15 College of Southern Nevada Responsibilities and Roles of Assistant Director of Business Transient Sales: Analysis of Role, Description and Specification, Impact to Hotel Sales Student Name HMD 259 Professor Michelle Scher 24 October 2016 Responsibilities and Roles of Assistant Director of Business Transient Sales: Analysis of Role, Description and Specification, Impact to Hotel Sales Introduction The goal of this paper is to thoroughly and adequately provide a job description and specification while presenting a short internal analysis of the importance of the role to a fictitious hotel. Using the lessons learned throughout the semester, an attempt will be made to outline the role and the requirements of the position using a description and specification as well as a short argument as to the value, importance and justification for the role. After completion of the paper, a conclusion will rectify the job description, job specification, and internal justification. Analysis of and B.T.S. Deployment Business Transient Sales efforts to date play an integral role in the success in achieving stated budgets for the hotel brand. Travelers in this segment include corporate and local negotiated rate programs, corporate project programs, extended stay, and government including state and federal. Overall revenue for the brand for all classified rate programs will account for 23% of sales revenues and 20% of available inventory. There are currently 19 properties within the brand accounting for 7,000 room nights per day with an additional 3 properties coming on line in 2017 with an additional 500 rooms of total per night inventory. Business travelers book through multiple channels with the current mix of sales indicated in the chart below for the entire brand portfolio. Although business travelers primarily book through special rate program tools, travelers are also able to book through travel agencies, direct using a special rate code, or through various outlets through the GDS. In total, business transient sales will generate over $30 million in revenue for the brand. OTA 20% Web Direct 40% BTS 15% RCC 9% GDS 8% GR/WH 8% Data reported from PMS and Onq internal data system YTD thru September 30, 2016 Current Deployment and Efforts In 2004 there were 6 properties within the brand portfolio accounting for 3,000 rooms. In 2016 there is 19 properties with 7,000 room nights and although the amount of properties and rooms have increased by over two-fold, additional headcount has not kept pace and today there is still only one sales person responsible for this market, Manager Business Transient Sales. The graph below outlines the growth of the hotel portfolio while headcount has remained static. Amount of properties reported from internal PMS system as of September 30, 2016 The weekly breakdown of work efforts by the current sales manager are as follows: · Review RFP’s and qualifiers for upcoming rate programs – 60% ...

1. 15
College of Southern Nevada
Responsibilities and Roles of Assistant Director of Business
Transient Sales: Analysis of Role, Description and
Specification, Impact to Hotel Sales
Student Name
HMD 259
Professor Michelle Scher
24 October 2016
Responsibilities and Roles of Assistant Director of Business
Transient Sales: Analysis of Role, Description and
Specification, Impact to Hotel Sales
Introduction
The goal of this paper is to thoroughly and adequately provide a
job description and specification while presenting a short
internal analysis of the importance of the role to a fictitious

2. hotel. Using the lessons learned throughout the semester, an
attempt will be made to outline the role and the requirements of
the position using a description and specification as well as a
short argument as to the value, importance and justification for
the role. After completion of the paper, a conclusion will
rectify the job description, job specification, and internal
justification.
Analysis of and B.T.S. Deployment
Business Transient Sales efforts to date play an integral role in
the success in achieving stated budgets for the hotel brand.
Travelers in this segment include corporate and local negotiated
rate programs, corporate project programs, extended stay, and
government including state and federal. Overall revenue for the
brand for all classified rate programs will account for 23% of
sales revenues and 20% of available inventory. There are
currently 19 properties within the brand accounting for 7,000
room nights per day with an additional 3 properties coming on
line in 2017 with an additional 500 rooms of total per night
inventory. Business travelers book through multiple channels
with the current mix of sales indicated in the chart below for the
entire brand portfolio. Although business travelers primarily
book through special rate program tools, travelers are also able
to book through travel agencies, direct using a special rate code,
or through various outlets through the GDS. In total, business
transient sales will generate over $30 million in revenue for the
brand.
OTA 20%
Web Direct 40%
BTS 15%
RCC 9%
GDS 8%
GR/WH 8%
Data reported from PMS and Onq internal data system YTD thru
September 30, 2016

3. Current Deployment and Efforts
In 2004 there were 6 properties within the brand portfolio
accounting for 3,000 rooms. In 2016 there is 19 properties with
7,000 room nights and although the amount of properties and
rooms have increased by over two-fold, additional headcount
has not kept pace and today there is still only one sales person
responsible for this market, Manager Business Transient Sales.
The graph below outlines the growth of the hotel portfolio while
headcount has remained static.
Amount of properties reported from internal PMS system as of
September 30, 2016
The weekly breakdown of work efforts by the current sales
manager are as follows:
· Review RFP’s and qualifiers for upcoming rate programs –
60%
· Review production of corporate and local account – 10%
· Define overall market strategy for each property while
working with revenue team – 10%
· Support each defined segment within the program through
tradeshows, agent training, and conferences – 10%
· Locate new accounts for share-shift opportunities and
untapped markets – 10%
In order to continue to grow this market an Assistant Director of
Business Transient Sales is needed in order to define brand rate
strategy, lead the efforts for corporate and local negotiated
accounts, represent the brand while vetting new technology
impacting the Brand Performance Team, discovery of new
trends and reporting back to the Senior Director of Sales.
Based on company needs and appropriate historical surveys
related to similar positions within the comp set and positions
with similar responsibilities, the appropriate candidate will need
to exhibit outstanding sales and negotiating skills, understand
and report revenue and data tracking, and have the ability to

4. communicate effectively with various departments. Without the
proper skill set or position in place, additional revenues and
missed opportunities from lack of role focus and share shift
opportunities are estimated at $1 million in lost business
transient sales room revenue for 2016 and continued losses
beyond.
Without a focus on transient hotel guests in the future, market
share and new account development will be affected. In
addition, a current tertiary issue of additional work load with
existing sales team members through the adding of new
accounts, properties, and sales related business trips will be
challenged, leading to possible turnover and employee
retention. The possible addition of unannounced property
developments will continue to add to an already stressed work
load. By ensuring an additional senior sales position,
delegation of duties and the splitting of responsibilities will
ensure the brand and company is poised for the future as well as
allowing the current sales team to focus on new and existing
customers. Responsibilities outlined in the Job Description and
Specifications of a Assistant Director of Business Transient
Sales are outlined below.
Description
Position Title: Assistant Director Business Transient Sales
Department: Hotel Sales
Wage Category: Exempt
Organizational Unit: Hotel Operations
Reports To: Senior Director Hotel Sales
Position Location: Las Vegas Sales Office
Date Written: October 1, 2016
Business Transient Sales travelers are one of the most important
guests for our hotel company. The Assistant Director of
Business Transient Sales ensures current strategies align with
goals of the hotel, reviews future potential partnerships for
opportunities, and aligns efforts with operations and finance

5. departments to ensure proper goals are met. The role will
include contact not only with internal team members across all
departments but also all valued partners through digital and face
to face communication. The ability to negotiate, present
solutions to challenges, and find win-win scenarios to internal
and external partners is imperative for success in this role.
Guest interaction, timely, attentive and responsible responses to
ensure business partner, team member and guest immersion in
the company’s core values is instrumental in insuring budgeted
room revenue as well as team member and guest satisfaction.
Regular office attendance in conformance with the brand
standards is essential to the successful performance of this
position.
Subordinate Staff: 1 staff member reporting directly to
Assistant Director of Business Transient Sales and 1 staff
member working in conjunction with sales team and reporting
non-directly to Assistant Director in a support role.
Internal Contacts: Contacts include: revenue management,
multiple hotel operations teams, accounting and corporate
finance, other brand performance team members within parent
company, Senior Director of Hotel Sales, sales managers, and
personnel within the brand’s corporate offices that assist to
ensure proper strategies, budget performance, protocols, and
education is in place.
External Contacts: Government travelers and contractors,
national brand sales manager representatives, national business
travel managers, travel consortia representatives, strategic
partners, local CVB’s and local travel managers or designated
company travel arranger.
Position Purpose: Manages, leads, and directs the development
and execution of strategic sales plans and efforts as it pertains
to business transient sales, goals and initiatives to maximize
profitability for the BTS market for all properties within the
brand and to achieve budget, revenue and market share, and
share-shift targets. Commitment and dedication to hospitality
culture and company core values are an expected behavior to be

6. displayed towards our guests and team members at all times.
Lead direct report team along with revenue team in overall
performance strategy. This may include points of contact for
government booking platforms, business travel accounts,
negotiated and dynamic pricing corporate account leaders, and
penetration of local market for local negotiated rates for a
cluster or multiple hotels within the brand.
Job Duties and Essential Functions (broken down by percentage
of weekly tasks):
50% Hotel and Brand Sales Efforts
· Directs sales efforts departments in the development,
implementation and achievement of their annual business and
market plan objectives in conjunction with the Senior Director
of Sales.
· Provides leadership, guidance and assistance relating to the
execution of sales functions, efforts, policies and standards as
established by the company for support and direct report staff
· Capitalizes on parent company sales programs, and resources
when appropriate including review of dynamic pricing strategy,
promotions, and any future program or resource that is
identified
· Actively participates in the sales process via customer
meetings, entertainment and attendance at client and other
relevant industry events.
· Directs coordination of cross-selling between resorts, joint
marketing initiatives and other hotel/brand synergies to
maximize exposure and profitability including agency contact,
media planning and hotel communications for Business
Transient Sales.
· Directs the solicitation efforts of the sales staff through
effective oral and written communication while providing
strategic direction of rate, date and space commitments for
room sales of the properties.
· Develops and implements strategic and tactical plans to
maintain current base and increase each hotel’s share in new
markets.

