How unit 3 is examined
The unit covers running a cloud in an organization and keeping it fast: scenarios, monitoring, load balancing, optimization, provisioning, real-time apps, mobile cloud and edge. Marks come from load balancing, static versus dynamic provisioning, monitoring with optimization, and MCC with edge.
Organizational scenarios of clouds
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. An organizational cloud scenario is the way a company chooses to place its workloads on public, private, community or hybrid clouds to meet its cost, control and compliance needs.
Key points.
- A start-up with no capital uses a public cloud and pays only for what it consumes.
- A bank or hospital keeps sensitive data on a private cloud for control and compliance.
- Organizations with a shared mission, such as universities, share a community cloud.
- A hybrid cloud keeps core data private and bursts peak load to the public cloud.
Administering & Monitoring cloud services
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Medium weight</span>
Definition. <mark>Cloud administration is the day-to-day management of users, resources, security and billing, and cloud monitoring is the continuous collection of metrics, logs and alerts to check that services meet their SLA.</mark>
Key points.
- Administration covers user and role management (IAM), provisioning, patching, backup, security policies and billing control.
- Monitoring collects metrics such as CPU utilization, memory, disk I/O, network traffic, response time and error rate.
- Alerts fire when a metric crosses a threshold, so the admin or an auto-scaling rule can react before users notice.
- Monitoring supports cost control, because idle or oversized resources become visible.
- It proves SLA compliance (uptime, latency) and supplies data for capacity planning and auto-scaling.
- Tools include Amazon CloudWatch, Azure Monitor, Google Cloud Monitoring, Nagios and Prometheus.
Answer frame. Open with the definitions of monitoring and optimization; then list metrics and tools; then importance (cost, SLA, performance, auto-scaling); then optimization techniques from the next topic but one; close that the two together keep a cloud cheap and reliable.
Asked: [7 marks] (Jun 2025) Discuss the importance of monitoring and resource optimization in cloud services.
Load balancing
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Low weight</span>
Definition. <mark>Load balancing is the distribution of incoming requests or workload across several servers or VMs so that no single resource is overloaded.</mark>
Diagram. <figure class="ds-fig" style="margin:1.4rem 0;overflow-x:auto"><svg xmlns="http://www.w3.org/2000/svg" id="dsfig-u3-01" viewBox="0 0 389.6 286.4" width="389.6" height="286.4" role="img" aria-label="Users send requests to the load balancer (LB), which forwards them to servers S1 to S3"><style>#dsfig-u3-01 .e{stroke:#454C5A;stroke-width:1.4;fill:none}#dsfig-u3-01 .e.hi{stroke:#2340B8;stroke-width:2.6}#dsfig-u3-01 .n{fill:#FFFFFF;stroke:#16181D;stroke-width:1.4}#dsfig-u3-01 .n.hi{fill:#E3E9FC;stroke:#2340B8;stroke-width:2.2}#dsfig-u3-01 .n.rb-b{fill:#16181D;stroke:#16181D}#dsfig-u3-01 .n.rb-r{fill:#BD3227;stroke:#BD3227}#dsfig-u3-01 text{font-family:"JetBrains Mono",ui-monospace,Menlo,Consolas,monospace;font-size:13px}#dsfig-u3-01 .t{fill:#16181D;font-weight:500}#dsfig-u3-01 .t.inv{fill:#FFFFFF;font-weight:700}#dsfig-u3-01 .kd{stroke:#16181D;stroke-width:1.2}#dsfig-u3-01 .dot{fill:#16181D}#dsfig-u3-01 .ann{fill:#2340B8;font-size:11px;font-weight:700}#dsfig-u3-01 .lbl{fill:#6F7787;font-family:system-ui,-apple-system,sans-serif;font-size:12px;font-weight:700}#dsfig-u3-01 .ptr{fill:#2340B8;font-size:12px;font-weight:700}#dsfig-u3-01 .ah{fill:#454C5A}#dsfig-u3-01 .ah.hi{fill:#2340B8}#dsfig-u3-01 .wl rect{fill:#FFFFFF;stroke:#DCE0E7}#dsfig-u3-01 .wl .t{font-size:12px;font-weight:700}#dsfig-u3-01 .wl.hi rect{fill:#2340B8;stroke:#2340B8}#dsfig-u3-01 .wl.hi .t{fill:#FFFFFF}html.dark #dsfig-u3-01 .e{stroke:#B1B7C3}html.dark #dsfig-u3-01 .e.hi{stroke:#8FA3FF}html.dark #dsfig-u3-01 .n{fill:#161920;stroke:#E6E8ED}html.dark #dsfig-u3-01 .n.hi{fill:#1E2748;stroke:#8FA3FF}html.dark #dsfig-u3-01 .n.rb-b{fill:#E6E8ED;stroke:#E6E8ED}html.dark #dsfig-u3-01 .n.rb-r{fill:#FF7E71;stroke:#FF7E71}html.dark #dsfig-u3-01 .t{fill:#E6E8ED}html.dark #dsfig-u3-01 .t.inv{fill:#0F1115}html.dark #dsfig-u3-01 .kd{stroke:#E6E8ED}html.dark #dsfig-u3-01 .dot{fill:#E6E8ED}html.dark #dsfig-u3-01 .ann{fill:#8FA3FF}html.dark #dsfig-u3-01 .lbl{fill:#858D9C}html.dark #dsfig-u3-01 .ptr{fill:#8FA3FF}html.dark #dsfig-u3-01 .ah{fill:#B1B7C3}html.dark #dsfig-u3-01 .ah.hi{fill:#8FA3FF}html.dark #dsfig-u3-01 .wl rect{fill:#161920;stroke:#2A2E37}html.dark #dsfig-u3-01 .wl.hi rect{fill:#8FA3FF;stroke:#8FA3FF}html.dark #dsfig-u3-01 .wl.hi .t{fill:#0F1115}</style><defs><marker id="ah3" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah" d="M0,1 L9,5 L0,9 z"/></marker><marker id="ahh3" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah hi" d="M0,1 L9,5 L0,9 z"/></marker></defs><path class="e" d="M59,143.2 L165.2,143.2" marker-end="url(#ah3)"/><path class="e" d="M202.3,133.1 L331.8,51.2" marker-end="url(#ah3)"/><path class="e" d="M205.2,143.2 L328.6,143.2" marker-end="url(#ah3)"/><path class="e" d="M202.3,153.3 L331.8,235.2" marker-end="url(#ah3)"/><circle class="n" cx="40" cy="143.2" r="18"/><text class="t" x="40" y="143.2" dy=".35em" text-anchor="middle">U</text><circle class="n" cx="186.2" cy="143.2" r="18"/><text class="t" x="186.2" y="143.2" dy=".35em" text-anchor="middle">LB</text><circle class="n" cx="349.6" cy="40" r="18"/><text class="t" x="349.6" y="40" dy=".35em" text-anchor="middle">S1</text><circle class="n" cx="349.6" cy="143.2" r="18"/><text class="t" x="349.6" y="143.2" dy=".35em" text-anchor="middle">S2</text><circle class="n" cx="349.6" cy="246.4" r="18"/><text class="t" x="349.6" y="246.4" dy=".35em" text-anchor="middle">S3</text></svg><figcaption style="font-size:.82em;opacity:.72;margin-top:.45rem">Users send requests to the load balancer (LB), which forwards them to servers S1 to S3</figcaption></figure>
Key points.
- Importance: it raises availability, since a failed server is removed from rotation and others carry on.
- It improves performance by keeping response time low and avoiding hot spots.
- It gives fault tolerance and better resource utilization, and lets the cloud scale out easily.
- Static algorithms use fixed rules and ignore the current server state, for example round-robin and weighted round-robin.
- Dynamic algorithms use live server state, for example least-connection, least response time and content-aware (layer 7) routing.
- Round-robin sends requests to servers in turn; weighted gives stronger servers a larger share; least-connection picks the server with fewest active connections.
Answer frame. Open with the definition and need; draw the LB diagram; give importance points 1-3; then a static versus dynamic classification with the examples; close that the choice depends on how uniform the load is.
Asked: [7 marks] (Jun 2025) Explain the importance of load balancing in cloud computing and the different load-balancing techniques.
Resource optimization
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Medium weight</span>
Definition. <mark>Resource optimization is using the least cloud resources and cost that still meet the performance and SLA targets.</mark>
Key points.
- Right-sizing matches VM size to real usage, removing waste from oversized instances.
- Auto-scaling adds instances at peak and removes them when demand falls.
- Scheduling and consolidation place workloads on fewer hosts so idle servers can be switched off.
- Load balancing spreads work evenly, and reserved or spot pricing lowers cost.
- Utilization is measured as $\text{Utilization} = \dfrac{\text{used capacity}}{\text{total capacity}} \times 100$.
