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NEW QUESTION: 1
TCPパフォーマンスの低下を防ぐQoSメカニズムはどれですか。
A. Shaper
B. WRED
C. LLQ
D. Rate-Limit
E. Fair-Queue
F. Policer
Answer: B
Explanation:
Explanation
Weighted Random Early Detection (WRED) is just a congestion avoidance mechanism. WRED drops packets selectively based on IP precedence. Edge routers assign IP precedences to packets as they enter the network. When a packet arrives, the following events occur:
The average queue size is calculated.
2. If the average is less than the minimum queue threshold, the arriving packet is queued.
3. If the average is between the minimum queue threshold for that type of traffic and the maximum threshold for the interface, the packet is either dropped or queued, depending on the packet drop probability for that type of traffic.
4. If the average queue size is greater than the maximum threshold, the packet is dropped.
WRED reduces the chances of tail drop (when the queue is full, the packet is dropped) by selectively dropping packets when the output interface begins to show signs of congestion (thus it can mitigate congestion by preventing the queue from filling up). By dropping some packets early rather than waiting until the queue is full, WRED avoids dropping large numbers of packets at once and minimizes the chances of global synchronization. Thus, WRED allows the transmission line to be used fully at all times.
WRED generally drops packets selectively based on IP precedence. Packets with a higher IP precedence are less likely to be dropped than packets with a lower precedence. Thus, the higher the priority of a packet, the higher the probability that the packet will be delivered.
Reference:
mt/qos-conavd-15-mt-book/qos-conavd-cfg-wred.html
WRED is only useful when the bulk of the traffic is TCP/IP traffic. With TCP, dropped packets indicate congestion, so the packet source will reduce its transmission rate. With other protocols, packet sources may not respond or may resend dropped packets at the same rate. Thus, dropping packets does not decrease congestion.
16/qos-conavd-xe-16-book/qos-conavd-oview.html
Note: Global synchronization occurs when multiple TCP hosts reduce their transmission rates in response to congestion. But when congestion is reduced, TCP hosts try to increase their transmission rates again simultaneously (known as slow-start algorithm), which causes another congestion. Global synchronization produces this graph:

NEW QUESTION: 2
Which of the following core network elements does CSFB have?
A. SGW
B. MME
C. MSC
D. PGW
Answer: B,C

NEW QUESTION: 3
Cisco FTDソフトウェアでは、アプライアンスを通過するトラフィックをパッシブに受信するように設定する必要があるインターフェイスモードはどれですか。
A. ERSPAN
B. タップ
C. ファイアウォール
D. IPSのみ
Answer: A

NEW QUESTION: 4
대규모 조직을 위해 일부 AWS 인프라를 배포 할 수 있는 범위가 주어졌습니다. 요구 사항은 많은 EC2 인스턴스가 있지만 Amazon EC2 집합의 평균 사용률이 높으면 더 추가해야 하고 반대로 CPU 사용률이 낮을 때는 제거해야 한다는 것입니다. 이를 위해 어떤 AWS 서비스를 사용하는 것이 가장 좋습니까?
A. Auto Scaling, Amazon CloudWatch 및 Elastic Load Balancing.
B. Auto Scaling, Amazon CloudWatch 및 AWS Elastic Beanstalk
C. Amazon CloudFront, Amazon CloudWatch 및 Elastic Load Balancing.
D. AWS Elastic Beanstalk, Amazon CloudWatch 및 Elastic Load Balancing.
Answer: A
Explanation:
Auto Scaling enables you to follow the demand curve for your applications closely, reducing the need to manually provision Amazon EC2 capacity in advance. For example, you can set a condition to add new Amazon EC2 instances in increments to the Auto Scaling group when the average utilization of your Amazon EC2 fleet is high; and similarly, you can set a condition to remove instances in the same increments when CPU utilization is low. If you have predictable load changes, you can set a schedule through Auto Scaling to plan your scaling activities. You can use Amazon CloudWatch to send alarms to trigger scaling activities and Elastic Load Balancing to help distribute traffic to your instances within Auto Scaling groups. Auto Scaling enables you to run your Amazon EC2 fleet at optimal utilization.
Reference: http://aws.amazon.com/autoscaling/