7. · Directs the coordination of ongoing research of the travel
industry local and national market to detect market trends.
· Directs the efforts to Business Transient Sales internally and
externally.
· Works with the local CVB to locate and track sales
opportunities.
20% Revenue Management
· Works in tandem with the Director of Revenue Management to
provide strategic revenue management plans within the resort to
include; rate development, establishment of group thresholds,
space utilization policy, deployment strategies through the
review of competitive data, demand analysis and market mix
management.
· Establishes appropriate qualifiers for targeted local and
national accounts in order to attract corporate travelers to brand
and to specific hotels.
· Presents business case to revenue teams for each account for
rate strategy.
· Discovers and presents market shifts along with revenue team
to develop strategies to meet assigned goals.
20% Leadership
· Works directly with Senior Director Hotel Sales to ensure
meeting or exceeding specified budget.
· Leads all efforts pertaining to the BTS market segment.
· Outlines guidelines, tasks, objectives, yearly reviews, and
tracking of challenges and opportunities for all direct reports.
· Oversees the management, training and career development of
sales staff within the framework of the local competitive
marketplace and recommends appropriate sales compensation.
· Manages personnel functions such as selection, orientation,
training, performance reviews, discipline, counseling,
scheduling, pay and recognition for direct reports.
· Maintains a positive cooperative work environment between
staff and management.
· Helps develop management talent by acting as a mentor for
direct reports and other team members within and outside the

8. department.
· Attends management meetings and conducts departmental
meetings.
· Tracks marketing and travel spend for fiscal year budgets.
Establishes and presents to senior leadership overall revenues
generated and spend for the market segment during quarterly
and year end meetings.
· Meets regional leaders and general managers to review
quarterly results, challenges, and opportunities specific to their
property.
10% Sales Systems
· Assures effective utilization and adherence to standards
relating to current systems such as sales information systems, e-
mail, internet accessibility, PMS systems, GDS, and any future
identifiable technology systems that impact the company, the
hotels, and roles.
· Oversee new hotel builds within the system and initiates sales
and marketing programs to support new resorts brought online.
The team member may be required to do other duties and
special projects as assigned by the Senior Director of Hotel
Sales including but not limited; to support of group sales
efforts, assistance with tracking brand performance and
representation of sales office as well as brand performance trade
show and event support during internal and external meetings.
Supportive Functions
In addition to performance of the essential functions, this
position may be required to perform a combination of the
following supportive functions, with the percentage of time
performing each function to be solely determined by the
supervisor based upon the particular requirements of the
company.
· Set up sales systems, processes, and partnerships internally
and externally for newly opened hotels.
· Oversee client and internal sales familiarization events for
greater sales and property exposure.
· Represent brand within as well as outside of given market at

9. sales industry events.
· Demonstrates working knowledge of the service standards set
forth by company guidelines.
Job Environment:
· Office location within a hotel property in Las Vegas.
· Sales calls and office visits required and not limited to the
following:
· Government agencies, contractors, and support centers
· Local corporate offices
· Travel consortia and call support centers
· National sales offices
· Area CVB’s
· Properties within the brand outside of the Las Vegas area
· Corporate office visits based outside of Las Vegas for brand
and parent company.
Benefits and Compensation
Our hospitality company offers competitive benefits and wages.
A benefits package is available after the first 90 days that
includes paid vacation time, health, dental, vision and life
insurance as well as a matching 401K program. In addition,
annual bonuses based on company performance are currently
available and commission based sales opportunities allow for
increasing the salary base throughout the year. We are an Equal
Opportunity employer.
Specifications
The individual must possess the following knowledge, skills and
abilities and be able to explain and demonstrate that they can
perform the essential functions of the job.
· Ability to perform critical analysis and to read, and analyze
sales data and financial data reports.
· Possesses a comprehensive knowledge of negotiating skills
and sales procedures associated with the hotel/resort industry.
· Commitment to excellence and professionalism at all times.
· Ability to delegate, manage and organize complex projects and
establish priorities consistent with department, brand and hotel
objectives.

10. · Excellent listening, speaking and presentation skills.
· Ability to manage multiple projects, meet deadlines and work
effectively under time and resource constraints.
· Ability to manage and lead each discipline of the department
independently.
· Demonstrates excellence in service quality standards that
affect guest satisfaction, responding to guests in a timely and
professional manner. A courteous and professional demeanor
must prevail when handling upset guests, customers, and
difficult situations.
· Excellent English language communication skills in order to
communicate both verbally and in writing with guests, clients,
and team members.
· Knowledge and ability to demonstrate the use of Word, Excel,
and PowerPoint.
· Knowledge of booking platforms and systems specific to
hospitality.
Minimum Qualifications
· Minimum 7 years substantial operations/sales experience in a
comparable hotel.
· Minimum 4 years in hospitality management leadership
position.
· Four-year college degree preferred; additional advanced
degree coursework in business administration, marketing and
communications a plus.
· Understanding of hotel sales systems and tools.
· Proven track records of successes in achieving revenue
objectives.
· Proven ability to recruit, motivate and train hospitality sales
teams.
· Must be able to travel to various parts of the country for up to
two weeks each month and attend sales and marketing
conventions approximately every other month requiring long
periods of standing, sitting, speaking, and listening.
· Must be able to travel internationally for external and internal

11. meetings to foreign destinations as well as site visits to current
properties within the portfolio located outside of the state as
well as outside of the United States.
· Ability to travel between resort locations to oversee sales
operations.
· Ability to travel between base office and corporate office
located out of state for internal meetings.
This role is an integral part of our company’s success. Come
join a dynamic and exciting organization that is a market leader
in customer and team member focus.
Conclusion
Given the scope of the role as well as the unique attributes to
the sales engine it is somewhat challenging to compare
competitor pay scales in market. For instance, the role of the
position will mimic a regional sales office however
responsibilities will be brand-wide. Given the phenomenon of
this duality and using competitor pay scales along with the
current company pay grade policy that includes a factor
comparison model, a base pay grade and compensation was
concluded. The pay grade for this position falls within the realm
of a higher tiered assistant executive level position as the title
indicates. The higher level of pay is justified by the increased
sales responsibility for the entire brand. Main Competitor X
has a similar title with reduced responsibilities at $60,000-
70,000 per year base salary with an additional potential pay out
of bonus of up to 20%. Main Competitor Y does not have a
similar role as the responsibilities are handled from a
centralized regional office that is not brand centric. However,
base salary for a regional Director position that carries a similar
size and scope in responsibilities, is rated higher in base pay
and potential bonus and, for Competitor Y, includes a base
salary of $90,000 - $110,000 and up to 25% bonus pay out
based on performance. Benefits will add an additional 40% of
base salary on to the total compensation package and does not
include any training costs associated with the role. Given the
need for this role for the company and in order to ensure

