Satish Narayana Srirama

Adapting scientific computing problems to clouds using MapReduce

Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study this, we established a scientific computing cloud (SciCloud) project and environment on our internal clusters. The main goal of the project is to study the scope of establishing private clouds at the universities. With these clouds, students and researchers can efficiently use the already existing resources of university computer networks, in solving computationally intensive scientific, mathematical, and academic problems. However, to be able to run the scientific computing applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. This paper summarizes the challenges associated with reducing iterative algorithms to the MapReduce model. Algorithms used by scientific computing are divided into different classes by how they can be adapted to the MapReduce model; examples from each such class are reduced to the MapReduce model and their performance is measured and analyzed. The study mainly focuses on the Hadoop MapReduce framework but also compares it to an alternative MapReduce framework called Twister, which is specifically designed for iterative algorithms. The analysis shows that Hadoop MapReduce has significant trouble with iterative problems while it suits well for embarrassingly parallel problems, and that Twister can handle iterative problems much more efficiently. This work shows how to adapt algorithms from each class into the MapReduce model, what affects the efficiency and scalability of algorithms in each class and allows us to judge which framework is more efficient for each of them, by mapping the advantages and disadvantages of the two frameworks. This study is of significant importance for scientific computing as it often uses complex iterative methods to solve critical problems and adapting such methods to cloud computing frameworks is not a trivial task.

Mobile code offloading: from concept to practice and beyond

The emerging mobile cloud has expanded the horizon of application development and deployment with techniques such as code offloading. While offloading has been widely considered for saving energy and increasing responsiveness of mobile devices, the technique still faces many challenges pertaining to practical usage. In this article, we adopt a systemic approach for analyzing the components of a generic code offloading architecture. Based on theoretical and experimental analysis, we identify the key limitations for code offloading in practice and then propose solutions to mitigate these limitations. We develop a generic architecture to evaluate the proposed solutions. The results provide insights regarding the evolution and deployment of code offloading.

A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service

Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.

SciCloud: Scientific Computing on the Cloud

SciCloud is a project studying the scope of establishing private clouds at universities. With these clouds, researchers can efficiently use the already existing resources in solving computationally intensive scientific, mathematical, and academic problems. The project established a Eucalyptus based private cloud and developed several customized images that can be used in solving problems from mobile web services, distributed computing and bio-informatics domains. The poster demonstrates the SciCloud and reveals two applications that are benefiting from the setup along with our research scope and results in scientific computing.

A generic middleware framework for handling process intensive hybrid cloud services from mobiles

Mobile technologies are drawing their attention to the cloud computing due to the increasing demand of the applications, for processing power, storage space and energy. However, developing mobile cloud applications involves working with services and APIs from different cloud vendors. Most often these APIs are not interoperable and the information processed and stored into the cloud is non-transferable across clouds. To counter these problems, a generic middleware framework, Mobile Cloud Middleware (MCM) is designed, which handles the interoperability issues, and eases the use of process-intensive services from mobile phones. A prototype of MCM is developed and several applications are demonstrated in different domains. Moreover, to verify the scalability of MCM, load tests are performed on the hybrid cloud resources. The detailed performance analysis of the middleware framework shows that MCM improves the quality of service for mobiles and helps in maintaining soft-real time responses for mobile cloud applications.

Adaptive code offloading for mobile cloud applications: exploiting fuzzy sets and evidence-based learning

Mobile cloud computing is arising as a prominent domain that is seeking to bring the massive advantages of the cloud to the resource constrained smartphones, by following a delegation or offloading criteria. In a delegation model, a mobile device consumes services from multiple clouds by efficiently utilizing solutions like middleware. In the offloading model, a mobile application is partitioned and analyzed so that the most computational expensive operations at code level can be identified and offloaded for remote processing. While code offloading is studied extensively for the development of mobile cloud applications, much of the advantages of cloud computing are still left unexploited and poorly considered in these approaches. Cloud computing may introduce many other dynamic variables like performance metrics, parallelization of tasks, elasticity etc., to current code offloading models that could affect the overall offloading decision process. To address this, we propose a fuzzy decision engine for code offloading, that considers both mobile and cloud variables. The cloud parameters and rules are introduced asynchronously to the mobile, using notification services. The paper also proposes a strategy to enrich the offloading decision process with evidence-based learning methods, by exploiting cloud processing capabilities over code offloading traces.

