A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin

An Experiment on Bare-Metal BigData Provisioning

Many BigData customers use on-demand platforms in the cloud, where they can get a dedicated virtual cluster in a couple of minutes and pay only for the time they use. Increasingly, there is a demand for bare-metal bigdata solutions for applications that cannot tolerate the unpredictability and performance degradation of virtualized systems. Existing bare-metal solutions can introduce delays of 10s of minutes to provision a cluster by installing operating systems and applications on the local disks of servers. This has motivated recent research developing sophisticated mechanisms to optimize this installation. These approaches assume that using network mounted boot disks incur unacceptable run-time overhead. Our analysis suggest that while this assumption is true for application data, it is incorrect for operating systems and applications, and network mounting the boot disk and applications result in negligible run-time impact while leading to faster provisioning time.This research was supported in part by the MassTech Collaborative Research Matching Grant Program, NSF awards 1347525 and 1414119 and several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or

Cloud Manufacturing Model to Optimise Manufacturing Performance

Being predicted as the future of modern manufacturing, cloud-based manufacturing has drawn the attention of researchers in academia and industry. Researches are being done towards transforming every service in to cloud based service-oriented manufacturing mode in the manufacturing industry. There are many challenges that would arise when travelling towards this paradigm shift which is being addressed by researchers, but there are very few researches that concentrate on the elastic capability of cloud. Elastic capability makes this paradigm unique from all the other approaches or technologies. If elasticity is not achievable then the necessity of migrating to cloud is unnecessary. So, it is imperative to identify if at all it is necessary to adopt cloud-based manufacturing mode and discuss the issues and challenges that would arise to achieve elasticity when shifting to this emerging manufacturing paradigm. This research explores the importance of adopting cloud-based manufacturing mode to improve manufacturing performance based on the competitive priorities such as cost, quality, delivery and flexibility and proposes an elasticity assessment tool to be included in the cloud-based manufacturing model for the users to assess the challenges and issues on the realisation of elasticity on the context of manufacturing, which is the novelty of this research. The contribution to knowledge is a clear understanding of the necessity of cloud based elastic manufacturing model in the manufacturing environment for the manufacturing SMEs to gain a competitive advantage by achieving the competitive priorities such as low-cost, high-quality, and on-time delivery. Finally, the research suggests the best combination of manufacturing parameters that has to be emphasised to improve the manufacturing performance and gain a competitive advantage

The Making of Cloud Applications An Empirical Study on Software Development for the Cloud

Cloud computing is gaining more and more traction as a deployment and provisioning model for software. While a large body of research already covers how to optimally operate a cloud system, we still lack insights into how professional software engineers actually use clouds, and how the cloud impacts development practices. This paper reports on the first systematic study on how software developers build applications in the cloud. We conducted a mixed-method study, consisting of qualitative interviews of 25 professional developers and a quantitative survey with 294 responses. Our results show that adopting the cloud has a profound impact throughout the software development process, as well as on how developers utilize tools and data in their daily work. Among other things, we found that (1) developers need better means to anticipate runtime problems and rigorously define metrics for improved fault localization and (2) the cloud offers an abundance of operational data, however, developers still often rely on their experience and intuition rather than utilizing metrics. From our findings, we extracted a set of guidelines for cloud development and identified challenges for researchers and tool vendors