Question

A single disk storage device has $N$ concentric tracks and one access arm. It has been loaded with data in such a way that successive movements of the access arm (called track seeks) are independent of one another. The probability that a randomly chosen seek will take the arm to track $i$ is $p_i$. Let $X$ represent the number of tracks passed between consecutive seeks, assuming that no physical repositioning of the access arm takes place between successive seek operations. Show the following: (a) $X$ assumes the values $0,1, \ldots, N-1$ and has the $\operatorname{pmf} p(\cdot)$ defined by $$ p(j)=P[X=j]= \begin{cases}\sum_{i=1}^N p_i^2, & j=0 \\ 2 \sum_{i=1}^{N-j} p_i p_{i+j}, & j=1,2, \ldots, N-1 .\end{cases} $$ (b) For the case that $p_i=1 / N$ for all $i$, it is true that $$ \begin{gathered} E[X]=\frac{\left(N^2-1\right)}{3 N} \approx \frac{N}{3}, \\ E\left[X^2\right]=\frac{\left(N^2-1\right)}{6} \approx \frac{N^2}{6}, \end{gathered} $$ and $$ \operatorname{Var}[X] \approx \frac{N^2}{18} . $$ (c) Suppose $T$, the seek time, is a linear function of $X$; that is, $$ T=A X+B, $$ where $A$ and $B$ are constants. ( $A$ is then given by $$ A=\frac{\text { maximum seek time }- \text { minimum seek time }}{N-1}, $$ and $B$ is minimum seek time.) Then it is true that $$ E[T]=\frac{A\left(N^2-1\right)}{3 N}+B \approx \frac{A N}{3}+B, $$ and $$ \operatorname{Var}[T]=A^2 \operatorname{Var}[X] \approx \frac{A^2 N^2}{18} . $$

   A single disk storage device has $N$ concentric tracks and one access arm. It has been loaded with data in such a way that successive movements of the access arm (called track seeks) are independent of one another. The probability that a randomly chosen seek will take the arm to track $i$ is $p_i$. Let $X$ represent the number of tracks passed between consecutive seeks, assuming that no physical repositioning of the access arm takes place between successive seek operations. Show the following:
(a) $X$ assumes the values $0,1, \ldots, N-1$ and has the $\operatorname{pmf} p(\cdot)$ defined by
$$
p(j)=P[X=j]= \begin{cases}\sum_{i=1}^N p_i^2, & j=0 \\ 2 \sum_{i=1}^{N-j} p_i p_{i+j}, & j=1,2, \ldots, N-1 .\end{cases}
$$
(b) For the case that $p_i=1 / N$ for all $i$, it is true that
$$
\begin{gathered}
E[X]=\frac{\left(N^2-1\right)}{3 N} \approx \frac{N}{3}, \\
E\left[X^2\right]=\frac{\left(N^2-1\right)}{6} \approx \frac{N^2}{6},
\end{gathered}
$$
and
$$
\operatorname{Var}[X] \approx \frac{N^2}{18} .
$$
(c) Suppose $T$, the seek time, is a linear function of $X$; that is,
$$
T=A X+B,
$$
where $A$ and $B$ are constants. ( $A$ is then given by
$$
A=\frac{\text { maximum seek time }- \text { minimum seek time }}{N-1},
$$
and $B$ is minimum seek time.)
Then it is true that
$$
E[T]=\frac{A\left(N^2-1\right)}{3 N}+B \approx \frac{A N}{3}+B,
$$
and
$$
\operatorname{Var}[T]=A^2 \operatorname{Var}[X] \approx \frac{A^2 N^2}{18} .
$$
Show more…
Probability, Statistics, and Queuing Theory with Computer Science Applications, Second Edition (Computer Science and Scientific Computing)
Probability, Statistics, and Queuing Theory with Computer Science Applications, Second Edition (Computer Science and Scientific Computing)
Arnold O. Allen 2nd Edition
Chapter 2, Problem 39 ↓

Instant Answer

verified

Step 1

- $X$ is defined as the number of tracks passed between consecutive seeks. This means if the arm moves from track $i$ to track $j$, then $X = |j - i|$. - The values of $X$ can range from $0$ (when the arm seeks the same track consecutively) to $N-1$ (when the arm  Show more…

