CLOUD COMPUTING FOR MOBILE USERS: CAN OFFLOADING COMPUTATION SAVE ENERGY?

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1 CLOUD COMPUTING FOR MOBILE USERS: CAN OFFLOADING COMPUTATION SAVE ENERGY? Appears in: 2010, Computer, IEEE Computer Society Authors: Karthik Kumar and Yung-Hsiang Lu Electrical and Computer Engineering, U.S, Purdue University Speaker: Allen Liao

2 OUTLINE INTRODUCTION SAVING ENERGY FOR MOBILE SYSTEMS OFFLOADING COMPUTATION TO SAVE ENERGY ENERGY ANALYSIS FOR COMPUTATION OFFLOADING SAMPLE APPLICATION BENEFITING FROM OFFLOADING MAKING COMPUTATION OFFLOADING MORE ATTRACTIVE CHALLENGES AND POSSIBLE SOLUTIONS PRIVACY AND SECURITY RELIABILITY REAL-TIME DATA CONCLUSION 2

BENEFITING FROM OFFLOADING MAKING COMPUTATION OFFLOADING MORE ATTRACTIVE

3 INTRODUCTION Cloud Computing A service provider owns and manages resources ( such as processing, memory, storage ), and users access them via the Internet. For example, Amazon Web Services Simple Storage Service (S3): let users store personal data Elastic Compute Cloud (EC2): perform computations on stored data 3

For example, Amazon Web Services Simple Storage Service (S3): let users store

4 INTRODUCTION Advantages Low initial capital investment Shorter start-up time for new services Lower maintenance and operation costs Higher utilization through virtualization Easier disaster recovery There are several benefits in shifting computing from the desktop to the cloud. The primary constraints for mobile computing are limited energy and wireless bandwidth. Can cloud computing provide energy saving as a service to mobile users? 4

several benefits in shifting computing from the desktop to the cloud.

5 SAVING ENERGY FOR MOBILE SYSTEMS Mobile systems Various studies have identified longer battery lifetime as the most desired feature of such systems. Many applications are too computation intensive. The computation must be performed in the cloud. Other applications can run on a mobile system. However, they consume significant amounts of energy. Image retrieval, voice recognition, gaming, and navigation. 5

The computation must be performed in the cloud. Other applications can run on a mobile system.

6 SAVING ENERGY FOR MOBILE SYSTEMS Low-power design has been an active research topic for many years. Basic approaches to saving energy and extending battery lifetime in mobile devices: 1. Adopt a new generation of semiconductor technology. As transistors become smaller, each transistor consumes less power. More transistors are needed to provide more functionalities and better performance 2. Avoid wasting energy. Whole systems or individual components may enter standby or sleep modes to save power. 3. Execute programs slowly. When a processor s clock speed doubles, the power consumption nearly octuples. If the clock speed is reduced by half, only one quarter of the energy is consumed Eliminate computation all together. The mobile system does not perform the computation. Instead, computation is performed somewhere else.

More transistors are needed to provide more functionalities and better performance 2. Avoid wasting energy. Whole systems or individual components may enter standby or sleep modes to save power. 3.

7 OFFLOADING COMPUTATION TO SAVE ENERGY What distinguishes cloud computing from the client-server computing model is the adoption of virtualization. Client-server computing: service providers managing programs running on servers Cloud computing: 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. Cloud computing can save energy for mobile users through computation offloading 7 Virtualization: Lets applications from different customers run on different virtual machines, thereby providing separation and protection.

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.

8 ENERGY ANALYSIS FOR COMPUTATION OFFLOADING Suppose the computation requires C instructions. M: The speeds in instructions per second of the mobile system S: The speeds in instructions per second of the cloud server D: The bytes which the server and mobile system exchanged B: Network bandwidth P c : The mobile system consumes for computing P i : The mobile system consumes while being idle P tr : The mobile system consumes for sending and receiving data. 8

cloud server D: The bytes which the server and mobile system exchanged B: Network bandwidth P c : The mobile

9 9 ENERGY ANALYSIS FOR COMPUTATION OFFLOADING

10 ENERGY ANALYSIS FOR COMPUTATION OFFLOADING Suppose the server is F times faster S = F M For example HP ipaq PDA M = 400 MHz, P c 0.9 W, P i 0.3 W, and P tr 1.3 W. Four-core server S = 3.2 GHz, F = (S/M) [(3.2 1,024 4 X)/400] Where X is the speedup due to additional memory If X = 5, F