12. attraction of the most qualified candidate a recommended base
salary of $75,000-$80,000 per year with bonus pay out that
follows a corporate plan that is based on overall company
performance, currently 11% of base pay, is recommended. This
position will add a much needed role to an important segment
while ensuring a return on investment.
Without an Assistant Director of Business Transient Sales the
brand and the hotels within the brand will continue to lose
opportunities to drive new account development and revenues.
Estimated revenue impact above and beyond additional revenues
from the opening of new properties and outside of the expected
revenue influence of a new property inventory through chain-
wide agreements (capturing revenues through this market by
additional inventory accessible through GDS or direct bookings)
by adding additional leadership position is as follows:
· 2017 +$1.4 million
· 2018 +$1.9 million
· 2019 +$2.5 million
· Total $5.8 million within 3 years
With salary, benefits, and wages estimated to be $125,000 with
the estimated return on investment in year 1 will at the ratio of
11.2:1. By year 3, increases in adjustments to salary, benefits
and wages, estimated at $133,000 using inflationary rate
increases of 3% per year, will be mitigated by additional
actualized revenue performance with year 3 R.O.I. estimated
with a ratio of 18.9:1. Additionally, greater efficiencies will be
achieved by ensuring responsibilities for this market is spread
across the sales team and limit the impact of job “burn-out” and
allow for an appropriate succession plan without losing
momentum due the natural course of employment fall-out and
team member attrition. The outlined Description and
Specification will ensure the right candidate is located for the
requested position. In conclusion, an Assistant Director of
Business Transient Sales for the brand is needed in order to

13. achieve revenue goals and team success today and into the
future. The thorough and cohesive outlined job Description and
Specification ensure the right candidate is selected for this
important position.
BRAND TOTAL OPEN HOTELS
YEAR68911121314151819222007200820092010201120122013
2014201520162017
Amount of Properties
YEAR
Evolution of storage
management: Transforming
raw data into information
S. Gopisetty
S. Agarwala
E. Butler
D. Jadav
S. Jaquet
M. Korupolu
R. Routray
P. Sarkar
A. Singh
M. Sivan-Zimet
C.-H. Tan
S. Uttamchandani
D. Merbach
S. Padbidri

14. A. Dieberger
E. M. Haber
E. Kandogan
C. A. Kieliszewski
D. Agrawal
M. Devarakonda
K.-W. Lee
K. Magoutis
D. C. Verma
N. G. Vogl
Exponential growth in storage requirements and an increasing
number of heterogeneous devices and application policies are
making enterprise storage management a nightmare for
administrators. Back-of-the-envelope calculations, rules of
thumb,
and manual correlation of individual device data are too error
prone for the day-to-day administrative tasks of resource
provisioning, problem determination, performance management,
and impact analysis. Storage management tools have evolved
over
the past several years from standardizing the data reported by
storage subsystems to providing intelligent planners. In this
paper,
we describe that evolution in the context of the IBM
TotalStoraget
Productivity Center (TPC)—a suite of tools to assist
administrators in the day-to-day tasks of monitoring,
configuring,
provisioning, managing change, analyzing configuration,
managing
performance, and determining problems. We describe our

15. ongoing
research to develop ways to simplify and automate these tasks
by
applying advanced analytics on the performance statistics and
raw
configuration and event data collected by TPC using the popular
Storage Management Initiative-Specification (SMI-S). In
addition, we provide details of SMART (storage management
analytics and reasoning technology) as a library that provides a
collection of data-aggregation functions and optimization
algorithms.
Introduction
Managing storage systems within an enterprise has
always been a complex task requiring skilled
administrators to ensure zero downtime and high
performance for business-critical applications. Over the
years, the management of storage area networks (SANs)
has become increasingly complex with petabyte-scale
enterprises, complex application requirements, and
heterogeneous hardware and protocols. Increased
sensitivity to the operational costs of information
technology is driving the efforts to optimally use
resources; just-in-time provisioning is replacing just-in-
case over-provisioning. To cope with the complexity,

16. administrators create diagrams of SAN device
connectivity, which provide only an out-of-date point in
time end-to-end view; they manage individual devices—
hosts, fabric switches, and storage controllers—that use
proprietary interfaces provided by individual vendors.
Each interface is different and reports data in
nonstandard formats. The administrators have developed
simple programs and collections of scripts to manage
these devices. In order to deal with the complexity and
because of the steep learning curve, administrators have
begun to specialize in specific areas based on function or
category. As a result of these conditions, administrators
of enterprise SANs no longer manage their SAN as a
whole; instead, they manage individual devices and use
manual correlation, specialization, and various forms of
bookkeeping to keep track of the parts.
In response, storage management tools have evolved to
assist administrators in managing increasingly complex

17. SANs. Several storage vendors, including IBM, have
recognized and responded to the need to simplify the
discovery, monitoring, and reporting of storage
subsystems and storage networks. Although devices such
as storage controllers and switches from various vendors
differ slightly in functionality, each device requires a
specific application programming interface (API) to
�Copyright 2008 by International Business Machines
Corporation. Copying in printed form for private use is
permitted without payment of royalty provided that (1) each
reproduction is done without alteration and (2) the Journal
reference and IBM copyright notice are included on the first
page. The title and abstract, but no other portions, of this
paper may be copied by any means or distributed royalty free
without further permission by computer-based and other
information-service systems. Permission to republish any other
portion of this paper must be obtained from the Editor.
IBM J. RES. & DEV. VOL. 52 NO. 4/5 JULY/SEPTEMBER
2008 S. GOPISETTY ET AL.
341
0018-8646/08/$5.00 ª 2008 IBM

18. retrieve configuration and performance information.
Thus, gathering performance data is done either by
means of vendor-provided APIs or via standard
interfaces, such as CIM (Common Information Model)
[1], SNMP (Simple Network Management Protocol) [2],
or SMI-S (Storage Management Initiative-Specification)
[3]. When communicating with devices using these
standard interfaces, connection is made either directly to
the device or indirectly through a secondary facilitator,
called a device agent, for example, a proxy CIM object
manager [4]. In addition to performance data and device
configuration, component failure and other events are
usually collected from devices using these same interfaces.
Collecting events and recording data from multiple
vendor devices was the starting point for tools such as the
IBM TotalStorage* Productivity Center (TPC) [5], the
EMC ControlCenter** [6], and the HP Systems Insight
Manager** [7]. These tools are generically referred to as

19. storage resource managers (SRMs). After retrieving data
from the device or device agents and computing deltas for
the device performance counters, SRMs place persistent
data in a database. From a single console, SRM
applications provide administrators with the ability to
monitor multiple devices, analyze device performance
thresholds, and track usage.
This is a significant step forward but falls short of what
administrators really need. For example, they need the
ability to configure devices and provision storage using a
common interface across multiple devices from different
manufacturers. SRM applications, however, use
proprietary API and CIM interfaces to perform
configuration changes and to provision tasks on the SAN
switches and storage subsystems. As a result, although
SRM device management interfaces are now used to
verify settings, rarely can an administrator use them to
perform device-specific changes. Thus, while these

20. generalized interfaces are powerful, they provide only the
capability to perform the most common tasks.
Aggregating end-to-end system data enables an
administrator to drastically improve the understanding of
how various devices within the data center are allocated
and to assess their current and historical utilization
values, but administrators still need additional help with
the decision-making required to perform administrative
tasks, especially in large environments. Consider a typical
data center scenario of a large SAN that consists of more
than 2 TB of storage from ten heterogeneous storage
controllers supplied by one or more vendors. On the host
side, there are more than 1,600 servers connected via four
Fibre Channel fabrics with multiple SAN switches. In
such an environment, administrators are typically
responsible for provisioning servers and storage when
new applications are added or the demand for an existing
application increases. Provisioning the storage and

21. adjusting the SAN zoning to create multiple paths to each
newly provisioned volume can take several days to a
week when done manually. The administrator needs to
identify which storage subsystems have available storage
and which of the newly provisioned servers are able to
meet the performance requirements and can access that
storage by means of at least two fabrics (to reduce the
likelihood of a single point of failure). Once the storage
controllers are identified, volumes are created (using the
storage controller management tool) and zoning is
performed (using the switch fabric management tool).
After performing several steps with different tools, the
final configuration may not be ideal and may cause
unintended problems with other systems attached to the
SAN. Thus, there is a need for higher-level tools that
assist the administrator with tasks such as provisioning
in order to prevent unintended side effects and to allow
changes to be made in hours instead of days.

