Cloud Computing For Mobile Users

Published Date: 02 Nov 2017

Cloud computing is a new paradigm in which computer resources such as memory, and storage are not physically present at the users location. Instead, a service provider owns and manages these resources and users access them via the Internet. For example, Amazon Web Services lets users store personal data via its Simple Storage Service (S3) and perform computations on stored data using the Elastic Compute Cloud (EC2).

This type of computing provides many advantages for businesses—including low initial capital investment, shorter start-up time for new services, lower maintenance and operation costs, higher utilization through virtual-ization, and easier disaster recovery—that make cloud computing an attractive option. Reports suggest that there are several benefits in shifting computing from the desktop to the cloud.1,2 What about cloud computing for mobile users? The primary constraints for mobile computing are limited energy and wireless bandwidth. Cloud computing can provide energy savings as a service to mobile users, though it also poses some unique challenges.

Mobile systems, such as smart phones, have become the primary computing platform for many users. Various studies have identified longer battery lifetime as the most desired feature of such systems. Many applications are too computation intensive to perform on a mobile system. If a mobile user wants to use such applications, the computation must be performed in the cloud. Other applications such as image retrieval, voice recognition, gaming, and navigation can run on a mobile system. However, they consume significant amounts of energy. Can offloading these applications to the cloud save energy and extend battery lifetimes for mobile users?

Low-power design has been an active research topic for many years. There are four basic approaches to saving energy and ex-tending battery lifetime in mobile devices:

Adopt a new generation of semiconductor technology..

Avoid wasting energy.

Execute programs slowly

Eliminate computation all together.

The paper focuses on the last approach for energy conservation.

OFFLOADING COMPUTATION TO SAVE ENERGY

Sending computation to another machine is not a new idea. The currently popular client-server computing model enables mobile users to launch Web browsers, search the Internet, and shop online. What distinguishes cloud computing from the existing model is the adoption of virtualization. Instead of service providers managing programs running on servers, virtualization allows cloud vendors to run arbitrary applications from different customers on virtual machines.

Cloud vendors thus provide computing cycles, and users can use these cycles to reduce the amounts of computation on mobile systems and save energy. Thus, cloud computing can save energy for mobile users through computation offloading. Virtualization, a fundamental feature in cloud computing, lets applications from different customers run on different virtual machines, thereby providing separation and protection.

ENERGY ANALYSIS FOR COMPUTATION OFFLOADING

Various cost/benefit studies focus on whether to offload computation to a server. The following example provides a simple analysis for this decision.

Suppose the computation requires C instructions. Let S and M be the speeds, in instructions per second, of the cloud server and the mobile system, respectively. The same task thus takes C/S seconds on the server and C/M seconds on the mobile system. If the server and mobile system ex­change D bytes of data and B is the network bandwidth, it takes D/B seconds to transmit and receive data. The mobile system consumes, in watts, Pc for computing, Pi while being idle, and Ptr for sending and receiving data. (Trans­mission power is generally higher than reception power, but for the purpose of this analysis, they are identical.)

If the mobile system performs the computation, the energy consumption is Pc × (C/M). If the server performs the computation, the energy consumption is [Pi × (C/S)] + [Ptr × (D/B)]. The amount of energy saved is

Pc×(C/M)−Pi×(C/S)−Ptr×(D/B). (1)

Suppose the server is F times faster—that is, S = F × M. We can rewrite the formula as

(C/M)×(Pc−(Pi/F))−Ptr×(D/B). (2)

CHALLENGES AND POSSIBLE SOLUTIONS

Does this make cloud computing the "ultimate" so­lution to the energy problem for mobile devices? Not quite. While cloud computing has tremendous potential to save energy, designers must consider several issues including privacy and security, reliability, and handling real-time data.

PRIVACY AND SECURITY

In cloud computing, Web applications and data replace traditional stand-alone programs, which are no longer stored in users’ computers. Shifting all data and computing resources to the cloud can have implications for privacy and security. Because the data is stored and managed in the cloud, security and privacy settings depend on the IT management the cloud provides.

A bug or security loophole in the cloud might result in a breach of privacy.

Cloud service providers typically work with many third-party vendors, and there is no guarantee as to how these vendors safeguard data.

Another potential privacy violation is the "tracking" of individuals through location-based navigation data offloaded to the cloud.

RELIABILITY

Another potential concern with mobile cloud computing is reliability. A mobile user performing computation in the cloud depends on the wireless network and cloud service. Dependence on the wire­less network implies that cloud computing may not even be possible, let alone energy efficient, when connectivity is limited. Mobile cloud com­puting is also difficult in locations such as the basement of a building, interior of a tunnel, a national park or subway. In these cases, where the value of B in Equation 2 can become very small or even zero, cloud computing does not save energy.

Dependence on the cloud for impor­tant computations could lead to problems during service outages. These can significantly reduce the value of F in Equation 2.

Data storage presents another reli­ability problem where in the cloud servers crash and all the users lose their data. One option in such scenarios involves an independent backup of data with an alternate service provider, which might increase the value of D in Equation 2.

REAL-TIME DATA

Some applications—including chess, searching newly captured images for content-based image retrieval, mobile sur­veillance, and context-aware navigation—have real-time data. In such scenarios, D in Equation 2 is no longer a pointer to the data; it refers to the actual data. For applications like chess, the value of D is small and hence offloading can still save energy. When the value of D is large, offloading may not save energy. In such cases, performing the computation on the mobile system may be more energy ef­ficient.

A possible solution is partitioning computation between the mobile system and the cloud to reduce energy consump­tion. Such a solution may include partially processing the real-time data on the mobile system. If the processed data are smaller in size, sending the processed data to the server reduces the wireless transmission energy.

Our analysis suggests that cloud computing can potentially save energy for mobile users. How­ever, not all applications are energy efficient when migrated to the cloud. Mobile cloud computing services would be significantly different from cloud services for desktops because they must offer energy savings. The services should consider the energy overhead for privacy, security, reliability, and data communication before offloading.