Genetic Programming for Dynamic Workflow Scheduling in Fog Computing

Computation offloading frameworks partition an application's execution between a cloud server and the mobile device to minimize its completion time on the mobile device. An important component of an offloading framework is the partitioning algorithm that decides which tasks to execute on mobile device or cloud server. The partitioning algorithm schedules tasks of a mobile application for execution either on mobile device or cloud server to minimize the application finish time. Most offloading frameworks partition parallel applications devices using an optimization solver which takes a lot of time. We show that by allowing duplicate execution of selected tasks on both the mobile device and the remote cloud server, a polynomial algorithm exists to determine a schedule that minimizes the completion time. We use simulation on both random data and traces to show the savings in both finish time and scheduling time over existing approaches. Our trace-driven simulation on benchmark applications shows that our algorithm reduces the scheduling time by 8 times compared to a standard optimization solver while guaranteeing minimum makespan.

The widespread use of smartphones and other mobile devices has brought significant changes to the storage requirements of cloud systems, necessitating a reevaluation of security protocols. Mobile Cloud Computing (MCC) is a computing paradigm that combines the capabilities of mobile devices with the flexibility and scalability of cloud computing. MCC allows mobile users to access computational power, storage, and applications over the internet, facilitating resource-intensive tasks such as data analysis, gaming, and multimedia streaming on resource-constrained mobile devices. The integration of mobile and cloud technologies has revolutionized industries such as healthcare, education, and entertainment, offering benefits like enhanced mobility, improved resource utilization, and cost efficiency. Despite its advantages, MCC faces significant security challenges that could undermine user trust and disrupt its adoption. Since mobile devices and cloud servers are connected via insecure networks, malicious actors can exploit vulnerabilities in the system. This paper aims to analyze the primary security issues in MCC technology, focusing on data protection, authentication, access control, and other critical concerns. Unlike other issues, when dealing with business objectives, protection and privacy in financial activities are of paramount importance. This article examines mobile cloud computing, its technological model, and its role in detail. Keywords: Mobile cloud computing, Technology, Privacy issues, Security.

Mobile cloud computing is high technology that extends existing IT capabilities and requirements. And it can also access to shareable remote computing resources pool through the network. As the concept of mobile cloud, many providers have served the mobile cloud services using their own service policies. In other words, there is no formal definition of quality criteria for mobile cloud service evaluation. To solve this problem, some quality models are proposed for cloud service evaluation. However, those did not include many metrics to evaluate the services. Even if the model included a number of criteria, it is difficult to identify whether the metrics are proper or not. Furthermore, most existing models were not concerned about mobile characteristics. Therefore, we propose a cloud service integration model to solve the problem as we mentioned above. First, we select additional metrics to satisfy the mobile characteristics. Second, we present an extended SLA model for modeling complex service-dependencies in mobile cloud services. Third, we describe a method of discovering relations between the metrics of service belonging to mobile cloud services and then using these relations for establishing newly generated SLA.

Providing mobile cloud services requires seamless integration between various platforms to offer mobile users optimum performance. To achieve this, many fundamental problems such as bandwidth availability and reliability, resource scarceness, and finite energy must be addressed before rolling out such services. This chapter aims to explore technological challenges for mobile cloud computing in the area of resource management focusing on both parts of the infrastructure: mobile devices and cloud networks. Starting with introducing mobile cloud computing, it then stresses the importance of resource management in the operation of mobile cloud services presenting various types of resources available for cloud computing. Furthermore, it examines the various types of resource management techniques available for mobile clouds. Finally, future directions in the field of resource management for mobile cloud computing environment are presented.