22. Data center environments are constantly evolving.
After the initial plan deployment, administrators are
typically required to continuously monitor application
performance to ensure that it is not degrading. Solving a
performance degradation problem is nontrivial in large
environments and can take several hours of investigation
to pinpoint a saturated server, switch, storage subsystem,
or Fibre Channel port. After pinpointing the saturated
device, the administrator has to then investigate the cause
of saturation. For example, a Fibre Channel port at
the storage controller can become saturated as a result of
re-zoning such that most of the storage traffic from the
fabric to the controller flows through a single port instead
of being load-balanced across the multiple storage-
controller ports. This underscores the need
administrators have for validating configuration changes
so they can prevent misconfiguration problems from
occurring. Further, there is a need to track changes in the

23. configuration over an extended period of time such that a
configuration snapshot for different time periods is
available. Finally, when the problem does occur,
administrators need help short-listing the devices and
configuration changes for deeper analysis.
Advanced analytic tools in SRMs can assist in change
management, configuration analysis, provisioning,
performance management, problem determination,
resiliency planning, root-cause analysis, and impact
analysis. These tools use the raw data aggregated by the
SRM and analyze it to generate insights and
configuration options for such tasks as provisioning and
problem resolution. In this paper, we describe such tools
in the context of the IBM TPC. These tools help with four
key administrative performance-management tasks:
change management, configuration analysis, provisioning
and capacity planning, and performance management
and problem determination, each discussed in the

24. S. GOPISETTY ET AL. IBM J. RES. & DEV. VOL. 52 NO. 4/5
JULY/SEPTEMBER 2008
342
subsequent sections. To further enhance the ability of
these modules to extract information from the raw data,
we are developing SMART (storage management
analytics and reasoning technology) [8], a library of data-
aggregation functions for device modeling. SMART uses
regression functions, workload trending using time-series
analysis, end-to-end dependency functions, and data-
clustering techniques to detect abnormalities in workload
and device characteristics. For more detailed information
about SMART and its functions, please refer to our
related papers [9–13].
Change management
Administrators often update the system configuration, for
example, create and delete storage volumes, configure
zones within the Fibre Channel switches, change the

25. logical unit number masking of hosts and storage
volumes, and add new devices, hosts, and switches.
Configuration changes do not always take into account
potential second-order effects on other applications that
share the same SAN. For example, re-zoning a switch
may cause traffic to be redirected to other switches, which
can create a potential bottleneck for other applications.
Also, it is well documented that a high percentage of
storage downtime is caused by incorrect configuration
changes [14, 15]. Traditionally, administrators
maintained change logs that were manually updated with
the details of the configuration changes. These logs are
used for problem diagnosis, often at a much later point in
time and by a person other than the one who made the
change. In enterprise environments in which tens of daily
configuration changes can be affected by multiple
administrators, there is a need for a systems management
tool with the ability to track configuration history at a

26. fine granularity so that an administrator can accurately
reconstruct the precise state of the infrastructure at a
given point in time and use this information for problem
determination, change management, or auditing
purposes. The change rover component in TPC is
designed to satisfy this requirement.
The change rover provides temporal browsing
capability by making old data versions nameable and
accessible, thus allowing the user to reconstruct
configuration changes over a specified duration of time.
Administrators have two mechanisms for generating the
configuration history: on-demand and scheduled. With
the on-demand method, an administrator can take an
asynchronous snapshot of the system configuration at
will. Additionally, each snapshot can be associated with
an optional text tag. This tag can facilitate subsequent
collaborative debugging by a team of administrators and
it provides a means for auditing configuration change

27. actions. The scheduled method of generating a
configuration history lets users specify how frequently
snapshots of the system configuration are taken. The
history generation scheduler wakes up at the assigned
time and does its work unobtrusively in the background
without requiring intervention. This method automates
the cumbersome task of collecting periodic snapshots of
the system configuration state. However, any product
that stores historical data results in increased
consumption of storage space. Thus, the change rover
uses innovative technology to populate the database
repository that records only the configuration deltas (as
opposed to global snapshots) to minimize storage space
consumption for history data. As in a log-structured file
system, there is still the overhead created by the need
to replay the history to reconstruct the configuration
at some point in time; however, this runtime overhead is
minimized by intelligent use of database views and

28. indexes, and the savings in database space and overhead
compensate for the residual performance overhead.
Semantically, the change rover shows changes to
devices, device attributes, device interconnections, and
zoning configurations. Fundamentally, there are four
types of change operations that are of interest with
respect to the configuration of an entity: addition (e.g.,
provisioning a new volume on a storage subsystem),
modification (e.g., increasing the capacity of a given
volume), deletion (e.g., deleting a storage volume), and
no change.
A typical usage scenario for the change rover follows.
In a large distributed system configuration, changes
happen quite frequently. A change that negatively
impacts performance may not be noticed for weeks. At
the point when the administrator tries to solve the
problem, it is typically very difficult to determine which of
the many configuration changes could have caused the

29. problem. Using the change rover, the administrator can
go back and compare the system state from the time
before trouble reports started coming in and compare it
with later states of the system. The time slot under
consideration can be further refined until the problem is
identified and fixed. The synchronized graphical and
tabular views generated by the change rover, along with
drill down (moving from a summary view to more
detailed data), make it possible for the administrator to
view and compare the configuration at discrete points in
time and thus rapidly determine which configuration
change was the culprit.
In summary, the change rover provides a scalable and
easy-to-use way to visualize storage configuration at a
specific point in time and to compare configurations at
specific points in time for rapid problem determination.
Configuration analysis
Adherence to best practices is essential for successful
configuration and deployment of complex systems. While

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deploying a system in a data center, experts rely on
experience and best-practice guidelines to proactively
prevent configuration problems from occurring.
According to the IBM SAN Central team—an internal
group in IBM that deals with installation, configuration,
and troubleshooting of SANs for customers and gathers
and maintains a large knowledge base of customer
problems, solutions, and best practices—80% of
configuration problems are caused by the violation of
best practices. Generating a best-practices user’s manual
is costly, requiring many man-years of data gathering and
analysis. It is difficult for system administrators to
maintain their own dynamic set of best practices because
the technology is continuously evolving and intervendor

31. interoperability standards are still immature and lead to
hard-to-diagnose configuration problems.
The configuration analysis functionality in TPC is a
better approach. It is an extensible, policy-based analytic
framework to validate storage infrastructures against
best-practice violations in an end-to-end fashion. Best-
practice policies are encoded in a declarative policy
language and cover a wide range of domains, such as
fabric security, fabric configuration, storage and server
security, and configuration. The functionality is
extensible and allows the addition of policies for such
areas as server management and IP (Internet Protocol)
network fabric management. These policies are grouped
into the following categories:
1. Parametric—Accepts input parameters from the
administrator as thresholds.
2. Nonparametric—Does not require input parameters
from the administrator.

32. The following is an example of a parametric policy.
� Policy—Each fabric may have a maximum of n
number of zones. (In this policy, the administrator
can supply the value of n on the basis of the type of
fabric that imposes the zone number constraint.)
� Explanation—The configuration analysis function
checks whether the number of zone definitions in the
fabric is larger than the number that was entered by
the administrator. In large fabrics, too large a number
of zone definitions can become a problem. Fabric
zone definitions are controlled by one of the switches
in that fabric, and limiting their number ensures that
the zoning tables for the switch do not run out of
space. The zone-set scope is not supported by this
policy.
The following is an example of a nonparametric policy.
� Policy—Each host bus adapter (HBA) accesses
storage subsystem ports or tape ports, but not both.
� Explanation—The configuration analysis function
determines whether an HBA accesses both storage

33. subsystem and tape ports. Because HBA buffer
management is configured differently for storage
subsystems and tape, it is not desirable to use the
same HBA for both disk and tape traffic. A policy
violation is generated if a zone set allows an HBA
port to access both disk and tape. The fabric and
zone-set scopes are not supported by this policy
because an HBA can be connected to multiple fabrics.
The configuration analysis tool can be configured to
have different scopes that can range from the entire
environment to a single Fibre Channel fabric or a set of
Fibre Channel zone sets. These scopes can be selected on
the basis of the policies to be verified. Administrators can
decide to run a group of policies on a particular scope,
which is called a profile. Primitives such as scope and
profile help administrators customize their configuration
analysis environment.
Generally, configuration changes are scheduled

34. periodically or are synchronized with important event
occurrences in a managed storage environment. Tasks
such as storage provisioning and access control are tested
offline before being put into production. Administrators
can synchronize their configuration changes using
configuration analysis to determine whether any best
practice will be violated because of these changes. They
can incrementally fix the violations and run configuration
analysis. A more detailed discussion of currently
supported policies is available in the TPC version 3.3
update guide [16]. In our ongoing research, we are
applying machine-learning techniques to generate the list
of best practices from large collections of customer
problem logs [17].
Provisioning and capacity planning
One of the most challenging and time-consuming tasks in
enterprise data centers is application provisioning.
Introducing a new application (or even changing the
characteristics of an existing application) often takes

