A computation offloading scheme for performance enhancement of smart mobile devices for mobile cloud computing

The amount of smart phones has increased exponentially. Due to the periodic release of high-performance smart phones and upgraded operating system, new smart phones become out-dated over 1 or 2 years. In order to solve environmental constraints of these smart phones, virtualization technology using Thin-Client terminal has been developed. However, in the case of Virtual Machine(VM), the applications associated with sensors and a GPS device can not run because they are not included. In this paper, by implementing the device driver for Android running in a virtual machine in the x86-based systems, it is to provide Android virtualization capabilities such as using the latest smart phones in the virtual machine environment. It would like to propose a method that the virtual device driver receives sensors and GPS information from the old Android smart phones(Thin-Client) that actually work and run as if the real device exists.

Mobile cloud computing (MCC) allows smart mobile devices (SMD) to access the cloud resources in order to offload data from smartphones and to acquire computational services for application processing. A distinctive factor in accessing cloud resources is the communication link. However, the communication links between SMD and cloud resources are weak, which allows intruders to perform malicious activities by exploiting their vulnerabilities. This makes security a key challenge in the MCC environment. Several intrusion detection and response systems (IDRSs) are adapted to address the exploitation of vulnerabilities that affect smartphones, communication links between cloud resources and smartphones, as well as cloud resources. In this article, we discuss the cloud-based IDRS in the context of SMD and cloud resources in the MCC infrastructure. The stringent security requirements are provided as open issues along with possible solutions. The article aims at providing motivations for researchers, academicians, security administrators, and cloud service providers to discover mechanisms, frameworks, standards, and protocols to address the challenges faced by cloud-based IDRS for SMD.

Virtualization is a key technology for mobile cloud computing (MCC) and the virtual machine (VM) is a core component of virtualization. VM provides a relatively independent running environment for different applications. Therefore, the VM placement problem focuses on how to place VMs on optimal physical machines, which ensures efficient use of resources and the quality of service, etc. Most previous work focuses on energy consumption, network traffic between VMs and so on and rarely consider the delay for end users’ requests. In contrast, the latency between requests and VMs is considered in this paper for the scenario of optimal VM placement in MCC. In order to minimize average RTT for all requests, the round-trip time (RTT) is first used as the metric for the latency of requests. Based on our proposed RTT metric, an RTT-Aware VM placement algorithm is then proposed to minimize the average RTT. Furthermore, the case in which one of the core switches does not work is considered. A VM rescheduling algorithm is proposed to keep the average RTT lower and reduce the fluctuation of the average RTT. Finally, in the simulation study, our algorithm shows its advantage over existing methods, including random placement, the traffic-aware VM placement algorithm and the remaining utilization-aware algorithm.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

A wide range of applications, many of which need ever-increasing processing capability, may now be supported by smartphones. Smartphones are resource-constrained devices with limited computation power, memory, storage, and battery; therefore, this presents a hurdle. Fortunately, dynamic resources for processing, storage, and service supply are nearly endless thanks to cloud computing technologies. In order to get beyond the limitations of smartphones, experts foresee expanding cloud computing services to mobile devices. Cloud computing, software as a service, community networks, online stores, and other application models have grown rapidly over the past several years as a result of developments in network -based computing and applications on demand. Since 2007, the scientific and business sectors have turned to studying cloud computing, an important application model in the Internet era. Since mobile cloud computing is still in its infancy, it is essential to have a complete understanding of the technology in order to identify the course of future research.

In recent years, mobile devices such as smart phones, tablets empowered with tremendous technological advancements.Augmenting the computing capability to the distant cloud help us to envision a new computing era named as mobile cloud computing (MCC).However, distant cloud has several limitations such as communication delay and bandwidth which brings the idea of proximate cloud of cloudlet.Cloudlet has distinct advantages and is free from several limitations of distant cloud.However, limited resources of cloudlet negatively impact the cloudlet performance with the increasing number of substantial users.Hence, cloudlet is a viable solution to augment the mobile device task to the nearest small scale cloud known as cloudlet.However, this cloudlet resource is finite which in some point appear as resource scarcity problem.In this paper, we analyse the cloudlet resource scarcity problem on overall performance in the cloudlet for mobile cloud computing.In addition, for empirical analysis, we make some definitions, assumptions and research boundaries.Moreover, we experimentally examine the finite resource impact on cloudlet overall performance.By, empirical analysis, we explicitly establish the research gap and present cloudlet finite resource problem in mobile cloud computing.In this paper, we propose a Performance Enhancement Framework of Cloudlet (PEFC) which enhances the finite resource cloudlet performance.Our aim is to increase the cloudlet performance with this limited cloudlet resource and make the better user experience for the cloudlet user in mobile cloud computing.

Abstract—Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on-demand. It is an emerging but promising paradigm to integrating mobile devices into cloud computing, and the integration performs in the cloud based hierarchical multi-user data-shared environment. With integrating into cloud computing, security issues such as data confidentiality and user authority may arise in the mobile cloud computing system, and it is concerned as the main constraints to the developments of mobile cloud computing. In order to provide safe and secure operation, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three-layer structure is proposed in this paper. In a specific mobile cloud computing model, enormous data which may be from all kinds of mobile devices, such as smart phones, functioned phones and PDAs and so on can be controlled and monitored by the system, and the data can be sensitive to unauthorized third party and constraint to legal users as well. The novel scheme mainly focuses on the data processing, storing and accessing, which is designed to ensure the users with legal authorities to get corresponding classified data and to restrict illegal users and unauthorized legal users get access to the data, which makes it extremely suitable for the mobile cloud computing paradigms.

