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 ArchitectureApplication

Master

SDN Controller

Cross-Layer Scheduling

Algorithm: Pre-computation

• Pre-compute all-pairs (, k-shortest paths– Stored in Topology-Map hash-table with key=(, ,

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