35. weeks. This is due primarily to the complexity involved in
capacity planning (identifying appropriate resources that
can be allocated to the application) and executing the
plan to provision the actual resources for the application.
Capacity planning has long been done manually by
using rules of thumb and back-of-the-envelope
calculations. Beginning with the basic capacity
requirement, an administrator decides how many storage
volumes to create, what their individual sizes should be,
and whether enough space is available in the subsystems
to accommodate the new volumes. With an understanding
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of the nature of the new workload (e.g., the read/write
ratio and the random/sequential ratio), the administrator
can try to choose where to place the new volumes so that

36. the application performance objectives can be met
without adversely impacting any preexisting workloads.
As shown in Figure 1, there are several parameters to take
into account. It requires not only familiarity with the
complex internal structure of all available subsystems and
access to the resource utilization and performance data
for the subsystem components, but also the ability to
analyze and match them appropriately.
The SAN storage-provisioning planner functionality in
TPC is designed to assist administrators in this process. It
uses live monitored performance data for each of the
internal subsystem components (device adapters, ranks,
and storage pools) and performs a detailed analysis on
the basis of subsystem models and performance upper
bounds to select appropriate subsystems. This analysis
and selection is a complex optimization task, as it
involves bin-packing algorithms and must deal with the
hierarchical constraints imposed by the internal structure

37. of the subsystems. Once the administrator selects a plan
to deploy, the volumes are created on the chosen
subsystems and a suitable number of paths from the hosts
to these volumes are set up, as are zoning configurations
as well [18].
The current TPC provisioning planner focuses
primarily on optimizing storage subsystem utilization by
careful placement. With new virtualization technologies
providing greater server isolation and mobility, more
attention is now being paid to ensuring the appropriate
utilization of server resources in conjunction with the
storage and I/O (input/output) fabric. Our ongoing work
extends the TPC planner to include integrated server and
storage placement using a new technique called SPARK
(stable proposals and resource knapsacks) [19]. Using a
novel combination of the stable marriage and 0/1
knapsack solutions, SPARK provides the first such
mechanism to decide placement for both computation

38. and data in a coupled manner. (Computation could be
placed, for instance, on a virtual machine.) This ensures
that applications requiring higher I/O rates are placed on
appropriate server–storage combinations.
A second stream of ongoing work is to reduce or
eliminate dependence on white-box models (only internals
can be viewed) for storage devices used in optimizations.
White-box models are less generic and limited to the
scope of only a few subsystems. SMART is a library of
black-box models (inputs and outputs can be viewed)
under development that is being designed to learn models
of subsystems based solely on their observed performance
data. Applicable machine-learning algorithms for device
models include regression methods, such as multivariate
linear regression and multivariate adaptive regression
splines [20], and decision-tree methods, such as
classification and regression tree (CaRT) [21] and
M5 [22]. Both CaRT and M5 are included in SMART.

39. Time-series models in SMART characterize a
workload on the basis of its historical behavior. The
model is used for predicting future behavior and for
analyzing pattern, periodicity, abnormality, and trend of
a data series. It helps the administrator make better
decisions in capacity planning. There are two main
categories of time-series analyses: time domain and
frequency domain. Analysis in the time domain is most
often used to determine trends and make predictions. We
use the popular autoregressive integrated moving average
(ARIMA) method [23] for time-domain analysis.
ARIMA models require that the order of the components
be determined—a challenging task when it has to be done
Figure 1
Storage configuration and planning operation. (LUN: logical
unit number; OLTP: online transaction processing.)
Provide best LUN
recommendations
Configuration

40. data
- I/O demand (I/O operations/s/GB)
- Average transfer size (KB/s)
- Sequential, random read and write percentages
- Cache utilization
- Peak activity time
- Standard OLTP
- OLTP high
- Data warehouse
- Sequential batch
- Document archival
- Based on workload analysis of existing volumes
Historical
performance data
Controller performance
upper bounds
User-defined workloads
Workload profile data Workload profile templates
Storage

41. planner
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manually. Through an extensive series of experiments, we
have developed best-practice values that allow us to
determine this order. We use fast Fourier transforms [24]
for frequency-domain analysis. Fourier transform gives
periodograms in which a periodic data series shows spikes
at its cycle, while a nonperiodic series is typically flat with
little variation.
Performance management and problem
determination
Storage administrators are responsible for ensuring that
enterprise applications maintain a certain level of I/O
performance (in terms of average I/O throughput and
response time). This task involves a detailed
understanding of the end-to-end server–storage path

42. consisting of server connectivity to Fibre Channel
switches, the connectivity of switches to other switches
and storage controllers, and the logical configuration of
storage pools and volumes within the storage controllers.
A typical enterprise-scale storage environment consists of
thousands of hosts, hundreds of Fibre Channel switches
with 8 to 64 ports each connecting tens of enterprise-class
storage controllers, tape libraries, and other devices. The
order of the number of end-to-end paths from host
servers to storage volumes can range upward from
thousands to millions. Manually correlating data
collected from individual devices within the infrastructure
is no longer a feasible alternative.
Performance management starts with appropriately
provisioning storage capacity and bandwidth on the basis
of application requirements. In addition, path and zone
planning is required to ensure that there is sufficient
bandwidth for connectivity between the application

43. server or servers and the storage subsystem. After the
initial setup, administrators continuously monitor and
analyze the end-to-end path to ensure that the
performance requirements are satisfied. Performance
violations can occur for several reasons with varying
levels of complexity. Violations can be caused by simple
device failures that are easy to detect or by relatively
complex device saturation caused by skew in the
workload of one or more applications sharing the device.
Thus, problem determination is an important aspect of
performance management and requires that
administrators drill down until they uncover the reason
for a performance violation.
There are several performance-management and
problem-determination tools with varying levels of
automation available in TPC. As described earlier, the
configuration analyzer continuously analyzes
configuration changes and checks for violations of best

44. practices as a method intended to prevent performance
problems before they occur. Similarly, the change rover
maintains historical configuration information, making it
possible for an administrator to review configuration
changes that could possibly have led to a performance
violation.
An important aspect of performance management and
problem determination is to provide end-to-end
information to the administrator using an intuitive,
flexible interface that allows administrators to understand
the overall environment and enables them to drill down
into the details of logical or physical entities to diagnose
system problems. The TPC datapath explorer is such an
interface. It uses advanced human–computer interaction
(HCI) concepts [25]. Its design objectives were derived
from numerous real-world case studies conducted to
understand how administrators execute their day-to-day
tasks and make use of available data for decision making.

45. The explorer provides a view of the end-to-end path
dependencies between servers and storage subsystems or
between storage subsystems (e.g., from a SAN volume
controller to back-end storage). In addition to
discovering path dependencies, the explorer also derives
the end-to-end performance and health information, that
is, information that consists of critical and other
configuration alerts related to the devices (typically found
in the device logs). In order to provide an intuitive view,
the overall datapath (Figure 2) is divided into three
groups: host, fabric, and subsystem.
Some of the key HCI concepts the explorer uses to
radically simplify tasks such as system diagnosis to trace
the source of a problem from a host to a switch to a
storage subsystem [26–28] are as follows:
� Semantic zooming and progressive disclosure—A
visualization technique for rendering very-high-
density data by adaptively changing the level of data
abstraction. While graphical zooming changes the

46. scale of the object being viewed, semantic zooming
changes the level of information abstraction, for
example, zooming out would mean going to a higher
level of abstraction. It is often employed in
conjunction with progressive disclosure, which
provides task-specific presentation and interaction in
a sequence of displays. Much of this capability was
achieved by anticipating the steps administrators
would take in completing tasks and then creating
displays to support the completion of those tasks
quickly.
� Multilevel, multiperspective layouts—Explorer is
capable of providing multiple views of the system
topology (server, fabric, and storage centric) with
varying levels of abstraction (overview, group, single
devices). Initially, users are shown an overview of
their entire systems environment in which devices are
grouped by type. In the event of a problem, users can

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view aggregated status to trace information to
troubled devices by drilling further down into the
environment, for example, beginning with fabric
groups and then moving downward eventually to a
switch in a fabric. The administrator can quickly
recall a specific view without having to back in to or
out of panel hierarchies or lose context.
� Grouping and aggregation—The explorer organizes
devices into a number of task-dependent groups that
can be custom defined. Users can focus not only on a
smaller number of devices but also on devices that are
relevant to the task at hand. For example, an
administrator can first regroup hosts by status and
then identify critical entities; they can then regroup
again by operating system or by a user-defined

