Sample processing of Abstract
Mobile cloud computing for computation offloading: Issues and challenges
By Akherfi, K., Gerndt, M. & Harroud, H. (2016, December 18). Applied Computing and Informatics . Retrieved November 22, 2017 from https://doi.org/10.1016/j.aci.2016.11.002Get rights and content
Task 1. Read the following definitions of computer terms; give their Russian equivalents.
1. cloud computing – internet-based computing in which large groups of remote servers are networked so as to allow sharing of data-processing tasks, centralized data storage, and online access to computer services or resources.
2. IT – information technology
3. MCC (mobile cloud computing) – cloud computing in combination with mobile devices.
4. ABI – Allied Business Intelligence
5. SMDs – short for Switched Multimegabit Data Services, a high-speed switched data communications service offered by telephone companies that enable organizations to connect geographically separate local-area networks (LANs) into a single wide-area network (WAN).
6. ASM – abstract state machines, based on the concept of an abstract state machine in computer science.
7. computation offloading – in computer science, computation offloading refers to the transfer of certain computing tasks to an external platform, such as a cluster, grid, or a cloud. Offloading may be necessary due to hardware limitations of a computer system handling a particular task on its own. These intensive computing tasks may be used in artificial intelligence, artificial vision and object tracking, or computational decision making.
8. turnaround time (TAT) – the time interval from the time of submission of a process to the time of the completion of the process. It can also be considered as the sum of the time periods spent waiting to get into memory or ready queue, execution on CPU and executing input/output. Turnaround time is an important metric in evaluating the scheduling algorithms of an operating system.
9. grid computing – the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
10. virtual server – a server that shares hardware and software resources with other operating systems (OS), versus dedicated servers. Because they are cost-effective and provide faster resource control, virtual servers are popular in Web hosting environments.
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The main goal of Cloud computing (CC) is to allow IT departments to focus on their businesses and projects instead of just taking care of their data centers and keeping them working. CC is a new concept that aims to provide computational resources as services in a quick manner, on demand, and paying as per usage. CC offers to users and business three main advantages: (1) enormous computing resources available on demand, (2) payment for use as needed and on a short-term basis (storage by the day and release them as needed), and (3) simplified IT management and maintenance capabilities. CC provides clients with different applications as services via the Internet.
Recently, user preferences for computing have changed because of the latest developments and enhancements in mobile computing technologies. Several reports and studies have presented the importance of MCC and its impact on mobile clients and enterprises. For instance, and according to a recent study by ABI Research, more than 240 million business will use cloud services through mobile devices by 2015 and this will push the revenue of the MCC to $5.2 billion. Moreover, the usage of smartphones has increased rapidly in various domains, including enterprise, management of information systems, gaming, e-learning, entertainment and health care.
Although the predictions that mobile devices will be dominating the future computing devices, mobile devices along with their applications are still restricted by some limitations such as the battery life, processor potential, and the memory capacity of the SMDs. Nowadays, modern mobile devices have sufficient resources such as fast processors, large memory, and sharp screens. However, it is still not enough to help with computing intensive tasks such as natural language processing, image recognition, and decision-making. Mobile devices provide less computational power comparing to server computers or regular desktops and computation-intensive tasks put heavy loads on battery power.
Currently, there are several works and research in CC that aim at enhancing the computing capabilities of resource-constrained mobile client devices by providing mobile clients access to cloud infrastructures, software, and computing services. For example, Amazon web services are used to protect and save clients' personal data via their Simple Storage Service (S3). In addition, there are several frameworks that allow to process data intensive tasks remotely on cloud servers. For instance, the ASM computation offloading framework showed that computation offloading helped to reduce the energy consumption cost of mobile devices by 33%, and the turnaround time of the application by 45%. These services allow the user to use virtualized resources in cloud data centers. Computational clouds implement a variety of service models in order to use them in different computing visions.
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MCC can be seen as a bridge that fills the gap between the limited computing resources of SMDs and processing requirements of intensive applications on SMDs. The Mobile Cloud Computing Forum defines MCC as follows: ‘‘Mobile Cloud Computing at its simplest form refers to an infrastructure where both the data storage and the data processing happen outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just smartphone users but a much broader range of mobile subscribers”. MCC has attracted the attention of business people as a beneficial and useful business solution that minimizes the development and execution costs of mobile applications, allowing mobile users to acquire latest technology conveniently on an on-demand basis.
Computation offloading is the task of sending computation intensive application components to a remote server. Recently, a number of computation offloading frameworks have been proposed with several approaches for applications on mobile devices. These applications are partitioned at different granularity levels and the components are sent (offloaded) to remote servers for remote execution in order to extend and enhance the SMD’s capabilities.
However there are issues and challenges in computation offloading for MCC. One of the challenges in the current computation offloading frameworks is the diversity and heterogeneity of smartphone architectures and operating systems. A standardized offloading framework for different smartphone platforms is still a challenging issue in the MCC field.
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Security of data transmission is an important concern in cloud based application processing. Security and privacy are two crucial concepts that need to be maintained during the offloading process. These concepts can be addressed from different angles: (1) Mobile device, (2) cloud data centers, and (3) during data transmission over the network.
In MCC, mobility is one of the most important attributes of SMDs. This is because freedom of movement and autonomy of communication during the consumption of mobile cloud services, are crucial criteria for users’ satisfaction. However, there are some constraints that prevent the achievement of seamless connectivity and uninterrupted access to cloud services while on the move. As mobile users move, data exchange rates and network bandwidth may vary. Moreover, users may lose their connection while sending or receiving data; therefore, offloading approaches should be provided with suitable fault-tolerant strategies in order to resend the lost components, minimize the response time, and reduce the energy consumption of mobile devices.
Thus we notice that current offloading frameworks are still facing some challenges and difficulties. It is important to come up with a lightweight paradigm or model that will help to overcome the difficulties and minimizing efforts while developing, deploying, and managing an offloading framework. We believe that exploring other alternatives, such as introducing a middleware based architecture using an optimizing offloading algorithm, could help better the available frameworks and provide more efficient and more flexible solutions to the MCC users.
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