Mobile Cloud computing has shown its capability to support mobile devices for provisioning computing, storage and communication resources. Many existing research has proposed to offload computation tasks from mobile devices to clouds in order to reduce energy consumption, where the offloading service model is usually one-to-one. Due to the development of mobile sensing and location-based mobile cloud services, the cloud edge has been extended to the mobile devices and sensors. As a result, the one-to-one model is not sufficient to model the dynamic changes of mobile cloud-based services. Thus, a many-to-many (or multi-site) mobile cloud service composition is highly desired. In this research, MoSeC is presented to model the many-to-many mobile cloud service composition, where there are multiple surrogates, such as cloud computing nodes, mobile devices, or sensors, and their services (i.e., computation, storage, sensing, etc.) can be composed to fulfill functions required by a mobile service requestor. MoSeC takes into considerations the surrogates' changes due to their mobility and resource constraints. Moreover, MoSeC takes into considerations several service metrics for service mapping (or allocation) through a mobile cloud service topology reconfiguration process. A set of algorithms are presented to address the Service Topology Reconfiguration Problem (STRP) in several mobile cloud representative application scenarios, i.e., they are modeled as finite horizon scenarios, infinite horizon scenarios, and large state space scenarios to represent ad hoc, long-term, and large-scale mobile cloud service composition scenarios, respectively.

Mobile edge computing (MEC) is an emerging computing and communication model that brings extremely resourceful servers at a network's edge. This reduces the latency associated with cloud computing as applications executing on mobile devices do not always need to communicate with a cloud server because most of the functionalities provided by the cloud server are available on an edge server. Moreover, with the emergence of 5G really high amount of bandwidth is available to a mobile device, hence communication latency between the edge server and a mobile device is low. The combination of 5G and edge computing has a potential to better enable many interesting time-critical and/or computationally expensive computing and communication use cases, for example, connected cars. The stated combination enables development of computationally expensive applications for mobile devices and sensors nodes as these devices can offload their computational tasks to the edge server. Task offloading is a promising feature of edge computing, therefore here priority-class-based methods are presented to allocated CPU cycles to an offloaded task at the edge server. In one of the presented methods, a mobile device or a senor node assign their task to one of the available priority classes, and then offload the task to the edge server. Afterwards, the edge server assigns CPU cycles to such a task depending upon its priority class. Similarly, another method is presented that assigns priority to each device based on the type of tasks the device is offloading. While assigning CPU cycles to different offloaded tasks, the edge server uses their device priority. The simulation results demonstrate that the presented methods are capable of providing service differentiation.

Compromise-resilient anonymous mutual authentication scheme for n by m-times ubiquitous mobile cloud computing services

MCC, or mobile cloud computing, is a new field. Mobile cloud computing is becoming a crucial component for mobile devices due to reliability and portability as data processing and storage take place outside of the mobile due to the widespread use of mobile devices and the variety of apps. It helps mobile devices conserve battery life and processing power, which is important for highpower mobile devices. Mobile cloud computing offers consumers to make calculations and use cloud services on their mobile devices. It is essential to gain a full understanding of current models and emerging trends because mobile Cloud computing is still in its infancy. The purpose of this survey is to identify and describe the main risks and obstacles associated with mobile cloud computing while alsoshowcasing emerging trends in this area.

Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

Recent trends in Cloud Computing have further stimulated the popularization of mobile device industry, creating a novel computing paradigm called Mobile Cloud Computing (MCC). MCC takes advantage of the powerful computation and storage capability of cloud servers by offloading heavy computing or storing tasks from mobile devices to cloud servers to keep a thin frontend on the mobile devices. Such benefit is important to MCC leveraging various sensors equipped in modern mobile devices. We explore the sensing capability of MCC and design an application framework that enables a class of exciting mobile applications to be developed in the sensing-oriented MCC environment. A critical issue in such an environment is accountability. We provide a comprehensive analysis of the accountability issues in this new computing context and show how the accountability function is integrated into the application framework.

