phases of engineering design applied in DSS is also proposed thereby bridging the gap between the abstract and the practical implementation of cognitive techniques. The design of cognitive DSS relies on the system and channel modeling to well-assess the external environment. Hence, this chapter provides an extensive account of models of cognitive DSS for both fixed satellite services and land mobile scenarios. In light of these models, we also present an elaborate taxonomy of cognitive techniques in DSS. This chapter serves as an overview for DSS and applicability of cognition in DSS. It also throws light upon open problems for future research.