Agile power management for future energy-efficient servers in data centers running latency-critical applications

ALPS aims at improving the energy efficiency of CPUs used in servers running latency-critical applications in data centers. It aspires to develop new idle power states that are fast to transition to and from and yet provide most of the power savings of deep idle states. Such new idle states are particularly critical for modern applications that use a distributed software architecture based on a number of microservices that each have microsecond-scale tail QoS requirements. Such tight latency requirements exacerbate the performance implications of killer microseconds, such as those caused during transition to legacy deep idle states, which leads vendors to recommend disabling deep idle power states in data center servers to avoid their performance degradation.



  1. MICRO
    AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications
    Jawad Haj-Yahya, Haris Volos, Davide B. Bartolini, Georgia Antoniou, Jeremie S. Kim, Zhe Wang, Kleovoulos Kalaitzidis, Tom Rollet, Zhirui Chen, Ye Geng, Onur Mutlu, and Yiannakis Sazeides
    In MICRO ’22: Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture 2022
  2. MICRO
    AgilePkgC: An Agile System Idle State Architecture for Energy Proportional Datacenter Servers
    Georgia Antoniou, Haris Volos, Davide B. Bartolini, Tom Rollet, Yiannakis Sazeides, and Jawad Haj-Yahya
    In MICRO ’22: Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture 2022
  3. NVMW
    Persistent Scripting
    Zi Fan Tan, Jianan Li, Haris Volos, and Terence Kelly
    In NVMW ’22: 13th Annual Non-Volatile Memories Workshop 2022
  4. ACM Queue
    Persistent Memory Allocation: Leverage to move a world of software
    Terence Kelly, Zi Fan Tan, Jianan Li, and Haris Volos
    ACM Queue magazine 2022
  5. TOCS
    Unified Holistic Memory Management Supporting Multiple Big Data Processing Frameworks over Hybrid Memories
    Lei Chen, Jiacheng Zhao, Chenxi Wang, Ting Cao, John Zigman, Haris Volos, Onur Mutlu, Fang Lv, Xiaobing Feng, Guoqing Harry Xu, and Huimin Cui
    ACM Transactions on Computer Systems 2022