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Network Assisted Mobile Computing with Optimal Uplink Query Processing pdf
Network Assisted Mobile Computing with Optimal Uplink Query Processing


.pdf  Network Assisted Mobile .pdf (Size: 704.95 KB / Downloads: 14)

Abstract

Many mobile applications retrieve content from remote servers via user generated queries. Processing these queries is
often needed before the desired content can be identified. Processing the request on the mobile devices can quickly sap the limited
battery resources. Conversely, processing user-queries at remote servers can have slow response times due communication latency
incurred during transmission of the potentially large query. We evaluate a network-assisted mobile computing scenario where mid-
network nodes with “leasing” capabilities are deployed by a service provider. Leasing computation power can reduce battery usage on
the mobile devices and improve response times. However, borrowing processing power from mid-network nodes comes at a leasing
cost which must be accounted for when making the decision of where processing should occur. We study the tradeoff between battery
usage, processing and transmission latency, and mid-network leasing. We use the dynamic programming framework to solve for the
optimal processing policies that suggest the amount of processing to be done at each mid-network node in order to minimize the
processing and communication latency and processing costs. Through numerical studies, we examine the properties of the optimal
processing policy and the core tradeoffs in such systems.

I NTRODUCTION

The processing and storage capabilities of mobile consumer
devices are becoming increasingly powerful. A gamut of new
mobile applications has thus emerged for providing a better
quality of experience for the end users. A class of such appli-
cations commonly referred to as mobile augmented reality [1]–
[3] includes ones that enable delivery of content in response
to the user-generated queries for enhancing user’s experience
of the environment. Text to speech conversion and optical
character recognition (OCR) based applications for mobile
devices follow a similar paradigm. Several interesting usage
scenarios thus arise. A user clicks a picture or shoots a video
of a desired object–a building, painting in a museum, a CD
cover, or a movie poster–through a camera phone. The video
or image is then processed and sent over the network to an
application server hosting a database of images. The extracted
query image is then matched with a suitable entry and the
resulting content–object information, location, title song from
a CD, or movie trailer–is then streamed back to the user.
A number of existing commercial product provide this type
of service [4]–[6].

Related Work

As mobile applications become more sophisticated and de-
manding, system operators are utilizing the network to im-
prove service. A substantial amount of work has examined
Network-Assisted Computing. However, the main distinction
between the previous works and ours is that we focus on
allowing processing power to be leased from mid-network
nodes and how to make this decision in an optimal manner.
In [27]–[29], Network-Assisted Computing has been exam-
ined in the case of cache management. The focus of these
works is to determine how to pre-fetch information from a
remote server in order to maximize quality of service. Due
to the varying quality of the wireless channel, data may not
be able to be retrieved at the precise instant it is needed. If
that data is not available to the wireless device when needed,
the processor will idle until it can be fetched. Pre-fetching is
done in a manner to minimize service latency. These works
focus on the downlink transmission to make data available and
minimize processing times. In contrast, there are applications
where the data necessary to complete a request is too large
to store at the mobile device. In Mobile Augmented Reality
applications, it is infeasible to store even part of the large
database required. In the applications we consider, we assume
that the request must be transmitted uplink to an Application
Server in order to be fully satisfied. We focus on the uplink
scheduling of how much processing to perform at each node
in order to minimize latency, battery usage, and leasing costs.

Request Size and Processing Model

A request originates at the Mobile Station. Each request
consists of M stages of processing before the desired content
can begin streaming to the MS. For instance, M can represent
the amount of time required to fully process the request at the
MS. Because the processing power at the MS may differ from
that at the AS due to different processor types and loads, M is
not the amount of time required to fully process the request at
the Application Server. Therefore, M is a normalized quantity
which represents the total amount of processing required to
satisfy the request. Certainly M will depend on the particular
request and type of data that requires processing.

C ONCLUSION

The popularity of mobile applications is steadily increasing.
Many of these applications require significant computation
power, especially in the case of multimedia applications.
As the demand, as well as the sophistication and required
computation power, for these types of applications increases,
battery and communication bandwidth limitations may prevent
the use of many of these applications. By “leasing” processing
power from mid-network nodes, the battery drain and commu-
nication latency may be diminished. Network-Assisted Mobile
Computing can help alleviate the processing burden off the
Mobile Station without increasing the service latency.
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