3 W, and P tr 1.3 W. Four-core server S = 3.2 GHz, F = (S/M) [(3.

11 ENERGY ANALYSIS FOR COMPUTATION OFFLOADING B o : The minimum bandwidth required for offloading to save energy 11

12 SAMPLE APPLICATION BENEFITING FROM OFFLOADING Chess Markovian: the game is fully expressed by the current state. The amount of computation for chess is very large. Can be parallelized, making the value of F very large. The amount of computation C is extremely large, and D is very small, offloading is beneficial for most wireless networks. 12 An image retrieval application retrieves images Features: The program accomplishes this by comparing numerical representations of the images. The features for the image collection can be computed in advance. The value of C is small. D is large since considerable data must be sent. Even if the values of F become, D/B might still be too large when compared to C/M Offloading saves energy only if B is very large At high bandwidths

12 An image retrieval application retrieves images Features: The program accomplishes this by comparing numerical representations of the images.

13 13 MAKING COMPUTATION OFFLOADING MORE ATTRACTIVE Energy saved by computation offloading B: Wireless bandwidth C: Amount of computation to be performed D: Amount of data to be transmitted Fundamental assumption Client-Server Model: Because the server does not already contain the data, all the data must be sent to the service provider. Cloud Computing: The cloud stores data and performs computation on it. Google s Picasa Amazon S3 Amazon EC2 There is no longer a need to send the data over the wireless network; it suffices to send a pointer to the data. A very small D and very large F imply that energy can always be saved.

to the service provider. Cloud Computing: The cloud stores data and performs computation on it.

14 CHALLENGES AND POSSIBLE SOLUTIONS PRIVACY AND SECURITY Because the data is stored and managed in the cloud, security and privacy settings depend on the IT(information technology) management of the cloud provides. A bug or security loophole in the cloud might result in a breach of privacy. For example March 2009, a bug in Google caused documents to be shared without the owners knowledge. July 2009, a breach in Twitter allowed a hacker to obtain confidential documents. 2007, a phishing attack duped a staff member for salesforce.com into revealing a password. 14

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

15 PRIVACY AND SECURITY Tracking of individuals through location-based navigation data offloaded to the cloud. Data stored at one location may not be secure at another due to different access rules. The EU has stricter privacy protection than the US Some types of data cannot be stored in the cloud without considering these privacy and security implications. One possible solution is to encrypt data before storage. 15

The EU has stricter privacy protection than the US Some types of data cannot be stored in the cloud

16 PRIVACY AND SECURITY Pc (Cp/M): The additional energy required to protect privacy and security. 16 If this value is significant, cloud computing might not save energy.

17 RELIABILITY A mobile user performing computation in the cloud depends on The wireless network Cloud computing may not be possible, when connectivity is limited. National parks Basement of a building The value of B can become very small or even zero. Cloud service 17 Cloud for important computations could lead to problems during service outages. Google Amazon T-Mobile Significantly reduce the value of F.

National parks Basement of a building The value of B can become very small or even zero.

18 RELIABILITY Data storage In October 2009, T-Mobile s and Microsoft s mobile Sidekick service crashed All customers lost their data and contacts. Independent backup of data with an alternate service provider Might increase the value of D 18

19 REAL-TIME DATA Real-time data: D is no longer a pointer to the data; it refers to the actual data. Chess Mobile surveillance Context-aware navigation When the value of D is large, offloading may not save energy. Partitioning computation between the mobile system and the cloud to reduce energy consumption. 19

Chess Mobile surveillance Context-aware navigation When the value of D is

20 20 CONCLUSION Analysis suggests that cloud computing can potentially save energy for mobile users. Not all applications are energy efficient when migrated to the cloud. Mobile cloud computing services would be significantly different from cloud services for desktops Offer energy savings Before offloading, the services should consider energy overhead for Privacy Security Reliability Data communication

Mobile cloud computing services would be significantly different from cloud services for desktops

perform computations on stored data (Elastic Compute Cloud (EC2). )

Karthik Kumar and Yung-Hsiang Lu, Purdue University Presenter Yifei Sun Take Amazon cloud for example. store personal data (Simple Storage Service (S3) ) perform computations on stored data (Elastic Compute