Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

A survey of mobile cloud computing: architecture, applications, and approaches

The Grid 2: Blueprint for a New Computing Infrastructure

One of the main reasons why cloud computing has gained so much popularity is due to its ease of use and its ability to scale computing resources on demand. As a result, users can now rent computing nodes on large commercial clusters through several vendors, such as Amazon and rackspace. However, despite the attention paid by Cloud providers, performance unpredictability is a major issue in Cloud computing for (1) database researchers performing wall clock experiments, and (2) database applications providing service-level agreements. In this paper, we carry out a study of the performance variance of the most widely used Cloud infrastructure (Amazon EC2) from different perspectives. We use established microbenchmarks to measure performance variance in CPU, I/O, and network. And, we use a multi-node MapReduce application to quantify the impact on real dataintensive applications. We collected data for an entire month and compare it with the results obtained on a local cluster. Our results show that EC2 performance varies a lot and often falls into two bands having a large performance gap in-between --- which is somewhat surprising. We observe in our experiments that these two bands correspond to the different virtual system types provided by Amazon. Moreover, we analyze results considering different availability zones, points in time, and locations. This analysis indicates that, among others, the choice of availability zone also influences the performance variability. A major conclusion of our work is that the variance on EC2 is currently so high that wall clock experiments may only be performed with considerable care. To this end, we provide some hints to users.

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Runtime measurements in the cloud: observing, analyzing, and reducing variance

A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency per node, and simple abstraction. This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then introduce its optimization strategies reported in the recent literature. We also discuss the open issues and challenges raised on parallel data analysis with MapReduce.

https://www.cs.arizona.edu/~bkmoon/papers/sigmodrec11.pdf

Parallel data processing with MapReduce: a survey

Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed computing dynamic environments for end-users. This paper reviews recent advances of Cloud computing, identifies the concepts and characters of scientific Clouds, and finally presents an example of scientific Cloud for data centers

Scientific Cloud Computing: Early Definition and Experience

This paper proposes and evaluates an approach to the parallelization, deployment and management of bioinformatics applications that integrates several emerging technologies for distributed computing. The proposed approach uses the MapReduce paradigm to parallelize tools and manage their execution, machine virtualization to encapsulate their execution environments and commonly used data sets into flexibly deployable virtual machines, and network virtualization to connect resources behind firewalls/NATs while preserving the necessary performance and the communication environment. An implementation of this approach is described and used to demonstrate and evaluate the proposed approach. The implementation integrates Hadoop, Virtual Workspaces, and ViNe as the MapReduce, virtual machine and virtual network technologies, respectively, to deploy the commonly used bioinformatics tool NCBI BLAST on a WAN-based test bed consisting of clusters at two distinct locations, the University of Florida and the University of Chicago. This WAN-based implementation, called CloudBLAST, was evaluated against both non-virtualized and LAN-based implementations in order to assess the overheads of machine and network virtualization, which were shown to be insignificant. To compare the proposed approach against an MPI-based solution, CloudBLAST performance was experimentally contrasted against the publicly available mpiBLAST on the same WAN-based test bed. Both versions demonstrated performance gains as the number of available processors increased, with CloudBLAST delivering speedups of 57 against 52.4 of MPI version, when 64 processors on 2 sites were used. The results encourage the use of the proposed approach for the execution of large-scale bioinformatics applications on emerging distributed environments that provide access to computing resources as a service.

http://salsahpc.indiana.edu/csci-b649-2011/CloudBlast.pdf

CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications

The Science Clouds provide EC2-style cycles to scientific projects. This document contains a description of technologies enabling this project and an early summary of its experiences.

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Science Clouds: Early Experiences in Cloud Computing for Scientific Applications

Infrastructure-as-a-service (IaaS) cloud computing is revolutionizing how we approach computing. Compute resource consumers can eliminate the expense inherent in acquiring, managing, and operating IT infrastructure and instead lease resources on a pay-as-you-go basis. IT infrastructure providers can exploit economies of scale to mitigate the cost of buying and operating resources and avoid the complexity required to manage multiple customer-specific environments and applications. The authors describe the context in which cloud computing arose, discuss its current strengths and shortcomings, and point to an emerging computing pattern it enables that they call sky computing.

Sky Computing

This paper describes the architecture of the first implementation of the In-VIGO grid-computing system. The architecture is designed to support computational tools for engineering and science research In Virtual Information Grid Organizations (as opposed to in vivo or in vitro experimental research). A novel aspect of In-VIGO is the extensive use of virtualization technology, emerging standards for grid-computing and other Internet middleware. In the context of In-VIGO, virtualization denotes the ability of resources to support multiplexing, manifolding and polymorphism (i.e. to simultaneously appear as multiple resources with possibly different functionalities). Virtualization technologies are available or emerging for all the resources needed to construct virtual grids which would ideally inherit the above mentioned properties. In particular, these technologies enable the creation of dynamic pools of virtual resources that can be aggregated on-demand for application-specific user-specific grid-computing. This change in paradigm from building grids out of physical resources to constructing virtual grids has many advantages but also requires new thinking on how to architect, manage and optimize the necessary middleware. This paper reviews the motivation for In-VIGO approach, discusses the technologies used, describes an early architecture for In-VIGO that represents a first step towards the end goal of building virtual information grids, and reports on first experiences with the In-VIGO software under development.

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From virtualized resources to virtual computing grids: the In-VIGO system

This paper describes a virtual networking approach for grids called ViNe. It enables symmetric connectivity among grid resources and allows existing applications to run unmodified. Novel features of the ViNe architecture include: easy virtual networking administration; support for physical private networks and support for multiple independent virtual networks in the same infrastructure. The requirements of an application-friendly virtual network environment are presented and it is shown how the proposed solution meets them. Qualitative arguments are provided to justify all design decisions. Also presented is an experimental evaluation of the round-trip latencies and bandwidths achieved by a reference implementation. Measurements are reported for WAN-scenarios involving three different institutions. Under favorable conditions, ViNe bandwidths are within 90 to 100% of the available physical network bandwidth.

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Mauricio Tsugawa

Papers

CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications

Science Clouds: Early Experiences in Cloud Computing for Scientific Applications

Sky Computing

From virtualized resources to virtual computing grids: the In-VIGO system

A virtual network (ViNe) architecture for grid computing