Every Cloud Has a Push Data Lining: Incorporating Cloud Services in a Context-Aware Application

We investigated context-awareness by utilising multiple sources of context in a mobile device setting. In our experiment we developed a system consisting of a mobile client, running on the Android platform, integrated with a cloud-based service. These components were integrated using pushmessaging technology.One of the key featureswas the automatic adaptation of smartphones in accordance with implicit user needs. The novelty of our approach consists in the use of multiple sources of context input to the system, which included the use of calendar data and web based user configuration tool, as well as that of an external, cloud-based, configuration file storing user interface preferences which, pushed at log-on time irrespective of access device, frees the user from having to manually configure its interface.The systemwas evaluated via two rounds of user evaluations (n = 50 users), the feedback of which was generally positive and demonstrated the viability of using cloud-based services to provide an enhanced context-aware user experience

Data location aware scheduling for virtual Hadoop cluster deployment on private cloud computing environment

With the advancements of Internet-of-Things (IoT) and Machine-to-Machine Communications (M2M), the ability to generate massive amount of streaming data from sensory devices in distributed environment is inevitable. A common practice nowadays is to process these data in a high-performance computing infrastructure, such as cloud. Cloud platform has the ability to deploy Hadoop ecosystem on virtual clusters. In cloud configuration with different geographical regions, virtual machines (VMs) that are part of virtual cluster are placed randomly. Prior to processing, data have to be transferred to the regional sites with VMs for data locality purposes. In this paper, a provisioning strategy with data-location aware deployment for virtual cluster will be proposed, as to localize and provision the cluster near to the storage. The proposed mechanism reduces the network distance between virtual cluster and storage, resulting in reduced job completion times

On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Context: Software has become more complicated, dynamic, and asynchronous than ever, making testing more challenging. With the increasing interest in the development of cloud computing, and increasing demand for cloud-based services, it has become essential to systematically review the research in the area of software testing in the context of cloud environments. Objective: The purpose of this systematic mapping study is to provide an overview of the empirical research in the area of software cloud-based testing, in order to build a classification scheme. We investigate functional and non-functional testing methods, the application of these methods, and the purpose of testing using these methods. Method: We searched for electronically available papers in order to find relevant literature and to extract and analyze data about the methods used. Result: We identified 69 primary studies reported in 75 research papers published in academic journals, conferences, and edited books. Conclusion: We found that only a minority of the studies combine rigorous statistical analysis with quantitative results. The majority of the considered studies present early results, using a single experiment to evaluate their proposed solution

Demonstrating 100 Gbps in and out of the public Clouds

There is increased awareness and recognition that public Cloud providers do provide capabilities not found elsewhere, with elasticity being a major driver. The value of elastic scaling is however tightly coupled to the capabilities of the networks that connect all involved resources, both in the public Clouds and at the various research institutions. This paper presents results of measurements involving file transfers inside public Cloud providers, fetching data from on-prem resources into public Cloud instances and fetching data from public Cloud storage into on-prem nodes. The networking of the three major Cloud providers, namely Amazon Web Services, Microsoft Azure and the Google Cloud Platform, has been benchmarked. The on-prem nodes were managed by either the Pacific Research Platform or located at the University of Wisconsin - Madison. The observed sustained throughput was of the order of 100 Gbps in all the tests moving data in and out of the public Clouds and throughput reaching into the Tbps range for data movements inside the public Cloud providers themselves. All the tests used HTTP as the transfer protocol.Comment: 4 pages, 6 figures, 3 table