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 dont 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 providers 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 requests 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 EC2s 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: T k : requests submitted to site k P j : the runtime of request j m j : the number of VMs required by request j ct j : request js completion time st j : 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 AWRT base is the AWRT achieved by a base strategy(FCFS with aggressive backfilling) that schedules requests using only the site's resources AWRT st 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 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

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 sizeRequests arrive timeRequests 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_spent st: amount spent with Cloud resources Viol base : the number of deadline violations under the base strategy set (aggressive backfilling and an Earliest Deadline First manner) Viol st :the number of deadline violations under the evaluated strategy set Cost of Reducing Deadline Violations

The deadline calculation is given by: Where: st j : the request j's submission time ct j : the completion time. ta j : 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=0.9sf=1.3 sf=1.7

Cost of Reducing Deadline Violations Tight deadlinesNormal deadlinesRelaxed deadlines

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