Cloud Resource Scheduling for Online and Batch Applications Kick-off meeting

Project Goal Develop a resource management system that ◦ Deploy different types of jobs to servers. ◦ Dynamically adjust the resource allocation according to job workloads. ◦ Meet the Service Level Agreement(SLA) of latency-sensitive jobs. ◦ Minimize the cost.  Penalty of violating SLA

Type of Job Interactive job ◦ Latency-sensitive ◦ State-less ◦ Strict SLA Batch job ◦ Consists of many (independent) tasks. ◦ Soft deadline.

Example YouTube ◦ Interactive: video streaming ◦ Batch: flow analysis Phone billing system ◦ Interactive: rate querying/changing ◦ Batch: calculating the phone bill per user.

Cost “Penalty” ◦ The price we have to pay for violating the SLA. Each job has different penalty rate. ◦ P( J a ) = penalty rate( J a ) * max(0, v), J a ∈ I  v: percentage of SLA violation within a time window ◦ P( J b ) = penalty rate ( J b ) * max(0, d), J b ∈ B  d: difference between job completion time and deadline.

Problem Definition Given a set of batch job B, a set of interactive job I, the number of processors m, and a penalty C. Is there a schedule to run all jobs with the total penalty no more than C? ◦ NP-complete ◦ Design heuristics to obtain schedule with reasonable quality.

Processor Allocation Estimate the penalty of interactive jobs on different number of processors. Estimate the penalty of batch jobs on different number of processors. Determine the number of processors for each job. ◦ With limit number of processors, find the assignment that minimize the total penalty.

System Architecture

Implement the scheduling algorithm and evaluate the result. Build the system on Docker / implement the scheduler in Kubernetes. Consider other resources. ◦ Memory, network…etc. To Do