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Sandpiper : Black box and Gray-Box resource management for Virtual Machines Journal : Computer Networks: The International Journal of Computer and Telecommunications Networking, 2009 Vinayak Gagrani
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Introduction Overloaded Data Center can be handled in two ways :- – Reallocation of resources within the physical machines – Migration of one or multiple VMs to distribute the load Manual Migration is error prone and lacks immediate response Sandpiper introduces technique for monitoring VMs: – Black Box : externally monitor VMs, without knowledge of applications executing within them – Gray Box : use metrics from OS on applications for more information Uses prediction to decide the utilization possible in future Uses greedy approach to decide which VMs to move around
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Sandpiper Archietecture
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Resource Provisioning Need to estimate the additional resource requirements by VM Black Box :- – High percentile of the tail distribution as initial estimate – VM is over using its fair share – VM is using its fair share completely, denotes less requirements Scaling (How much to scale ?) Gray Box :- – Better provisioning using service rate, response rate and drop rate – Applications modeled as G/G/1 queuing system – Allows to reduce the memory allocated in case its not being used fully
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Hotspot Detection & Mitigation Hotspot Detection – Black box – per physical server, Gray box – per virtual server – Prolonged exceeding of hotspot (k in N) as well as next predicted value then only hotspot are marked – Conservative or Aggressive approach ( based on k and N ) – Prediction of future values using auto-regressive predictors Hotspot Mitigation – VM Resizing – Migration NP Hard Capturing Multi-dimension loads – Volume of server Migration phase Swap phase
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Positives and Negatives Positives – Very good demonstration of using one technology (live migration) into other (resource management) – Lot of figures and graphs to assist text – Very detailed description, efficient and ready to be used Negatives – Separate machine is to be dedicated as control plane – Lot of data to be kept for predicting and profiling for each VM – Possible bottleneck (?) – Algorithms in mitigation could have been more structured – Does not describe how to determine ‘k’ lowest VSR VMs in swapping phase
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Points to ponder Memory resizing in black box approach – Issues and possible solution Quantify the load of a machine – Problem with current metric for volume – Alternatives ? Experiments ?
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Future Work Multiple Control Planes ? – Instead of one control plane use multiple planes which interact with each other – Utilize features of distributed computations – Remove bottleneck in monitoring(?) – Reduce chances of failure on central machine (?) Any Other ?
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