48. location property to gain a different perspective on
the problem. Individual groups can be collapsed or
expanded in place. Collapsed groups show a summary
of their contents that enables users to survey the
contents and see important device information, such
as degraded status, even at higher levels. Aggregation
of device information helps in monitoring a large
number of entities, even when monitoring at higher
levels, and helps guide administrators to the root
cause of a problem at lower levels.
� Overlays—The viewer provides overlays to add task-
specific information such as health status,
performance status, or zone memberships. Overlay
status information is aggregated for groups up the
hierarchy of devices.
As an example, if a host is running slowly, the system
administrator can use explorer to ascertain the health of
the associated I/O path and determine whether a
component has failed or a link is congested. The explorer

49. highlights the performance problems that might be
causing the slow application response. As another
example, a system administrator may want to determine
whether the I/O paths for two applications (on separate
host logical unit numbers) are in conflict with each
other (e.g., because they share a common switch).
After viewing the I/O paths for these two applications,
the administrator can make the required zoning or
connectivity change to alleviate the problem.
Our ongoing research is focused on two aspects of
problem determination: abnormality detection and path
correlation. Abnormality detection analyzes the monitored
data to identify similarity clusters and isolate abnormal
samples in multidimensional performance data. It is
designed to answer questions such as What are the typical
workload characteristics? and Is the input abnormal? If
an abnormality is detected, it triggers an alert for the
Figure 2

50. End-to-end entity correlation using topology viewer.
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administrator and records a detailed snapshot of the
system configuration for later analysis. Path correlation
refers to the task of determining the mapping of each
application workload to the different paths and links in
the system. It is used to answer questions such as Which
applications are going through this link, port, or device?
and What are the application paths? Path correlation
functions are the basis for dependency discovery, problem
determination, and impact analysis. The literature shows
that there has been significant interest in using correlation
models for problem diagnosis and root-cause analysis
[26–30]. These models capture the relationships among
different components in the system by analyzing request
traces collected by node instrumentation or request

51. probing. These path correlation models capture the
relationships among different components in the system
by analyzing request traces collected by node
instrumentation or request probing.
To provide support for abnormality detection, we are
implementing a data-clustering module as part of the
SMART library. Data clustering is done using machine-
learning algorithms, namely k-means [31] and expectation
maximization [32]. The basic idea is that normal
monitoring samples will have similar values and will
always be clustered together (e.g., the response time of a
device for a given load will be similar in normal
circumstances); the abnormal samples will be far away
from their corresponding clusters and hence can be
detected and notification provided. The distance
measurement for abnormality considers the weighted
Euclidean distance between the sample and its cluster
centroid. We use weighted distance because different

52. metrics have different statistics, for example, a cache hit is
between 0% and 100%, while the I/O rate ranges into the
thousands. Metric weights are obtained from in-house
experiments and preloaded in the SMART library.
The path correlation module in SMART uses the
topology and fabric zoning information available in TPC.
The application-to-server mappings and those to the
server port, controller port, controller, and disk array are
extracted from the TPC. Routing information within the
fabric network is managed automatically by fabric
switches and, thus, is not available. Fortunately, fabric
networks typically use uniform configurations with
simple topology designs, which makes it easy to infer
routing paths. High redundancy in enterprise storage
systems is a challenge for path correlation. In a typical
real-world setup, one server has at least two unshared
fabric networks connecting to the storage controller, and
each path uses two to four redundant connections at each

53. device for load-balancing and failover. Existing
dependency models are not applicable since we cannot
currently instrument storage controllers or send probing
requests. A complete study of load-balancing and failover
behavior remains for future work.
Related work
Data storage needs have been rapidly increasing, creating
the need for more automated storage management. There
has been a significant amount of research in the area of
storage resource manager (SRM) tools that can be
differentiated along five axes: discovery and monitoring
of heterogeneous storage hardware and resources,
analyzing and reporting normal and anomalous behavior,
configuration and capacity planning, change execution,
and ease of use. The key SRM tools available today
include CA Storage Resource Manager [33], EMC
ControlCenter [6], HP Storage Essentials [34], IBM
TPC [5], Symantec storage management solutions [35],

54. and Network Appliance NetApp Storage Suite [36]. In
addition to these, there are other smaller companies (such
as Akorri, Brocade Communications Systems, and
Tek-Tools) in the market that focus on individual aspects
of storage management. A brief comparative study of
these commercial tools is available from Russell and
Passmore [37] in their ‘‘magic quadrant’’ analysis, which
compares major SRM software against different criteria.
In our view, the key aspect that distinguishes the IBM
TPC solution from the others is an easy-to-use unified
console that integrates all the SRM functions and
provides a seamless way for the administrator to discover,
monitor, analyze, plan, and execute by making use of the
advanced analytics described in this paper.
Visualizing high-density data is an area of active
research in the HCI domain [25, 38]. Topology viewer
uses some of the HCI concepts such as semantic zooming
and progressive disclosure to change the level of data

55. abstraction adaptively. The change rover is related to
software versioning tools that keep track of different
software modifications and allow users to compare their
changes with earlier versions of their code. The change
rover applies similar concepts in the SAN environment so
that system administrators can keep track of changes in
the configuration of devices, zones, and interconnects.
The configuration analyzer enables the use of Technology
Infrastructure Library (ITIL**) [39] best practices for the
management of storage infrastructures and services.
Provisioning and capacity planning have been well
studied [40]. There are many commercially available tools
(e.g., EMC ControlCenter SAN Manager [6] and CA
SAN Designer [41]) and research prototypes (such as
Minerva [42], Ergastulum [43], and HP Appia [44]) that
perform capacity planning for shared storage systems.
One of the major factors that differentiate TPC from
these products is that it can plan volume allocation, port

56. selection, or zoning on the basis of runtime performance
and subsystem internal component utilization, which may
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become necessary once the infrastructure has been
deployed. Previous algorithms [45] for disk layouts and
file placements have been proposed, but the difficulty is
taking into account the hierarchical and other practical
constraints that are common in modern SAN
environments.
Conclusion and future work
In the last few years, there has been a significant evolution
in the domain of storage management. Starting with the
manual collection of data from individual device
management graphical user interfaces, storage
management is evolving to an approach that standardizes
the collection of data for multivendor devices followed by

57. persistence in a common repository and provides end-to-
end topology information integrated with analytic tools
to assist administrators with day-to-day administrative
tasks. In this paper, we presented a description of various
analytic features of the IBM TPC in the context of
existing techniques used by administrators and described
how TPC tools can simplify the day-to-day tasks of
change management, configuration analysis, provisioning
and capacity planning, performance management, and
problem determination.
Our ongoing research is focused on further automation
and simplification of the error-prone tasks of disaster
recovery planning, charge back [46], end-to-end
provisioning optimization [19], storage service
outsourcing [47], and others that are currently executed
using back-of-the-envelope calculations. Management
decisions are becoming more proactive rather than being
reactive. Administrators are increasingly using what-if

58. analyzers [48] to evaluate the impact of configuration
changes and system events. Our grand vision is a tighter
integration of storage management with server, virtual
machine, and IP network management, providing an end-
to-end application-level management environment with
dynamic continuous optimization.
*Trademark, service mark, or registered trademark of
International Business Machines Corporation in the United
States,
other countries, or both.
**Trademark, service mark, or registered trademark of EMC
Corporation, Hewlett-Packard Development Company, L.P.,
Office of Government Commerce, or Sun Microsystems, Inc., in
the United States, other countries, or both.
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Uttamchandani, and A. Verma, ‘‘Automated Planners for
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19. A. Singh, M. Korupolu, and B. Bamba, ‘‘SPARK: Integrated
Resource Allocation in Virtualization-Enabled SAN Data
Centers,’’ Research Report RJ-10407, IBM Almaden Research
Center, San Jose, CA 95120, April 2007; see http://www.
aameeksingh.com/projects/spark-rj10407.pdf.
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26. S. Agarwala, F. Alegre, K. Schwan, and J. Mehalingham,
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27. P. Barham, A. Donnelly, R. Isaacs, and R. Mortier, ‘‘Using
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63. Characterizing Dynamic Dependencies for Problem
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36. NetApp, Inc., NetApp Management Software: Storage Suite;
see http://www.netapp.com/us/products/management-software/.

