Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userInvestigating Key User Experiencing Engineering Aspects in Software-as-a-Service Service Delivery Model
78
0
0.0
(
0
)
Added by
Yupeng Jiang
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Software as a Service (SaaS) is well established as an effective model for the development, deployment and customization of software. As it continues to gain more momentum in the IT industry, many user experience challenges and issues are being reported by the experts and end users.
rate research
Read More
This study presents an innovative solution for powering electric vehicles, named Charging-as-a-Service (CaaS), that concerns the potential large-scale adoption of light-duty electric vehicles (LDEV) in the Mobility-as-a-Service (MaaS) industry. Analogous to the MaaS, the core idea of the CaaS is to dispatch service vehicles (SVs) that carry modular battery units (MBUs) to provide LDEVs for mobility service with on-demand battery delivery. The CaaS system is expected to tackle major bottlenecks of a large-scale LDEV adoption in the MaaS industry due to the lack of charging infrastructure and excess waiting and charging time. A hybrid agent-based simulation model (HABM) is developed to model the dynamics of the CaaS system with SV agents, and a trip-based stationary charging probability distribution is introduced to simulate the generation of charging demand for LDEVs. Two dispatching algorithms are further developed to support the optimal operation of the CaaS. The model is validated by assuming electrifying all 13,000 yellow taxis in New York City (NYC) that follow the same daily trip patterns. Multiple scenarios are analyzed under various SV fleet sizes and dispatching strategies. The results suggest that optimal deployment of 250 SVs may serve the LDEV fleet in NYC with an average waiting time of 5 minutes, save the travel distance at over 50 miles per minute, and gain considerable profits of up to $50 per minute. This study offers significant insights into the feasibility, service efficiency, and financial sustainability for deploying city-wide CaaS systems to power the electric MaaS industry.
It is widely acknowledged that the forthcoming 5G architecture will be highly heterogeneous and deployed with a high degree of density. These changes over the current 4G bring many challenges on how to achieve an efficient operation from the network management perspective. In this article, we introduce a revolutionary vision of the future 5G wireless networks, in which the network is no longer limited by hardware or even software. Specifically, by the idea of virtualizing the wireless networks, which has recently gained increasing attention, we introduce the Everything-as-a-Service (XaaS) taxonomy to light the way towards designing the service-oriented wireless networks. The concepts, challenges along with the research opportunities for realizing XaaS in wireless networks are overviewed and discussed.
Function-as-a-Service (FaaS) platforms and serverless cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. Performant cache consistency emerges as a key challenge in this architecture. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.
Existing bare-metal cloud services that provide users with physical nodes have a number of serious disadvantage over their virtual alternatives, including slow provisioning times, difficulty for users to release nodes and then reuse them to handle changes in demand, and poor tolerance to failures. We introduce M2, a bare-metal cloud service that uses network-mounted boot drives to overcome these disadvantages. We describe the architecture and implementation of M2 and compare its agility, scalability, and performance to existing systems. We show that M2 can reduce provisioning time by over 50% while offering richer functionality, and comparable run-time performance with respect to tools that provision images into local disks. M2 is open source and available at https://github.com/CCI-MOC/ims.
Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is one of the important components in data science. However, due to privacy concerns, data should be enforced with strong privacy guarantees before sharing. Different privacy-preserving approaches were developed for privacy preserving data sharing; however, identifying the best privacy-preservation approach for the privacy-preservation of a certain dataset is still a challenge. Different parameters can influence the efficacy of the process, such as the characteristics of the input dataset, the strength of the privacy-preservation approach, and the expected level of utility of the resulting dataset (on the corresponding data mining application such as classification). This paper presents a framework named underline{P}rivacy underline{P}reservation underline{a}s underline{a} underline{S}ervice (PPaaS) to reduce this complexity. The proposed method employs selective privacy preservation via data perturbation and looks at different dynamics that can influence the quality of the privacy preservation of a dataset. PPaaS includes pools of data perturbation methods, and for each application and the input dataset, PPaaS selects the most suitable data perturbation approach after rigorous evaluation. It enhances the usability of privacy-preserving methods within its pool; it is a generic platform that can be used to sanitize big data in a granular, application-specific manner by employing a suitable combination of diverse privacy-preserving algorithms to provide a proper balance between privacy and utility.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
