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Server-Storage Virtualization: Integration and Load Balancing in Data Centers. Aameek Singh, Madhukar Korupolu (IBM Almaden) Dushmanta Mohapatra (Georgia Tech). Overview. Motivation Virtualization is common in datacenters Both compute and storage New degrees of freedom for load balancing

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server storage virtualization integration and load balancing in data centers

Server-Storage Virtualization: Integration and Load Balancing in Data Centers

Aameek Singh, Madhukar Korupolu (IBM Almaden)Dushmanta Mohapatra (Georgia Tech)

  • Motivation
    • Virtualization is common in datacenters
      • Both compute and storage
    • New degrees of freedom for load balancing
    • Integrating compute & storage mgmt is important
    • Multiple resource dimensions complicate solution
    • Hierarchical data flows must be considered
  • A system for virtual server and storage monitoring and control
    • Monitoring & migration are off-the-shelf
  • Employs VectorDot, a heuristic load balancing algorithm for load balancing systems with multidimensional and hierarchical constraints
    • Inspired by Toyoda method for solving multidimensional knapsack problem
harmony overview
Harmony overview

Servers and Storage Mgmt

Trigger Detection

Configuration and Performance Manager

Server Virtualization Mgmt

Optimization Planning (VectorDot)

Virtualization Orchestrator

Storage Virtualization Mgmt



load balancing input
Load balancing input
  • Record system state in utilization, capacities and thresholds
  • Any node which has any utilization above threshold is called a trigger
multidimensionality and hierarchy constraints
Multidimensionality and hierarchy constraints
  • Vitems, nodes w/ multidimensional resources
    • E.g., VM requires 100MHz CPU, 50 MB RAM, 0.5Mbps network, 0.2 Mbps of storage IO
    • Server with 2GHz of CPU, 512 MB RAM, 2Mbps network, 2Mbps storage
  • VMs also use switch resources, determined by paths to the root switch
    • Path vectors encode path from node to the root
      • What if a flow doesn’t go all the way to the root?
node load and virtual item fraction vectors
Node load and virtualitem fraction vectors
  • Usage fraction & threshold for each resource
    • For a server:
      • <cpuU/cpuCap, memU/memCap, netU/netCap, ioU/ioCap>,<cpuT, memT, netT, ioT>
    • For storage node:
      • <spaceU/spaceCap, ioU/ioCap>, <spaceT, ioT>
    • For switch:
      • <ioU/ioCap>, <ioT>
  • Requirements for VMs and vdisks
    • VM: <cpuU, memU, netU, ioU>
    • Vdisk: <spaceU, ioU>
imbalance scores
Imbalance scores
  • Imbalance score penalizes nodes for being above threshold
  • IBscore(f, T) = 0 if f < T, e^(f – T)/T otherwise
    • Exponential weighting penalizes nodes which are further over threshold
    • E.g., distinguish between (3T, T) and (2T, 2T)
  • Sum scores over all dimensions and all nodes
path vectors
Path vectors
  • FlowPath(u) for a node is the path from node to the storage virtualizer
  • Score of mapping virtual items to nodes
  • Start with simple dot product of the PathLoadFracVec(u) (Au) and the ItemPathLoadFracVec(vi, u) (Bu(vi))
    • Example:
      • Au = <0.4, 0.2, 0.4, 0.2, 0.2>
      • Aw = <0.2, 0.4, 0.2, 0.4, 0.2>
      • Bu(vi) = Bv(vi) = <0.2, 0.05, 0.2, 0.05, 0.2>
      • Au . Bu(vi) < Av . Bv(vi), so assign vi to u
extended vector product evp
Extended vector product (EVP)
  • Extensions to account for thresholds, imbalance scores, and avoid oscillations
  • First: Smooth PathLoadFracVec(u) with respect to PathThresholdVec(u)
    • Similar to exponential penalization of imbalance
    • E.g., component at utilization 0.6 with threshold of 0.4 gets higher value than 0.6 when threshold is 0.8
  • Second: Avoid oscillations by considering post-move load vectors
using evp
Using EVP
  • Identify trigger nodes: those whose load fraction exceeds the threshold along any dim
    • Search among trigger nodes in descending IBScore order
  • Consider four selection criteria for search, traversing destination nodes in static order (i.e., by name)
    • FirstFit
    • BestFit
    • WorstFit
    • RelaxedBestFit
      • Visit nodes in random order until N feasible nodes are found, then choose that with minimum EVP
migration overheads
Migration overheads
  • Simple experiment: live migration of VM running PostMark benchmark, and its vdisk
  • Migration incurs some overhead
evaluation simulation
Evaluation: Simulation
  • Built a simulator to generate topologies and system and node configurations
  • Simple ratios between # of components
    • E.g., 500 vms, 1 disk per vm mapped onto 100 physical hosts, 33 storage nodes, 10 edge switches 4 core switches
    • No details on what these ratios are besides example
  • Load capacities and resource requirements from Normal distributions
    • No details on parameters, other than the default for α and β are 0.55, although they claim to vary them…
  • Generate vms, vdisks, servers, switches and storage nodes, do initial mapping, then balance with VectorDot
results imbalance
Results: Imbalance
  • BestFit and RelaxedBestFit achieve low imbalance scores
results moves from initial state
Results: Moves from initial state
  • BestFit and RelaxedBestFit require fewest moves to reach balance
  • At no point does the # of triggers or imbalance score increase
results running time
Results: Running time
  • Basic allocation 35 seconds, max
  • Better initial placement = faster load balancing

Initial placement + load balancing

Time for initial placement

evaluation real data center
Evaluation: Real data center
  • Figure 1
    • 3 servers, 3 switches, 3 storage nodes
    • 6 vms, 6 storage volumes
    • Disabled caching?
  • Workload generators – lookbusy, IOMeter
  • Virtual server + virtual storage load balancing together
  • Harmony: System for monitoring, planning, and executing server & storage load balancing
    • They just use off the shelf software…
  • VectorDot: Heuristics for multidimensional and hierarchical load balancing
    • Does this generalize back to other problems?
  • Evaluation w/ simulated & “real” datacenters
    • “Real” evaluation seems too dinky