Cloud Scheduling

Cloud Scheduling
Dynamic Request Allocation with
Respect to Context and SLA
Charles Snyder
The Problem
• Applications use some “context” data
▫ Accessed by context id
▫ Burst access is common
▫ Minimize data migration time
• Profit determined by SLA conformance
▫ User classes with different SLA’s
▫ Some % of requests completed on time
▫ Profit only charged if SLA is met
• How do we maximize profit?
System Model
Simple Solutions
• Static Allocation
▫ Each context maps to a particular machine
▫ Can cause build-up
• Scheduling
 Complete tasks quickly
▫ Weighted Round Robin (WRR)
 Complete high-profit tasks first
• Divide execution into subintervals with 2 steps:
• Adaptation
▫ Compute global SLA compliance levels
• Allocation
▫ Find the best-suited server
 Appropriate context
 Appropriate service-endpoint
 Least loaded
▫ Servers schedule their own queues
• Each data center sends interval data to all others
▫ Authors assume communication time is negligible
• All data centers compute:
for (each class of user)
current compliance =
prev compliance * prev # serviced + # on time
total # serviced
Before Allocation
• Quantify risk-reward for SLA
▫ Profit Score
• For a given user class:
if (current compliance < SLA compliance)
profit score = profit / (SLA compliance – current compliance)
profit score = 0
• Servers keep lookup table for loaded contexts
and service-endpoints
for (each server with context)
if (server is “compatible” with task)
allocate task
for (each server with service-endpoint)
if (server is “compatible” with task)
allocate task
allocate task to least loaded server
Compatibility Check
add new request to queue
sort queue by profit score
sort queue by deadlines
create approximate schedule
average position in both sorts
if (deadline of new request will be met)
Server Scheduling
• gi-FIFO
• Pick user class with highest profit score
• Modified FIFO
▫ Pick task with longest wait that can be completed
on time
▫ Otherwise, FIFO
Static vs Dynamic Allocation
• K. Boloor, R. Chirkova, Y. Viniotis, and T. Salo.
“Dynamic request allocation and scheduling for
context aware applications subject to a
percentille response time SLA in a distributed
cloud.” Proc. IEEE 2nd Int. Conf. on Cloud
Computing Technology and Science, pp. 464472, 2010.

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