<p class="0abstract">Computing with mobile is still in its infancy due to its limitations of computational power, battery lifetime and storage capacity. These limitations hinder the growth of mobile computing, which in-turn affects the growth of computationally intensive applications developed for the mobile devices. So in-order to help execute complex applications within the mobile device, mobile cloud computing (MCC) emerged as a feasible solution. The job of offloading the task to the cloud data center for storage and execution from the mobile seems to gain popularity, however, issues related to network bandwidth, loss of mobile data connectivity, and connection setup does not augment well to extend the benefits offered by MCC. Cloudlet servers filled this gab by assisting the mobile cloud environment as an edge device, offering compute power to the connected devices with high speed wireless LAN connectivity. Implementation constraints of cloudlet faces severe challenges in-terms of its storage, network sharing, and VM provisioning. Moreover, the number of connected devices of the cloudlet and its load conditions vary drastically leading to unexpected bottleneck, in which case the availability to server becomes an issue. Therefore, a scalable cloudlet, Client Aware Scalable Cloudlet (CASC) is proposed with linear regression analysis, predicting the knowledge of expected load conditions for provisioning new virtual machines and to perform resource migration.</p>

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.

In the current era of technological advancement, mobile smart devices are being used extensively. As a result the requirements of these devices are also growing. With the help of Mobile Cloud Computing, these devices are able to communicate directly with the cloud and perform complex tasks which used to be very farfetched in the past. In such a scenario, the movement of mobile devices is the major research area. The challenge of maintaining a study link between these devices and the network is under extensive study. In this paper we propose a Advanced Q-MAC architecture for resource allocation of mobile devices. The proposed method improves the QoS of the system and higher efficiency and reliability. Experimental results show that the proposed method performs data offload better that the existing methods and has better results in terms of efficiency and process time.

Nevertheless, mobile devices harness resource poverty, which includes limited processing power, limited battery life, and limited memory storage, and this is still a considerable drawback to the provision of intensive applications, including 3D simulations and data analytics in real-time. The paper will suggest a strong three-tier hybrid architecture providing integration of mobile computing and cloud services to provide seamless delivery of education. The architecture consisted of a User Interface Tier of lightweight front-end interactions, a Middleware Tier of a Task Orchestrator and a Threshold-Based Elastic Scaling (TBES) algorithm, and a Data Tier of hybrid NoSQL storage for real-time synchronization. Created a mathematical model of energy-aware offloading to estimate the optimal point of offloading, depending on network latency and the CPU intensity locally. Simulation outcomes show that there are great performance increases in comparison with the traditional local-execution models. The TBES algorithm was able to keep the system response time at 185 ms, which is much lower than the 200 ms Rmax that is required, and even at the maximum verified load of 500 users. Furthermore, computation offloading extended mobile battery longevity by 73.3%, reducing power consumption from a 450-mW baseline to 120 mW. Bandwidth efficiency was also optimized through a differential synchronization protocol, which achieved an 88.5% reduction in data overhead. Correlation analysis using Kaggle education datasets further suggests that these technical enhancements contributed to a 22% increase in course completion rates and a 92% improvement in student engagement. These results confirm that the suggested architecture can efficiently isolate the quality of education on the one hand and the limitations of local hardware on the other, providing a solution that can be scaled to comprehensive, global e-learning.

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.

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

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.

Background: The integration of mobile computing and cloud computing has the potential to revolutionize healthcare delivery by providing ubiquitous access to resources and enabling new service development and delivery models. While cloud computing has been extensively studied in fields such as genomics and molecular medicine, its application in healthcare beyond these domains remains relatively unexplored. This scoping review aims to identify the current state and emerging research topics in cloud computing in healthcare outside of the traditional “OMICS context.” Methods: A comprehensive search was conducted in MEDLINE in July 2013 and December 2014 using keywords related to cloud computing and cloud-based services. The identified journal and conference articles were independently categorized and summarized by two researchers, who subsequently consolidated their findings. Results: A total of 102 publications were analyzed, revealing six main topics in cloud computing in healthcare: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Common features utilized in these applications included broad network access for data sharing and access, as well as rapid elasticity to meet computing demands. While some articles highlighted the cost-effectiveness of pay-per-use cloud services, only 14 articles reported successful implementations, with many publications focusing on conceptual or prototypic projects. Additionally, several articles equated cloud computing with internet-/web-based data sharing, failing to illustrate the unique benefits of the cloud computing paradigm. Conclusions: Although the integration of mobile computing and cloud computing in healthcare is gaining attention, successful implementations in the field are still limited. Many papers use the term “cloud” interchangeably with “virtual machines” or “web-based” without clearly demonstrating the advantages of the cloud paradigm. Data safety and security concerns associated with involving external cloud partners remain significant barriers to adoption in the healthcare domain. As of now, cloud computing is primarily favored for its individual features, such as elasticity, pay-per-use models, and broad network access, rather than as a comprehensive cloud computing paradigm.