Evaluating the Cost-Benefit of Using Cloud Computing to

Extend the Capacity of Clusters

Presenter: Xiaoyu Sun

Cluster Computing

Users have to know cluster very well

administrative privileges

What is Cloud Computing?

Cloud computing provides computation, software, data access, and storage resources without requiring cloud users to know the location and other details of the computing infrastructure.

Empowerment Users control resource by themselves not by a centralized IT service

Agility users' ability to re-provision technological infrastructure resources.

Application Programing Interface Cost Device and Location Independence

enable users to access systems using a web browser regardless of their location or what device they are using

Virtualization servers and storage devices to be shared and utilization be increased

Reliability and Scalability Performance

Monitor by web services as the system interface Security

providers are able to devote resources to solving security issues that many customers cannot afford Maintenance

Applications don’t need to be installed on each user's computer and can be accessed from different places

Characteristics of Cloud Computing

Describe a system that enables an organization to augment its computing infrastructure by allocating resources from a Cloud provider.

Provide various scheduling strategies that aim to minimize the cost of utilizing resources from the Cloud provider.

Evaluate the proposed strategies, considering different performance metrics; namely average weighted response time, job slowdown, number of deadline violations, number of jobs rejected, and the money spent for using the Cloud.

Purpose

Cloud Computing

Figure 1:The resource provisioning scenario

Strategy sets

Conservative each request is scheduled when it arrives in the system, and requests are

allowed to jump ahead in the queue if they do not delay the execution of other requests.

Aggressive Only the request at the head of the waiting queue called the pivot is granted

a reservation. Other requests are allowed to move ahead in the queue if they do not delay the pivot.

Selective Requests are given reservations if they have waited long enough in the queue.

Long enough is determined by the requests’ expansion factor:

Xfactor = (wait time + run time)/run time (1) The threshold is given by the average slowdown of previously completed requests.

Backfilling Policies

Naïve: Both site and cloud schedulers use

Conservative backfilling to schedule the requests

The redirection algorithm is executed at the arrival of each job at the site

Use cloud provider when the request cannot start immediately on local cluster

Strategy Sets

Shortest Queue: Aggressive backfilling First-Come-First-Served (FCFS) manner At the arrival or complete of each job at the

site Compute the ratio of number of VMs required

by requests to the number of VMS available Redirect request if cloud provider’s number is

smaller

Strategy Sets

Weighted Queue: Aggressive backfilling First-Come-First-Served (FCFS) manner Number of VMs that can be borrowed from

cloud provider is the number of VMs required by requests minus VMs in use

Strategy Sets

Selective Selective backfilling Compute the ratio of number of VMs required

by requests to the number of VMS available When the request’s xFactor exceeds the

threshold, the scheduler makes a reservation at the place that provides the earliest start time.

Strategy Sets

Simulation of two-month-long periods SDSC Blue Horizon machine with 144 nodes

Number of VMs Price of a virtual machine per hour

Amazon EC2’s small instance: US $0.10 Network and storage are not considered

Values are average of 5 simulation runs

Experiments

Average Weighted Response Time(AWRT) of site k:

Tk: requests submitted to site k Pj: the runtime of request j mj: the number of VMs required by request j ctj: request j’s completion time stj: the submission time of request j

Performance Metrics

Performance Improvement Cost of a strategy set st:

Amount spent is the amount spent running virtual machines on the Cloud provider AWRTbase is the AWRT achieved by a base strategy(FCFS with aggressive

backfilling) that schedules requests using only the site's resources AWRTst is the AWRT reached by the strategy st when Cloud resources are also

utilized.

Performance Metrics

Using Lublin99's model to generate different workloads:

Umed: the mean number of virtual machines required by a request to log2m-umed where m is the maximum number of virtual machines allowed in the system, from 1.5 to 3.5.

Barr: the inter-arrival time of requests at rush hours, from 0.45 to 0.55 .

PB: the proportion p of the first gamma in Lublin99's model is given by p = pa * nodes + PB, from 0.5 to 1.0.

Performance Improvement Cost

Performance Improvement Cost

These three graphs show the site's utilization using the base aggressive backfilling strategy without Cloud resources

The larger the value of Umed, the smaller the requests.

The larger the value of PB, the smaller the duration of the requests

Performance Improvement Cost

Requests’ size Requests’ arrive time

Requests’ duration

Users may have stringent requirement on when the virtual machines are required

Deadline constrained requests have: Ready time Duration Deadline

Cost of using Cloud resources used to meet requests’ deadlines and decrease the number of deadline violations and request rejections

Deadline Constrained Applications

Conservative both local site and Cloud schedule requests using conservative

backfilling. Places a request where it achieves the best start time If rejections are allowed and deadline cannot be met, reject the

request Aggressive

both local site and Cloud use aggressive backfilling to schedule requests

Earliest Deadline First If request deadlines are broken in the local cluster, try the cloud

provider If rejections are allowed and deadlines are broken, reject the request

Deadline Aware Strategies

The non-violation cost is given by:

Where: Amount_spentst: amount spent with Cloud resources Violbase: the number of deadline violations under the

base strategy set (aggressive backfilling and an Earliest Deadline First manner)

Violst:the number of deadline violations under the evaluated strategy set

Cost of Reducing Deadline Violations

The deadline calculation is given by:

Where: stj: the request j's submission time ctj: the completion time. taj: the difference between the request's completion and submission

times. sf : a stringency factor that indicates how urgent the deadlines are.

Deadline calculation

Cost of Reducing Deadline Violations

sf=0.9 sf=1.3 sf=1.7

Cost of Reducing Deadline Violations

Tight deadlines

Normal deadlines

Relaxed deadlines

Cost to Reduce Job Rejections: Aggressive Strategy Set

Cost to Reduce Job Rejections: Aggressive Strategy Set

Different strategy sets can yield different ratios of performance improvement to money spent Naïve strategy has a higher performance

improvement cost Selective strategy provides a good ratio of

money spent to job slowdown improvement Using cloud provider to meet job deadlines

Less than $3,000 were spent to keep the number of rejections close to zero

Conclusions