Answer frame. Give this as the second half of the monitoring answer: definition, the four techniques, then cost and SLA benefit.
Resource dynamic reconfiguration
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Low weight</span>
Definition. <mark>Dynamic reconfiguration changes the resources assigned to a running service, by adding, removing or resizing them on demand, without stopping it.</mark>
| Basis | Static provisioning | Dynamic provisioning |
|---|---|---|
| Meaning | Fixed resources allocated in advance | Resources allocated on demand at run time |
| Scalability | Poor, limited to the planned capacity | High, elastic scale up and down |
| Cost | Pay for peak capacity even when idle | Pay only for what is used |
| Utilization | Low, wasted at off-peak, or overloaded at peaks | High, matches demand |
| Effort | Manual planning and change | Automatic through policies and auto-scaling |
| Example | Buying a server sized for the peak | AWS Auto Scaling group adding VMs |
Answer frame. Define both terms first; give the table of 6 rows; close with an example and that dynamic provisioning suits variable load.
Asked: [7 marks] (Dec 2024) Distinguish between static resource provisioning and dynamic resource provisioning.
Implementing real time application
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. A real-time cloud application processes data and responds within a strict time limit, as in video streaming, online gaming or live tracking.
Key points.
- Low latency is the main need, so servers are placed near users through regions and CDNs.
- Streaming and message services such as Kafka or Kinesis carry data continuously.
- Auto-scaling and load balancing keep response time steady under load.
- Monitoring of latency and jitter, plus redundancy, protects the deadline.
Mobile Cloud Computing
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Medium weight</span>
Definition. <mark>Mobile Cloud Computing (MCC) lets mobile devices offload storage and heavy computation to cloud servers over the network, overcoming the limits of battery, CPU and memory.</mark>
Key points.
- Offloading moves heavy tasks such as image processing or speech recognition to the cloud and returns only the result.
- It saves battery and storage on resource-constrained phones.
- Data sync lets users reach the same files from any device, as in Google Drive.
- Mobile apps such as maps, banking, m-learning and health apps rely on it.
- Its challenges are network latency, bandwidth, security and intermittent connectivity.
Edge computing
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Medium weight</span>
Definition. <mark>Edge computing processes data close to where it is produced, on local devices or nearby edge servers, instead of sending it all to a distant central cloud.</mark>
Key points.
- Low latency: processing near the source gives fast responses.
- It suits IoT and real-time work such as smart cities, autonomous vehicles and industrial sensors.
- It saves bandwidth, as only filtered results go to the cloud.
- It keeps working during poor connectivity and keeps sensitive data local.
- Fog computing and Multi-access Edge Computing (MEC) are related forms placed between device and cloud.
Answer frame. Define MCC and edge; give the role of MCC (offloading, resource limits), then the role of edge (latency, IoT), then use-cases such as maps, smart traffic and video analytics; close that both bring the cloud closer to the user.
Asked: [7 marks] (Jun 2025) Discuss the role of Mobile Cloud Computing and Edge Computing in modern IT solutions.
Last-minute revision
- Load balancing spreads requests across servers to avoid overload.
- Static algorithms: round-robin, weighted; dynamic: least-connection, least response time, content-aware.
- Static provisioning is fixed and paid at peak; dynamic is elastic and pay-per-use.
- Monitoring metrics: CPU, memory, disk, network, latency, error rate.
- Monitoring tools: CloudWatch, Azure Monitor, Nagios, Prometheus.
- Optimization: right-sizing, auto-scaling, scheduling.
- MCC offloads computation from phones to the cloud.
- Edge computes near the source for low latency.
- Hybrid cloud bursts peak load to the public cloud.
Memory hooks
- LB = "Lots of servers, Balanced": RR, WRR, LC.
- Static = stone (fixed); dynamic = water (flows with demand).
- MCC = phone lifts weight to cloud; Edge = cloud comes down to the device.
- Monitor, Alert, Scale, Save.
Coverage checklist
- Organizational scenarios of clouds: no past questions.
- Administering & Monitoring cloud services: Jun 2025 monitoring and optimization question.
- Load balancing: Jun 2025 importance and techniques.
- Resource optimization: covered with the Jun 2025 monitoring question.
- Resource dynamic reconfiguration: Dec 2024 static versus dynamic provisioning.
- Implementing real time application: no past questions.
- Mobile Cloud Computing: Jun 2025 MCC and edge question.
- Edge computing: Jun 2025 MCC and edge question.