64. 37. D. Russell and R. E. Passmore, ‘‘Magic Quadrant for
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;routram/ChargeView.pdf.
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66. the IEEE International Conference on Services Computing,
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Received October 1, 2007; accepted for publication
S. GOPISETTY ET AL. IBM J. RES. & DEV. VOL. 52 NO. 4/5
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December 19, 2007; Internet publication June 18, 2008
Sandeep Gopisetty IBM Almaden Research Center,
650 Harry Road, San Jose, California 95120
([email protected]). Mr. Gopisetty is a Senior Technical
Staff Member and manager. He leads the autonomic storage
management research, where he is responsible for the strategy,

67. vision, and architecture of the TPC and its analytics. He is
currently working on various optimization and resiliency
analytics
for autonomic storage resource manager and integrated systems
management. He is the recipient of several patents and IBM
corporate recognition awards including an Outstanding
Innovation
Award and a Supplemental Outstanding Technical Achievement
Award for his vision and technical contributions to the
architecture
of the TPC as well as leadership in driving his vision into plan
and
through implementation with a team that spanned three
divisions.
He also received an Outstanding Technical Achievement Award
and a Supplemental Outstanding Technical Achievement Award,
both for character recognition. His research interests include
object-oriented systems, Sun Java**, C and Cþþ programming,
and distributed database systems development. He graduated
with
an M.S. degree in computer engineering from Santa Clara
University.
Sandip Agarwala IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120 ([email protected]).

68. Dr. Agarwala is a Research Staff Member. He holds a Ph.D.
degree
in computer science from the Georgia Institute of Technology,
and
a B.Tech. degree in computer science from the Indian Institute
of
Technology, Kharagpur. His research interests are in the general
area of experimental computer systems, with primary focus on
the
design, development, and analysis of system- and middleware-
level
techniques to diagnose performance, manage resources, and
automate the management of large-scale distributed systems.
Eric Butler IBM Almaden Research Center, 650 Harry Road,
San Jose, California 95120. Mr. Butler is an Advisory Software
Engineer. He holds B.S. and M.S. degrees in electrical
engineering
from San Jose State University. His research interests include
data
center optimization; integrated system, storage, and network
management; and storage systems.
Divyesh Jadav IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120 ([email protected]).

69. Dr. Jadav is a Software Architect in the Storage Systems and
Servers group. He holds a B.E. degree from Bombay University,
India, and M.S. and Ph.D. degrees from Syracuse University, all
in
computer engineering. He has worked in the areas of RAID
(Redundant Array of Independent Disks) software, autonomic
performance control, and storage resource management.
Stefan Jaquet IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120. Mr. Jaquet is a Senior
Software
Engineer. He holds a B.S. degree in mathematics and computer
science from Santa Clara University, and an M.S. degree in
computer science from San Jose State University. He has
worked
on various data management, storage systems, and storage
management projects, and he is currently focused on integrated
storage and systems management as well as storage performance
management software.
Madhukar Korupolu IBM Almaden Research Center,
650 Harry Road, San Jose, California 95120
([email protected]). Dr. Korupolu is a Research Staff
Member. He holds M.S. and Ph.D. degrees in computer science

70. from the University of Texas at Austin, and a B.Tech. degree in
computer science from the Indian Institute of Technology,
Madras.
His areas of interest and contribution are in capacity planning
and
provisioning (technology released as part of IBM TotalStorage
Productivity Center), autonomic resource management and
related
server and storage optimization in data centers, virtualization
management, and more generally, algorithms and distributed
systems. He is presently an Associate Editor for the ACM
journal
Transactions on Storage.
Ramani Routray IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120 ([email protected]).
Mr. Routray is an Advisory Software Engineer. He holds an
M.S.
degree in computer science from Illinois Institute of
Technology.
His research interests include storage systems, SAN simulation,
integrated systems management, machine learning, and disaster
recovery.
Prasenjit Sarkar IBM Almaden Research Center, 650 Harry

71. Road, San Jose, California 95120. Dr. Sarkar is a Research Staff
Member in computer science and Master Inventor whose focus
is
on autonomic data storage resource management. He has made
key architectural contributions in the areas of self-management,
optimization, fault analysis, storage provisioning, and
orchestration that are featured in the IBM TotalStorage
Productivity Center suite of products. He holds a B.S. degree in
computer science and engineering from the Indian Institute of
Technology, Kharagpur, and M.S. and Ph.D. degrees in
computer
science, both from the University of Arizona. His initial
research
at IBM focused on the then-emerging field of storage
networking
over IP networks. In addition to authoring Internet Engineering
Task Force (IETF) industry standards, he was instrumental in
designing and releasing the industry’s first iSCSI (Internet
Small
Computer System Interface) storage controller in June 2001. He
has received five patents and three IBM Outstanding Technical
Achievement Awards.
Aameek Singh IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120 ([email protected]).

72. Dr. Singh holds a Ph.D. degree in computer science from the
Georgia Institute of Technology. His research interests include
integrated management and security for enterprise-scale storage
and distributed systems.
Miriam Sivan-Zimet IBM Almaden Research Center, 650
Harry Road, San Jose, California 95120. Ms. Sivan-Zimet is an
Advisory Software Engineer and holds an M.S. degree in
computer
science from the University of California at Santa Cruz.
Chung-Hao Tan IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120 ([email protected]).
Mr. Tan is a Senior Software Engineer. He holds an M.S. degree
in
computer science from the University of Southern California.
His research interests include HCI, system management, and
machine learning.
Sandeep Uttamchandani IBM Almaden Research Center,
650 Harry Road, San Jose, California 95120
([email protected]). Dr. Uttamchandani holds M.S. and
Ph.D. degrees from University of Illinois, Urbana–Champaign.
He

73. IBM J. RES. & DEV. VOL. 52 NO. 4/5 JULY/SEPTEMBER
2008 S. GOPISETTY ET AL.
351
currently leads the research effort in developing and delivering
a
resiliency planner for the IBM systems management product
line.
He has been involved in projects relating to storage protocols,
distributed file systems, autonomic storage management, and
large-
scale customer deployments. He started and developed the
SMART project at IBM Almaden Research Center, which
explored model-based techniques for storage management. He
has
authored several papers in key systems conferences and key
patent
disclosures in the systems management domain.
David Merbach IBM Systems and Technology Group,
3605 Highway 52 North, Rochester, Minnesota 55901
([email protected]). Mr. Merbach is an architect for the

74. TotalStorage Productivity Center.
Sumant Padbidri IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120 ([email protected]).
Mr. Padbidri is a Senior Technical Staff Member and lead
architect
for the TotalStorage Productivity Center at IBM. He holds an
M.S. degree in computer science from the University of
Bombay.
Andreas Dieberger IBM Almaden Research Center,
650 Harry Road, San Jose, California 95120. Dr. Dieberger is a
Research Staff Member working on HCI.
Eben M. Haber IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120. Dr. Haber is a Research Staff
Member working on HCI. He holds a Ph.D. degree from the
University of Wisconsin–Madison where he worked on
improving
user interfaces for database systems. His interests include
databases, user interfaces, and visualization of structured
information. He has worked on data mining and visualization as
well as user interface design, and he is currently studying
human
interactions with complex systems.

75. Eser Kandogan IBM Almaden Research Center, 650 Harry
Road, San Jose, California 95120. Dr. Kandogan is a Research
Staff Member. He holds a Ph.D. degree from the University of
Maryland, where he studied computer science with a
specialization
in HCI. His current interests include human interaction with
complex systems, policy-based system management,
ethnographic
studies of system administrators, information visualization, and
end-user programming.
Cheryl A. Kieliszewski IBM Almaden Research Center,
650 Harry Road, San Jose, California 95120 ([email protected]).
Dr. Kieliszewski is a Research Scientist focused on the human
element of service system design. She has worked in human
factors
and has a background in general design and HCI. She holds a
Ph.D. degree in industrial and systems engineering from the
Virginia Polytechnic Institute.
Dakshi Agrawal IBM Research Division, Thomas J. Watson
Research Center, 19 Skyline Drive, Hawthorne, New York
10532
([email protected]). Dr. Agrawal received a B.Tech. degree from

76. the Indian Institute of Technology–Kanpur, an M.S. degree from
Washington University, and a Ph.D. degree from the University
of
Illinois, Urbana–Champaign, all in electrical engineering. He
manages the Network Management Research group.
Murthy Devarakonda IBM Research Division, Thomas J.
Watson Research Center, P.O. Box 218, Yorktown Heights,
New York 10598. Dr. Devarakonda is a Senior Manager and
Research Staff Member in the Services Research department at
the
IBM T. J. Watson Research Center. He received his Ph.D.
degree
in computer science from the University of Illinois at Urbana–
Champaign in 1988. Presently, his research is focused on
distributed file systems, Web technologies, storage and systems
management, and now services computing. He received three
IBM
Research Division Awards for his work on distributed file
systems
and Global Technology Outlook development. Dr. Devarakonda
is a Senior Member of the IEEE and the ACM.
Kang-Won Lee IBM Research Division, Thomas J. Watson
Research Center, 19 Skyline Drive, Hawthorne, New York

77. 10532.
Dr. Lee is a Research Staff Member and a manager of the
Wireless
Network Research group. He holds a Ph.D. degree in computer
science from the University of Illinois, Urbana–Champaign, and
B.S. and M.S. degrees in computer engineering from the Seoul
National University. His research interest lies in distributed
computing systems, wired and wireless computer networks, and
on-demand policy-based computer system management. He
received an IBM Research Division Award for his contribution
in
policy-based autonomic computing systems.
Kostas Magoutis IBM Research Division, Thomas J. Watson
Research Center, P.O. Box 218, Yorktown Heights, New York
10598 ([email protected]). Dr. Magoutis is a Research Staff
Member in the Services Research department. His research
interests are in distributed systems, storage systems, and IT
services. Recently, he has been working on modeling and
management of distributed middleware systems, self-regulating,
high-speed access to network storage systems, and improving
the
design and delivery of IT services. He holds a Ph.D. degree in
computer science from Harvard University.

