Resource Provision for Batch and Interactive Workloads in Data Centers Ting-Wei Chang, Pangfeng Liu Department of Computer Science and Information Engineering, National Taiwan University Graduate Institute of Networking and Multimedia, Nation Taiwan University Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer Science and Information Engineering, National Taiwan University Jan-Jan Wu Institute of Information Science, Academia Sinica Research Center for Information Technology Innovation, Academia Sinica Chia-Chun Shih, Chao-Wen Huang Chunghwa Telecom Laboratories

Agenda Introduction Problem Definition Resource Provisioning Algorithm Evaluation Conclusion

Motivation Private cloud has limited amount of hardware resources. ◦ Fixed amount of servers for most of the time. Applications have varying characteristics and SLAs. ◦ SLA: service level agreement ◦ Insufficient resources allocation leads to SLA violation, which incurs penalty.

Goal Dynamically adjust the computing resources for different types of applications, such that the penalty is minimized. ◦ Penalty incurred by SLA violation. ◦ Private cloud, where hardware resources are considered to be fixed and limited.

Application Type – Batch Job A set of independent computation- intensive tasks with the similar execution time. ◦ [SLA]: finish within a (soft) deadline.

Application Type – Interactive Job Interactive job ◦ Long-running application that serves requests from users.  State-less ◦ [SLA]: response within a threshold.

Penalty of Jobs The penalty of a job j with m processing units: ◦ r : the penalty rate. ◦ v: the amount of SLA violation

SLA Violation For each job j  c(m): the completion time  d: the (soft) deadline  E: the expected fraction of satisfying requests.  f(m): the actual fraction of satisfying responses

Contribution Design a framework that allocate resources to batch and interactive jobs. Provide theoretical analyses on the expected penalty of jobs. Propose a heuristic algorithm that minimizes the total penalties.

Resource Provisioning Problem Given a set of batch jobs B, a set of interactive jobs I, the number of processing units M, and a penalty value C. Is there a schedule to run all jobs with the total penalty incurred before all batch jobs complete no more than C? ◦ Each job has a penalty rate r and corresponding SLA requirement.

Finding A Solution Given total M processing units. ◦ Dynamically determine M i and M b that minimize the total penalty. MiMi MbMb

Heuristic Estimate the penalty of interactive jobs. Estimate the penalty of batch jobs. Determine M i and M b. ◦ The number of processing units assigned to interactive and batch jobs.

Penalty Estimation – Interactive Jobs Given k interactive job For any given M i : Determine m 1 ~ m k that minimize …… m1m1 m2m2 mkmk

Minimizing Penalty of Interactive Jobs Compute the minimum penalty of all interactive jobs using dynamic programming. ◦ Define D(j, m) as the minimum penalty of running the first j jobs with m processing units. Minimum penalty:

Penalty Estimation – Batch Jobs Given k batch job For any given M b : b2b2 b2b2 b3b3 b3b3 b3b3 b1b1 Time

Job Execution Order and Scheduling Execution order: Greedy ◦ Select the job with the least effects to other unselected jobs until all jobs are selected. Scheduling: ◦ An available processing unit pick a task from the sorted job list for execution. Compute penalty

Determine M i and M b Penalty of interactive jobs: Penalty of batch jobs: Minimize penalty P:

Evaluation Environment Hardware ◦ Managing nodes and four worker nodes. ◦ Each worker node has 16 processing units. Workload trace ◦ Batch job: Samples from SDSC-Par96 trace log. ◦ Interactive job: ◦ Samples form Calgary-HTTP and Saskatchewan-HTTP trace log.

Evaluation Conducted two sets of experiments ◦ Batch jobs  Compares the average penalty of our greedy algorithm against other methods. ◦ Batch and interactive jobs  Compares the SLA violation penalty.

Penalty among Different Methods Apply different methods to schedule batch jobs, and compare the total penalty. ◦ Compare our Greedy method with Earliest Deadline First strategy(EDF), Least Slack Time First strategy (LST), Least Slack Time Rate First strategy (LSTR), and Highest Penalty Rate First strategy (HPRF)

Penalty of Mixed Jobs Apply different methods to determine M i and M b, and compare the total penalty. ◦ Compare our dynamic programming(DP) with static fraction(SF) and penalty proportion(PP).

Conclusion We propose a heuristic algorithm that minimize the total SLA violation penalty of different types of applications. ◦ Batch and interactive jobs. The experimental results suggest that our system effectively reduces total penalty by allocating proper amount of resources to heterogeneous jobs.

Thank you!

Penalty of Batch Job Penalty of a batch job: ◦ r : the penalty rate. ◦ c(m) : the completion time with m processing units. ◦ d :the (soft) deadline.

Penalty of Interactive Job Penalty of a interactive job: ◦ r : the penalty rate. ◦ E : the expected fraction of satisfying requests. ◦ f(m) : the fraction of satisfying requests when given m processing units.

Penalty Estimation – Interactive Jobs Penalty of an interactive job j: ◦ f(j, m) : the fraction of satisfying requests of job j when given m processing units. Estimate P(j, m) by computing f(j, m) for each j and m using queuing theory.

Penalty Estimation – Batch Jobs Penalty of an batch job i: ◦ c(i,m,s): the completion time of job i with m processing units. ◦ s: the starting time of job i.

Accuracy on Penalty Estimation Compute the ratio between P t and the actual final penalty. ◦ P t : the estimated penalty of all jobs during execution ◦ Converges quickly.