Academic Cloud Computing Research: Five Pitfalls and Five Opportunities

This discussion paper argues that there are five fundamental pitfalls, which can restrict academics from conducting cloud computing research at the infrastructure level, which is currently where the vast majority of academic research lies. Instead academics should be conducting higher risk research, in order to gain understanding and open up entirely new areas. We call for a renewed mindset and argue that academic research should focus less upon physical infrastructure and embrace the abstractions provided by clouds through five opportunities: user driven research, new programming models, PaaS environments, and improved tools to support elasticity and large-scale debugging. The objective of this paper is to foster discussion, and to define a roadmap forward, which will allow academia to make longer-term impacts to the cloud computing community.Comment: Accepted and presented at the 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud'14

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

Policy-based SLA storage management model for distributed data storage services

There is  high demand for storage related services supporting scientists in their research activities. Those services are expected to provide not only capacity but also features allowing for more flexible and cost efficient usage. Such features include easy multiplatform data access, long term data retention, support for performance and cost differentiating of SLA restricted data access. The paper presents a policy-based SLA storage management model for distributed data storage services. The model allows for automated management of distributed data aimed at QoS provisioning with no strict resource reservation. The problem of providing  users with the required QoS requirements is complex, and therefore the model implements heuristic approach  for solving it. The corresponding system architecture, metrics and methods for SLA focused storage management are developed and tested in a real, nationwide environment

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

Lightweight Multilingual Software Analysis

Developer preferences, language capabilities and the persistence of older languages contribute to the trend that large software codebases are often multilingual, that is, written in more than one computer language. While developers can leverage monolingual software development tools to build software components, companies are faced with the problem of managing the resultant large, multilingual codebases to address issues with security, efficiency, and quality metrics. The key challenge is to address the opaque nature of the language interoperability interface: one language calling procedures in a second (which may call a third, or even back to the first), resulting in a potentially tangled, inefficient and insecure codebase. An architecture is proposed for lightweight static analysis of large multilingual codebases: the MLSA architecture. Its modular and table-oriented structure addresses the open-ended nature of multiple languages and language interoperability APIs. We focus here as an application on the construction of call-graphs that capture both inter-language and intra-language calls. The algorithms for extracting multilingual call-graphs from codebases are presented, and several examples of multilingual software engineering analysis are discussed. The state of the implementation and testing of MLSA is presented, and the implications for future work are discussed.Comment: 15 page

Characterizing Issue Management in Runtime Systems

Modern programming languages like Java require runtime systems to support the implementation and deployment of software applications in diverse computing platforms and operating systems. These runtime systems are normally developed in GitHub-hosted repositories based on close collaboration between large software companies (e.g., IBM, Microsoft) and OSS developers. However, despite their popularity and broad usage; to the best of our knowledge, these repositories have never been studied. We report an empirical study of around 118K issues from 34 runtime system repos in GitHub. We found that issues regarding enhancement, test failure and bug are mostly posted on runtime system repositories and solution related discussion are mostly present on issue discussion. 82.69% issues in the runtime system repositories have been resolved and 0.69% issues are ignored; median of issue close rate, ignore rate and addressing time in these repositories are 76.1%, 2.2% and 58 days respectively. 82.65% issues are tagged with labels while only 28.30% issues have designated assignees and 90.65% issues contain at least one comment; also presence of these features in an issue report can affect issue closure. Based on the findings, we offer six recommenda

Characterizing the impact of network latency on cloud-based applications’ performance

Businesses and individuals run increasing numbers of applications in the cloud. The performance of an application running in the cloud depends on the data center conditions and upon the resources committed to an application. Small network delays may lead to a significant performance degradation, which affects both the user’s cost and the service provider’s resource usage, power consumption and data center efficiency. In this work, we quantify the effect of network latency on several typical cloud workloads, varying in complexity and use cases. Our results show that different applications are affected by network latency to differing amounts. These insights into the effect of network latency on different applications have ramifications for workload placement and physical host sharing when trying to reach performance targets

Business Intelligence for Small and Middle-Sized Entreprises

Data warehouses are the core of decision support sys- tems, which nowadays are used by all kind of enter- prises in the entire world. Although many studies have been conducted on the need of decision support systems (DSSs) for small businesses, most of them adopt ex- isting solutions and approaches, which are appropriate for large-scaled enterprises, but are inadequate for small and middle-sized enterprises. Small enterprises require cheap, lightweight architec- tures and tools (hardware and software) providing on- line data analysis. In order to ensure these features, we review web-based business intelligence approaches. For real-time analysis, the traditional OLAP architecture is cumbersome and storage-costly; therefore, we also re- view in-memory processing. Consequently, this paper discusses the existing approa- ches and tools working in main memory and/or with web interfaces (including freeware tools), relevant for small and middle-sized enterprises in decision making