Server storage virtualization integration and load balancing in data centers
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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)

Overview Balancing in Data Centers

  • 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

Harmony Balancing in Data Centers

  • 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 Balancing in Data Centers

Servers and Storage Mgmt

Trigger Detection

Configuration and Performance Manager

Server Virtualization Mgmt

Optimization Planning (VectorDot)

Virtualization Orchestrator

Storage Virtualization Mgmt



Cluster testbed
Cluster testbed Balancing in Data Centers

Load balancing input
Load balancing input Balancing in Data Centers

  • 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 Balancing in Data Centers

  • 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 virtual Balancing in Data Centersitem 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 Balancing in Data Centers

  • 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 Balancing in Data Centers

  • FlowPath(u) for a node is the path from node to the storage virtualizer

VectorDot Balancing in Data Centers

  • 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) Balancing in Data Centers

  • 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 Balancing in Data Centers

  • 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 Balancing in Data Centers

  • Simple experiment: live migration of VM running PostMark benchmark, and its vdisk

  • Migration incurs some overhead

Evaluation simulation
Evaluation: Simulation Balancing in Data Centers

  • 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 Balancing in Data Centers

  • BestFit and RelaxedBestFit achieve low imbalance scores

Results moves from initial state
Results: Moves from initial state Balancing in Data Centers

  • BestFit and RelaxedBestFit require fewest moves to reach balance

  • At no point does the # of triggers or imbalance score increase

Results convergence
Results: Convergence Balancing in Data Centers

  • ??

Results running time
Results: Running time Balancing in Data Centers

  • 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 Balancing in Data Centers

  • Figure 1

    • 3 servers, 3 switches, 3 storage nodes

    • 6 vms, 6 storage volumes

    • Disabled caching?

  • Workload generators – lookbusy, IOMeter

Results single server overload
Results: Single server overload Balancing in Data Centers

  • Figure 11b

Results multi server overload
Results: Multi-server overload Balancing in Data Centers

Results server storage overload
Results: Server+storage overload Balancing in Data Centers

Results switch overload
Results: Switch overload Balancing in Data Centers

Summary Balancing in Data Centers

  • 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