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UID:submissions.supercomputing.org_SC25_sess287_pap585@linklings.com
SUMMARY:Workload Intelligence: Workload-Aware IaaS Abstraction for Cloud E
 fficiency
DESCRIPTION:Lexiang Huang (NetApp); Anjaly Parayil, Jue Zhang, Xiaoting Qi
 n, and Chetan Bansal (Microsoft Corporation); Jovan Stojkovic (University 
 of Illinois Urbana-Champaign); Pantea Zardoshti, Pulkit Misra, Eli Cortez,
  Raphael Ghelman, Íñigo Goiri, Saravan Rajmohan, Jim Kleewein, and Rodrigo
  Fonseca (Microsoft Corporation); Timothy Zhu (The Pennsylvania State Univ
 ersity); and Ricardo Bianchini (Microsoft Corporation)\n\nToday, cloud wor
 kloads are largely opaque to the cloud platform. Typically, the only infor
 mation the platform receives is the virtual machine (VM) type and possibly
  a decoration to the type (e.g., the VM is evictable). Similarly, workload
 s receive minimal information from the platform; generally, only telemetry
  from their VMs or occasional signals (e.g., just before a VM is evicted).
  The narrow interface between workloads and platforms has several drawback
 s: (1) a surge in VM types and decorations in public cloud platforms compl
 icates customer selection; (2) key workload characteristics (e.g., low ava
 ilability requirements) are often unspecified, hindering platform customiz
 ation for optimized resource usage and cost savings; and (3) workloads may
  be unaware of potential optimizations or lack sufficient time to react to
  platform events. To resolve these issues and improve cloud efficiency, we
  propose Workload Sage, a framework for enabling dynamic bi-directional co
 mmunication between cloud workloads and cloud platform.\n\nTag: System Sof
 tware and Cloud Computing\n\nRecording: Livestreamed, Recorded\n\nRegistra
 tion Category: Technical Program Reg Pass\n\nSession Chair: Zhaorui Zhang 
 (The Hong Kong Polytechnic University)\n\n
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