Progress Report9/9

CHT ProjectDevelop a resource management

scheduling algorithm for CHT datacenter.◦Two types of jobs, interactive/latency-sensitive and batch/computation-intensive.

◦Meet the SLA of jobs while maximizing the server utilization.Minimize SLA violation with limited resources. # of servers

Target PlatformCHT datacenter

◦A number of heterogeneous servers Red Hat Openshift

ImplementationMonitor, controller/decision

maker◦Stand-alone modules

Scheduler◦Openshift plug-in

方案 1 方案 2 方案 3

實作方式

Monitor 使用 Openshift 內建機制，並使用 JVM console 等方式讀出。

第三方 monitor 工具 自行開發 (AP/OS level)

Scheduler

實作本計畫演算法 實作本計畫演算法 以 Plugin 方式介接Openshift

Controller

透過 OS API 調整系統設定 透過 OS API 調整系統設定 使用 Openshift 內建機制

是否需修改 Openshift 原始碼

不需修改 不需修改 不需修改 ( 替換原scheduler)

優點 1. 實作簡單，只需實作scheduler.

2. 不須改動 Openshift 原先 scheduler.

1. 不須改動 Openshift 原先 scheduler.

2. 可自行選用不同的monitor 工具

3. 不須改動 Openshift 原先 scheduler.

1. 是 Openshift 內建的 Scheduler 介接方式 (CHT)

2. 介接方式不複雜 (CHT)

缺點 1. 1. 尚不確定 Service

Layer 及 Routing

layer 有合適的 API 可以使用 (CHT)

1. 尚不確定 Service Layer

及 Routing layer 有合適的 API 可以使用 (CHT)

1. Plugin 需使用 go 語言開發 (CHT)

IdeaOne interactive task per server.

Spare resource monitoring.◦Monitor the status of interactive

tasks on a server.◦Deploy batch tasks to servers with

spare resources.◦Separate the tasks from the same

job in order to avoid resource contention.

Pseudo Code 1. findServer(newContainer): 2. foreach server in serverList: 3. if( server.canHost(newContainer) ): 4. candidate <- server 5. 6. foreach server in candidate: 7. compute server.Score(newContainer) 8. 9. target = maxScore(candidate) 10. return target ## Deploy newContainer to target

server

Some IssuesMeasuring the loading of

interactive jobs◦CHT will try to provide agents in

container that return the (critical) resource usage of the APs.

Size of container◦Fixed for the same job.◦Varies among different jobs.

Some Issues(Cont.)Candidate while choosing server

◦May encounter restrictions such as # of license.

Score function while choosing server◦Assign tasks/containers to the server

with the highest score.◦Score = w1*activeServer +

w2*spareResource + w3*(-#otherInJobConatiner) + w4*…

Some Issues(Cont.)Hardware ability (by Prof. Lin)

◦Using the loading may be problematic. Example:

Assume α CPU with computing ability S, while β CPU with 2 S. Host OS consumes 0.25 S. The container consumes S to meet QoS. If we deploy the same container to α and β, the loading (from the container perspective) may be 75% vs 50%, while the container on α violating the QoS (insufficient resource).

◦Not sure, will verify.

NextKeep working