A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Secure Data Transactions in Mobile Cloud Computing using FAAS

In recent times, security breaches have come to light in mobile cloud transactions, raising concerns about the vulnerability of data stored in mobile clouds. This data is at risk of tampering or unauthorized modification by external users, especially because it resides within a public cloud infrastructure managed by organizations. Such breaches can significantly impact the authenticity and integrity of the stored data. Mobile cloud computing (MCC) is a technology designed to facilitate the transfer of data and communication with end-users over the internet through a mobile cloud infrastructure. To address the urgent need to secure and protect data stored in mobile clouds, we propose the implementation of the Mobile Cloud-Security Model (MCSM). This innovative model is poised to provide an elevated level of data security and integrity for user data by harnessing the power of Federated Learning (FL) and Federation as a Service (FaaS). Federated Learning (FL) seamlessly integrates into the data training process, culminating in the generation of a model using the data hosted in the mobile cloud. This pioneering approach enables collaborative model training while steadfastly upholding data privacy and security. Federation as a Service (FaaS) represents a cloud-based solution that streamlines collaboration and data sharing among diverse organizations or entities. It simplifies the complex processes of configuring trust relationships, managing identities, and establishing data exchange agreements among federated entities, all made possible through the provision of Service Level Agreements (SLAs) for data stored in the mobile cloud. The user data stored in the mobile cloud will be retrieved using Machine Learning (ML) algorithms that learn from user data. Subsequently, this data is offloaded from the edge devices. The outcome of this research is to maintain user data within the FAAS cloud service with higher-level of confidentiality, security and integrity of user’s data

A combined computing framework for load balancing in multi-tenant cloud eco-system

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required

A user-centric execution environment for <em>CineGrid</em> workloads

The abundance and heterogeneity of IT resources available, together with the ability to dynamically scale applications poses significant usability issues to users. Without understanding the performance profile of available resources users are unable to efficiently scale their applications in order to meet performance objectives. High quality media collaborations, like CineGrid, are one example of such diverse environments where users can leverage dynamic infrastructures to move and process large amounts of data. This paper describes our user-centric approach to executing high quality media processing workloads over dynamic infrastructures. Our main contribution is the CGtoolkit environment, an integrated system which aids users cope with the infrastructure complexity and large data sets specific to the digital cinema domain