Cross-Layer Scheduling in Cloud
Computing Systems
Authors: Hilfi Alkaff, Indranil Gupta
Motivation
• Many cloud computing frameworks out there
– Batch Processing Framework: Hadoop
– Stream Processing Framework: Storm
• Current applications are not aware of
underlying network topology
– Might schedule tasks on machines with low
bandwidth.
Challenges
• Need to expose underlying network topology
efficiently to applications
• Huge state space to search
– Thousands of machines in a cluster
– Users demand more interactive jobs
• Multiple possible data-path representation
– Want to have generic schedulers
Data-Path: Map-Reduce
Data-Path: Stream
Proposed Solution
• Cross-Layer Scheduling Framework
– First-level scheduler in application Level
– Second-level scheduler in routing level
• Use Simulated Annealing at each level
– Probabilistic framework
– Idea: If neighboring state is better, always move
there but if it is not, move there with probability
P(T) that decreases with time
Proposed Architecture
Application
Master
M1
SDN
Controller
R1
R3
R2
M2
M3
Algorithm: Pre-computation
• Pre-compute all-pairs (𝑀1 , 𝑀2 ) k-shortest
paths
– Stored in Topology-Map hash-table with key=(𝑀1 ,
𝑀2 ), value=array of k-shortest paths
• Too many duplicates
– Intelligently merge similar sub-paths
– Hash-Table’s value is now a tree instead of array
Algorithm: Main
Algorithm: genState() Heuristic
• Too many neighboring states
– Not possible to traverse all of them
• Application Level
– Prefer node that has higher # of sink vertices
– Prefer node that has higher # of source vertices
• Routing Level
– Prefer paths that have lower number of hops
– Prefer paths that have higher amount of available
bandwidth
Emulab Result: Throughput
Simulation Result: Computation Time
Simulation Results: CDF
Le Questions?
Algorithm: Failures
• Link-Failures
– Need to re-allocate flows using that link
– Keep a separate hash-table where key=edge,
value=flows
– Get another path from Topology-Map.
• Machine-failures
– Re-run main algorithm on