PurposeCloud computing becomes mobile when a mobile device tries to access the shared pool of computing resources provided by the cloud, on demand. Mobile applications may enrich their functionality by delegating heavy tasks to the clouds as the remote processing and storage have become possible by adding asynchronous behavior in the communication. However, developing mobile cloud applications involves working with services and APIs from different cloud vendors, which mostly are not interoperable across clouds. Moreover, by adding asynchronicity, mobile applications must rely on push mechanisms which are considered to be moderately reliable, and thus not recommended in scenarios that require high scalability and quality of service (QoS). To counter these problems, and the purpose of this paper, is to design a middleware framework, Mobile Cloud Middleware (MCM), which handles the interoperability issues and eases the use of process‐intensive services from smartphones by extending the concept of mobile host.Design/methodology/approachMCM is developed as an intermediary between the mobile and the cloud, which hides the complexity of dealing with multiple cloud services from mobiles. Several applications are presented to show the benefits of mobiles going cloud‐aware. Moreover, to verify the scalability of MCM, load tests are performed on the hybrid cloud resources using well known load balancing mechanisms like HAProxy and Tsung.FindingsFrom the study it was found that it is possible to handle hybrid cloud services from mobiles by using MCM. The analysis demonstrated that the MCM shows reasonable performance levels of interaction with the user, thus validating the proof of concept. Moreover, MCM decreases the effort in developing mobile cloud applications and helps in keeping soft‐real time responses by using its asynchronous approach.Originality/valueMCM fosters the utilization of different types of cloud services rather than the traditional mobile cloud services based on data synchronization. By offloading heavy tasks to the clouds, the framework extends the processing power and storage space capabilities of the constrained smart phones. The applications mentioned in the paper bring an added value by being success stories for mobile cloud computing domain in general.

Smart computational offloading for mobile edge computing in next-generation Internet of Things networks

Quality of cloud service (QoS) is one of the crucial factors for the success of cloud providers in mobile cloud computing. Context-awareness is a popular method for automatic awareness of the mobile environment and choosing the most suitable cloud provider. Lack of context information may harm the users’ confidence in the application rendering it useless. Thus, mobile devices need to be constantly aware of the environment and to test the performance of each cloud provider, which is inefficient and wastes energy. Crowdsourcing is a considerable technology to discover and select cloud services in order to provide intelligent, efficient, and stable discovering of services for mobile users based on group choice. This article introduces a crowdsourcing-based QoS supported mobile cloud service framework that fulfills mobile users’ satisfaction by sensing their context information and providing appropriate services to each of the users. Based on user's activity context, social context, service context, and device context, our framework dynamically adapts cloud service for the requests in different kinds of scenarios. The context-awareness based management approach efficiency achieves a reliable cloud service supported platform to supply the Quality of Service on mobile device.

In today`s world of science & technology mobile device especially Smartphones plays the role of “"Jinn" who fulfill demand of the user. Smartphone through its Internet facility allows users to access various internet based web services ubiquitously to fulfill his/her demand. Web Services can be of two types SOAP or Restful services. Many researchers have mentioned that SOAP based services are heavy to consume on mobile device despite the fact that most of the companies still uses SOAP services but answer to this challenge is Restful Web Services. Restful services also surfers from many problems like processing of work or accessing services on device due to mobile phone`s small screen size, low processing power, less memory storage capacity etc. That`s why the heavy processing work is shifted on Cloud and only client part will be on mobile device, this integration is called as Mobile Cloud Computing (MCC). In MCC important point need to consider is to improve the performance of web services in terms of speed, memory usage and reducing latency. To resolve these issues, this paper has proposed MCC architecture. We have divided this paper is into two parts, in the first part; we did comparative analysis of Web Service consumption on mobile devices using cloud and various parameters used. In second part we have proposed architecture for mobile Web Services in Cloud which compress response data using Gzip and use of web caching to reduce overhead of repeated or previously accessed Web Service.

Mobile edge computing (MEC) is a recent communication paradigm developed mainly for cellular networks. MEC is introduced to improve the whole network efficiency by offloading its operations to nearby clouds. Cellular networks are able to offer the cloud computing capabilities at the edge of the radio access network through MEC servers. Mobiles services and tasks can either be executed at the mobile device or offloaded to the edge server. In this work, we provide a latency aware and energy aware offloading algorithm for the 5G multilevel edge computing based cellular system. The algorithm enables the mobile device to request offloading or decide the local execution independently based on the available resources at the mobile device and edge server. The algorithm takes into consideration the energy consumption to handle the service and make the offloading decision that achieves higher energy performance. The system is simulated and numerical results are included for performance evaluation.