Mobile cloud computing for computation offloading: Issues and challenges

International audienceDespite the evolution and enhancements that mobile devices have experienced, they are still considered as limited computing devices. Today, users become more demanding and expect to execute computational intensive applications on their smartphone devices. Therefore, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing (CC) in order to extend capabilities of mobile devices using offloading techniques. Computation offloading tackles limitations of Smart Mobile Devices (SMDs) such as limited battery lifetime, limited processing capabilities , and limited storage capacity by offloading the execution and workload to other rich systems with better performance and resources. This paper presents the current offloading frameworks, computation offloading techniques, and analyzes them along with their main critical issues. In addition , it explores different important parameters based on which the frameworks are implemented such as offloading method and level of partitioning. Finally, it summarizes the issues in offloading frameworks in the MCC domain that requires further research

Uma proposta para o uso de progressive web apps em ambientes de computação móvel em nuvens

The increased use of mobile applications is directly associated with the constant development of Mobile Computing (CM), such as smartphones, tablets and notebooks. However, the use of applications on these devices turns out to be a problem for their processing limitations, RAM, storage, network connection and especially battery. To mitigate these limitations, an area known as Mobile Cloud Computing (MCC) has presented some alternatives. One of these is Computing Offloading, which consists of sending all or part of the execution of processes or tasks from the mobile device to a remote computing resource with higher processing power. However, the efficiency of this discharge from running in MCC environment can be directly related to the type of application used by the mobile device. Therefore, the objective of this work is to propose a methodology for the use of Progressive Web Applications or Progressive Web Apps (PWA) to offload tasks in Mobile Cloud Computing. Based on the proposed methodology, two case studies involving face detection and Fibonacci sequence calculation were developed. These case studies were evaluated by performing different workloads using the PWA application on different mobile devices and offloading in Docker containers using the 3G, 4G and WiFi networks. The results of the evaluation were obtained from the task execution time metric, noting that in some cases local execution of these tasks is better than cloud execution; while in others, cloud is a better processing option. The developed PWA application was further analyzed using the LightHouse PWA inspection tool, obtaining a maximum score of 100 for the application performance.O aumento no uso de aplicações móveis está diretamente associado ao desenvolvimento constante da Computação Móvel (CM), a exemplo dos smartphones, tablets e notebooks. No entanto, o uso de aplicações nesses dispositivos acaba sendo um problema para suas limitações de processamento, memória RAM, armazenamento, conexão de rede e, principalmente, bateria. Para mitigar essas limitações, uma área conhecida como Computação Móvel em Nuvem ou Mobile Cloud Computing – MCC tem apresentado algumas alternativas. Uma delas está na descarga computacional ou Computing Offloading, que consiste em fazer o envio completo ou parcial da execução de processos ou tarefas do dispositivo móvel para um recurso computacional remoto com maior poder de processamento. No entanto, a eficiência dessa descarga da execução em ambiente MCC pode ter relação direta com o tipo de aplicação utilizada pelo dispositivo móvel. Diante disso, o objetivo deste trabalho consiste em propor uma metodologia para o uso de Aplicações Web Progressivas ou Progressive Web Apps (PWA) para fazer o offloading de tarefas na Computação Móvel em Nuvem. A partir da metodologia proposta, foram desenvolvidos dois estudos de caso, envolvendo a detecção de faces e o cálculo da sequência Fibonacci. A avaliação desses estudos de caso foi feita através da execução de diferentes cargas de trabalho, usando a aplicação PWA em diferentes dispositivos móveis e através do offloading em containers Docker, utilizando as redes 3G, 4G e WiFi. Os resultados da avaliação foram obtidos a partir da métrica de tempo de execução das tarefas, observando-se que, em alguns casos, a execução local dessas tarefas é melhor que a execução na nuvem; enquanto em outros, a nuvem é uma melhor opção de processamento. A aplicação PWA desenvolvida ainda foi analisada utilizando a ferramenta de inspeção de PWA LightHouse, obtendo a nota máxima de 100 para o quesito performance da aplicação

A Comprehensive Review on Adaptability of Network Forensics Frameworks for Mobile Cloud Computing

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC

A Comprehensive Review on Adaptability of Network Forensics Frameworks for Mobile Cloud Computing

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC