Metrics | Computer Networks - Computer Science Engineering (CSE) PDF Download

Metrics
The preceding discussion assumes that link costs, or metrics, are known when we execute the routing algorithm. In this section, we look at some ways to calculate link costs that have proven effective in practice. One example that we have seen already, which is quite reasonable and very simple, is to assign a cost of 1 to all links—the least-cost route will then be the one with the fewest hops. Such an approach has several drawbacks, however. First, it does not distinguish between links on a latency basis. Thus, a satellite link with 250-ms latency looks just as attractive to the routing protocol as a terrestrial link with 1-ms latency. Second, it does not distinguish between routes on a capacity basis, making a 9.6-Kbps link look just as good as a 45-Mbps link. Finally, it does not distinguish between links based on their current load, making it impossible to route around overloaded links. It turns out that this last problem is the hardest because you are trying to capture the complex and dynamic characteristics of a link in a single scalar cost.

The ARPANET was the testing ground for a number of different approaches to link-cost calculation. (It was also the place where the superior stability of link-state over distance-vector routing was demonstrated; the original mechanism used distance vector while the later version used link state.) The following discussion traces the evolution of the ARPANET routing metric and, in so doing, explores the subtle aspects of the problem.

The original ARPANET routing metric measured the number of packets that were queued waiting to be transmitted on each link, meaning that a link with 10 packets queued waiting to be transmitted was assigned a larger cost weight than a link with 5 packets queued for transmission. Using queue length as a routing metric did not work well, however, since queue length is an artificial measure of load—it moves packets toward the shortest queue rather than toward the destination, a situation all too familiar to those of us who hop from line to line at the grocery store. Stated more precisely, the original ARPANET routing mechanism suffered from the fact that it did not take either the bandwidth or the latency of the link into consideration.

A second version of the ARPANET routing algorithm, sometimes called the “new routing mechanism,” took both link bandwidth and latency into consideration and used delay, rather than just queue length, as a measure of load. This was done as follows. First, each incoming packet was timestamped with its time of arrival at the router (ArrivalTime); its departure time from the router (DepartTime) was also recorded. Second, when the link-level ACK was received from the other side, the node computed the delay for that packet as

Delay = (DepartTime− ArrivalTime) +TransmissionTime +Latency

where TransmissionTime and Latency were statically defined for the link and captured the link’s bandwidth and latency, respectively. Notice that in this case, DepartTime − ArrivalTime represents the amount of time the packet was delayed (queued) in the node due to load. If the ACK did not arrive, but instead the packet timed out, then DepartTime was reset to the time the packet was retransmitted. In this case, DepartTime − ArrivalTime captures the reliability of the link—the more frequent the retransmission of packets, the less reliable the link, and the more we want to avoid it. Finally, the weight assigned to each link was derived from the average delay experienced by the packets recently sent over that link.

  • A highly loaded link never shows a cost of more than three times its cost when idle;
  • The most expensive link is only seven times the cost of the least expensive;
  • A high-speed satellite link is more attractive than a low-speed terrestrial link;
  • Cost is a function of link utilization only at moderate to high loads.

Metrics | Computer Networks - Computer Science Engineering (CSE) 

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FAQs on Metrics - Computer Networks - Computer Science Engineering (CSE)

1. What are metrics in marketing?
Metrics in marketing are quantifiable measures used to track and assess the performance and effectiveness of marketing campaigns, strategies, and activities. They provide valuable insights into various aspects of marketing, such as customer engagement, brand awareness, lead generation, conversion rates, and return on investment (ROI).
2. Why are metrics important in marketing?
Metrics play a crucial role in marketing as they enable businesses to evaluate the success of their marketing efforts and make data-driven decisions. By tracking and analyzing relevant metrics, companies can identify what marketing strategies or campaigns are working well and which ones need improvement. This helps optimize marketing activities, allocate resources effectively, and ultimately drive better business results.
3. What are some key metrics to measure marketing performance?
There are several key metrics that businesses often use to measure marketing performance. These include: 1. Customer Acquisition Cost (CAC): This metric calculates the average cost of acquiring a new customer, helping businesses understand the efficiency of their customer acquisition efforts. 2. Conversion Rate: Conversion rate measures the percentage of website visitors or leads who take the desired action, such as making a purchase or filling out a form. It indicates how effective a marketing campaign is at driving conversions. 3. Return on Investment (ROI): ROI measures the profitability of a marketing campaign by comparing the revenue generated to the cost of the campaign. It helps determine the overall effectiveness and profitability of marketing initiatives. 4. Customer Lifetime Value (CLV): CLV assesses the total value a customer brings to a business over their lifetime as a customer. It helps identify the long-term worth of acquiring and retaining customers. 5. Website Traffic and Engagement: Monitoring metrics like website traffic, page views, bounce rate, and time spent on site provides insights into the effectiveness of digital marketing efforts and user engagement.
4. How can businesses use metrics to improve their marketing strategies?
Businesses can use metrics to improve their marketing strategies in several ways: 1. Identify strengths and weaknesses: By analyzing metrics, businesses can identify which marketing strategies are yielding positive results and which ones are underperforming. This information helps them refine their strategies and focus resources on the most effective tactics. 2. Optimize campaigns: By closely monitoring metrics during marketing campaigns, businesses can quickly identify areas that need improvement and make necessary adjustments. This enables them to optimize campaigns in real-time and maximize their effectiveness. 3. Set realistic goals: Metrics provide businesses with a benchmark to set realistic and measurable goals for their marketing efforts. By setting specific targets based on relevant metrics, businesses can track progress and ensure they are on track to achieve their objectives. 4. Allocate resources effectively: Metrics help businesses understand which marketing channels or campaigns are generating the highest return on investment. This information enables them to allocate resources more effectively by investing in the most successful strategies and eliminating or reducing spending on less effective ones. 5. Improve customer experience: By analyzing metrics related to customer engagement and satisfaction, businesses can identify areas for improvement in the customer experience. This allows them to make necessary adjustments to marketing strategies and tactics to better meet customer needs and expectations.
5. How can metrics help businesses measure the success of their marketing campaigns?
Metrics provide businesses with tangible and quantifiable data to measure the success of their marketing campaigns. By tracking relevant metrics, businesses can assess various aspects of campaign performance, such as reach, engagement, conversion rates, and ROI. This information allows them to evaluate whether the campaign achieved its objectives and compare its performance to previous campaigns or industry benchmarks. Metrics also help businesses identify areas for improvement and make data-driven decisions to optimize future campaigns for better results.
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