Show all steps

lock
AceChat toggle button
Close icon
Ace pointing down

Please give Ace some feedback

Your feedback will help us improve your experience

Thumb up icon Thumb down icon
Thanks for your feedback!
Profile picture
A single disk storage device has $N$ concentric tracks and one access arm. It has been loaded with data in such a way that successive movements of the access arm (called track seeks) are independent of one another. The probability that a randomly chosen seek will take the arm to track $i$ is $p_i$. Let $X$ represent the number of tracks passed between consecutive seeks, assuming that no physical repositioning of the access arm takes place between successive seek operations. Show the following: (a) $X$ assumes the values $0,1, \ldots, N-1$ and has the $\operatorname{pmf} p(\cdot)$ defined by $$ p(j)=P[X=j]= \begin{cases}\sum_{i=1}^N p_i^2, & j=0 \\ 2 \sum_{i=1}^{N-j} p_i p_{i+j}, & j=1,2, \ldots, N-1 .\end{cases} $$ (b) For the case that $p_i=1 / N$ for all $i$, it is true that $$ \begin{gathered} E[X]=\frac{\left(N^2-1\right)}{3 N} \approx \frac{N}{3}, \\ E\left[X^2\right]=\frac{\left(N^2-1\right)}{6} \approx \frac{N^2}{6}, \end{gathered} $$ and $$ \operatorname{Var}[X] \approx \frac{N^2}{18} . $$ (c) Suppose $T$, the seek time, is a linear function of $X$; that is, $$ T=A X+B, $$ where $A$ and $B$ are constants. ( $A$ is then given by $$ A=\frac{\text { maximum seek time }- \text { minimum seek time }}{N-1}, $$ and $B$ is minimum seek time.) Then it is true that $$ E[T]=\frac{A\left(N^2-1\right)}{3 N}+B \approx \frac{A N}{3}+B, $$ and $$ \operatorname{Var}[T]=A^2 \operatorname{Var}[X] \approx \frac{A^2 N^2}{18} . $$
Close icon
Play audio
Feedback
Powered by NumerAI
*

Labs

-

Want to see this concept in action?

NEW

Explore this concept interactively to see how it behaves as you change inputs.

View Labs

*

Key Concepts

-
Linear Transformation of Random Variables
A linear transformation involves modifying a random variable by scaling it (multiplying by a constant) and shifting it (adding a constant). Such transformations affect the expectation linearly and scale the variance by the square of the coefficient multiply, which is essential when relating one measurable outcome to another, such as converting distances to times.
Independence
Independence in probability indicates that the outcome of one event does not affect the outcome of another. When successive actions or measurements are independent, their joint probabilities can be obtained by multiplying their individual probabilities, simplifying the analysis of complex systems.
Variance
Variance is a measure of the spread or dispersion of a random variable's distribution around its expectation. It quantifies how much the outcomes vary from the mean by averaging the squared differences between each outcome and the mean, which is critical for understanding the reliability and risk associated with the random variable.
Uniform Distribution
A uniform distribution is a type of probability distribution in which all outcomes are equally likely. When a random variable is uniformly distributed, calculations for expectation and variance become simpler and more symmetric, as each outcome contributes equally to the overall distribution.
Random Variables and Their Distributions
A random variable is a function that assigns a numerical value to the outcome of a random process. Its distribution, which can be described by a pmf in the discrete case, fully encapsulates the probabilities associated with each possible outcome, enabling analysis of likelihoods, expected outcomes, and other statistical properties.
Probability Mass Function
A probability mass function (pmf) gives the probability that a discrete random variable is exactly equal to each of its possible values. It is a fundamental tool for characterizing discrete probability distributions and allows one to compute the probability of any event by summing the appropriate mass function values.
Expectation
Expectation, or the mean, of a random variable represents the long-run average value of the outcomes and provides a central measure of the distribution. It is calculated by summing the product of each outcome with its probability, offering insight into the 'center' of the distribution.

*

Recommended Videos

-
a-consider-a-multi-armed-bandit-problem-with-5-armsactions-indexed-by-the-set-1-2-3-4-5-let-a-denote-the-expected-reward-of-arm-a-and-ta-denote-the-sample-mean-reward-of-arm-a-at-time-t-it-i-71645

a. Consider a multi-armed bandit problem with 5 arms/actions indexed by the set {1, 2, 3, 4, 5}. Let μ(a) denote the expected reward of arm a and μˆt(a) denote the sample mean reward of arm a at time t. It is given that: [μ(1) μ(2) μ(3) μ(4) μ(5)]=[0.4 0.4 0.5 0.6 0.7] [μˆt(1) μˆt(2) μˆt(3) μˆt(4) μˆt(5)]=[0.3 0.4 0.5 0.7 0.6] Assume that we use the following greedy strategy at time t: • Select at = arg max a μˆt(a) with probability 1 − 1/t. • Select at randomly from {1, 2, 3, 4, 5} with probability 1/t. a.1 Based on the values given above, identify the arm with the highest expected reward. Denote this arm by a∗. Write as a function of t the probability that arm a∗ will be selected at time t by the greedy strategy. a.2 Compute the expected reward of the greedy strategy at time t as a function of t.

Need help? Use Ace
Ace is your personal tutor. It breaks down any question with clear steps so you can learn.
Start Using Ace
Ace is your personal tutor for learning
Step-by-step explanations
Instant summaries
Summarize YouTube videos
Understand textbook images or PDFs
Study tools like quizzes and flashcards
Listen to your notes as a podcast
Continue solving this problem
Create a free account to:
  • View full step-by-step solution
  • Ask follow-up questions with Ace AI
  • Save progress and study later
Continue Free
Numerade

Get step-by-step video solution
from top educators

Continue with Clever
or



By creating an account, you agree to the Terms of Service and Privacy Policy
Already have an account? Log In

A free answer
just for you

Watch the video solution with this free unlock.

Numerade

Log in to watch this video
...and 100,000,000 more!


EMAIL

PASSWORD

OR
Continue with Clever