78. Dinesh C. Verma IBM Research Division, Thomas J. Watson
Research Center, 19 Skyline Drive, Hawthorne, New York
10532
([email protected]). Dr. Verma is a researcher and senior
manager in the networking technology area. He holds a Ph.D.
degree in computer networking from the University of
California
Berkeley, a Masters in Management of Technology degree from
Polytechnic University, and a B.S. degree in computer science
from
the Indian Institute of Technology, Kanpur. He holds 24 patents
related to computer networks and has authored more than 50
papers and four books in the field. He is the program manager
for
the U.S. and UK International Technology Alliance in Network
Sciences. He is a Fellow of the IEEE and has served in various
program committees and technical committees. His research
interests include topics in wireless networks, network
management,
distributed computing, and autonomic systems.
Norbert G. Vogl IBM Research Division, Thomas J. Watson
Research Center, P.O. Box 218, Yorktown Heights, New York
10598 ([email protected]). Mr. Vogl holds degrees in
mathematics

79. and computer science from Clarkson University and
Pennsylvania
State University. He develops service and application
prototypes,
and his experience includes decision support for storage
allocation,
IT in the small and medium business sector, bulk file delivery
via
satellite communication systems, video and data transmission
over
residential broadband, and workflows of intraenterprise
electronic
commerce.
S. GOPISETTY ET AL. IBM J. RES. & DEV. VOL. 52 NO. 4/5
JULY/SEPTEMBER 2008
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Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.

80. Category
HMD 259 Human Resources Paper
Exceeds Standard
Meets Standard
Nearly Meets Standard
Does Not Meet Standard
Score
(50 points maximum)
Introduction and
Topic Development
Paper thoroughly details topic student selected Significant
detail is shown and topic is well developed and significances of
topic is reasonably developed.
Strong development of topic is present. Missing a few
developmental areas. Paper is still easy to understand and topic
is moderately developed.
Limited introduction or background on topic is present. Several
development areas are missing. Topic is hard to follow and
unclear.
No background or development of topic present.
Practical merit and usability of material presented
Student presents material that is usable and practical in the
modern hospitality industry. Material is strong and correlates
with course text. Material has clear transferability to career.

81. Material presented is mostly usable in the hospitality industry.
Material mostly correlates with course text. Material presented
has some transferability to career.
Material presented has limited transferability to career. Few
course text elements covered. Project has limited practical use.
Material presented has no transferability to industry and no
course text elements were not used.
Paper Flow and Organization
Paper is well developed and organized. Paper is easy to follow
and logical in its presentation. All graded elements met
Paper has minor organization issues. Majority of graded
elements met. Paper is mostly easy to follow
Paper is hard to follow and lacks a clear organizational
structure. Several graded elements missed and paper is hard for
reader to follow.
Poor organization structure is presented. Majority of graded
elements missed. Paper is illogical in its presentation and no
formal structure is present
Paper Formatting and Grammar
Paper is properly formatted to grading scale and according to
directions. Grammar is correct and paper is free from
significant grammar and language errors
Paper is mostly formatted according to grading scale. A few

82. formatting issues missed. Overall paper has good grammar with
only a few significant grammar and language errors
Paper is not formatted to graded elements or has missed several
graded elements. Significant grammar and language issues
throughout make paper hard to read and clearly understand.
No required formatting is used. Paper is not presented as
directions present. Major grammar and language issues
throughout.
Original Content Used
Papers is 100% original in its analysis. Details about company
not original are thoroughly documented by student and proper
documentation is used. Works cited page is present for non-
original material.
Paper is mostly own analysis. Only minor elements appear to be
used from other sources. Citations are used for all unoriginal
work, but may be may not be clearly presented or properly
formatted. Minor content issues arise.
Paper appears to be significantly taken from another source.
Limited citation used and limited or no formal works cited page
is present. Paper is hard to follow what is original and what is
used from outside sources.
Paper entirely appears to be from an outside source. No
citations presented and no works cited page present.

83. Word Count
2000 or more words presented.
1950 – 1800 words: 20%-point deduction. Paper cannot score
higher than 40 points
1799 – 1600 words: 30%-point deduction. Paper cannot score
higher than 35 points
1599 – 1400 words: 40%-point deduction. Paper cannot score
higher than 30 points
1399 – 1000 words: 50%-point deduction. Paper cannot score
higher than 25 points
Less than 999 words: 75%-point deduction. Paper cannot score
higher than 12 points
Less than 500 words: no points awarded.
College of Southern Nevada
Department of Hospitality Management

84. HMD 259 DE – Paper
Objective: To strengthen authentic education skill and create
industry practical material to coach students and develop
applied skills associated with recruiting employees.
Requirements: Students will choose one of the following topics
and develop material to be used in the hospitality industry to
recruit and interview employees.
Choose ONE topic:
· Develop a job description and job specification for a real
hospitality industry career. It can be any job from entry level to
senior management. The job description must be detailed and
well developed and include all key elements outlined in the
course text. This must be your own job designed and not
plagiarized from the internet.
· Develop a recruitment plan for a real hospitality industry
career. Detail the recruitment process for the job, where and
how will you promote the job, and how will you prepare for the
recruitment interview.
· Develop a well-designed set of recruitment questions for a job
in the hospitality industry. What questions are appropriate to
ask in the interview process? What does each question measure?
What is the best type of interview for the job you have selected?

85. What is the manager looking for in the interview?
Paper Formatting: Papers are to be formatted as follows:
· Typed
· 12 point font
· Double spaced
· Title page attached with students name and business visited
(the title page does not count as part of the total word count)
· Headings on each section (Introduction, conclusion, specific
headings to topic selected, etc.)
· Page numbers
· Papers must be turned in to the correct assignment box located
on CANVAS. Professor’s computer and CSN computers use MS
Word. To ensure professor can open paper use MS Word
Document.
Paper Due Date: Review the syllabus for specific due date.
Papers can be turned in early if student is concerned about
meeting the deadline. NO LATE PAPERS ARE ACCEPTED.
Grading scale: A grading scale for the paper is attached to this
assignment. Students will be graded on spelling and grammar
in their papers. Minor spelling errors and grammar errors will
be accepted, but poorly written or grammatically incorrect
papers will lose points. Students may visit the CSN writing

86. center for writing help.
The Writing Center is located in the C building room 112 on the
West Charleston Campus. Phone number: 702-651-7402. The
Writing Center provides free assistance with all aspects of paper
writing.

88. COLLEGE OF SOUTHERN NEVADA
DEPARTMENT OF HOTEL MANAGEMENT
HMD 259 – Paper Grading Form
50 points
Grammar & Format
· Spelling
· Grammar
· Sentence Structure
· Double Spacing
· 12 point font
· Headings
· Layout
· Title Page
Content
· Student detailed topic they selected.
· Material presented was original content and not plagiarized.
· Any outside material was documented.
· Students material was well developed.
· Student used terms and material covered in text book.
· Student presented all material asked in topic they selected.
· Student created material that is practical in its use.

89. · Student demonstrated an understanding of the topic and its
importance in the hiring process.
· Student demonstrated modern and best practice approach for
their recruiting topic.
· Student used authentic methods that can be transferred to
industry.
· Overall topic content was thoroughly addressed.
· Student met the required word length for this project.
Paper Layout, Flow, and Overall Impression
· Does the paper have a beginning, middle, and an end?
· Does the paper flow or jump around from topic to topic?
· Does the paper have direction?
· Is the paper well written?
· Is the paper easy to follow?
· Does the paper have detailed information?
Paper must be at required length of 2000 to 4000 words to
receive full credit.
Plagiarized papers receive a 0 score.