Scalable Mobile Web Service Discovery in Peer to Peer Networks

Due to the astonishing development in memory and processing capabilities of hand held devices such as smart phones, it is not a dream anymore to enable mobile devices not only as conventional web service requesters but even as providers. The willingness and enthusiasm of service providers place abundant services at the disposal. But this abundance makes the efficiency of service discovery a critical issue. Centralized registries have severe drawbacks in such a scenario due to the dynamic and spontaneous nature of mobile peers. In the quest for a more appropriate approach for mobile web service discovery, we observed P2P to share very similar characteristics with behaviors of peers in mobile network. Hence we tried to find alternate mobile web service discovery mechanisms by using the features of the P2P networks like JXTA modules. The scalability analysis of the approach proves that the discovery can scale to the needs of large cellular networks.

Mobile Cloud Middleware

Abstract Mobile Cloud Computing (MCC) is arising as a prominent research area that is seeking to bring the massive advantages of the cloud to the constrained smartphones. Mobile devices are looking towards cloud-aware techniques, driven by their growing interest to provide ubiquitous PC-like functionality to mobile users. These functionalities mainly target at increasing storage and computational capabilities. Smartphones may integrate those functionalities from different cloud levels, in a service oriented manner within the mobile applications, so that a mobile task can be delegated by direct invocation of a service. However, developing these kind of mobile cloud applications requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, programmatically provisioning of resources (Web APIs) and cloud intercommunication. To overcome these issues, we have developed a Mobile Cloud Middleware (MCM) framework, which addresses the issues of interoperability across multiple clouds, asynchronous delegation of mobile tasks and dynamic allocation of cloud infrastructure. MCM also fosters the integration and orchestration of mobile tasks delegated with minimal data transfer. A prototype of MCM is developed and several applications are demonstrated in different domains. To verify the scalability of MCM, load tests are also performed on the hybrid cloud resources. The detailed performance analysis of the middleware framework shows that MCM improves the quality of service for mobiles and helps in maintaining soft-real time responses for mobile cloud applications.

Mobile hosts in enterprise service integration

Mobile data services in combination with profluent web services are seemingly the path-breaking domain in current information systems research. In the Mobile Web Service (MWS) sphere, resource-constrained mobile terminals are used as both web service clients and providers (mobile hosts). Mobile hosts enable seamless integration of user-specific services into the enterprise. This paper addresses several technical aspects of the MWS provisioning domain, such as providing proper Quality of Service (QoS), especially in terms of security and reasonable scalability, and discovery aspects of the huge number of services possible with each mobile host that provides some services. The paper also discusses the features, components and realisation details of our enterprise service-bus-technology-based integration framework, which ensures QoS and discovery of MWS and helps in providing a birds-eye view of the mobile enterprise.

Mobile web services mediation framework

Mobile data services in combination with profluent Web services are seemingly the path breaking domain in current information systems research. In mobile Web services sphere, resource constrained mobile terminals are used as both Web services clients and providers. While service delivery and management from Mobile Host are technically feasible, the ability to provide proper quality of service (QoS) and discovery mechanisms for the huge number of services possible with Mobile Hosts is observed to be very critical. We have studied the security, scalability and discovery aspects of the mobile Web services and the analysis has identified the necessity of a mediation framework. This paper summarizes our QoS and discovery research and discusses the realization details and features of our enterprise service bus technology based integration framework for mobile Web service provisioning.