Wang et al., 2020 - Google Patents
Energy minimization for cloud services with stochastic requestsWang et al., 2020
View PDF- Document ID
- 14362469108143803727
- Author
- Wang S
- Sheng Q
- Li X
- Mahmood A
- Zhang Y
- Publication year
- Publication venue
- Service-Oriented Computing: 18th International Conference, ICSOC 2020, Dubai, United Arab Emirates, December 14–17, 2020, Proceedings 18
External Links
Snippet
Energy optimization for cloud computing services has gained a considerable momentum over the recent years. Unfortunately, minimizing energy consumption of cloud services has its own unique research problems and challenges. More specifically, it is difficult to select …
- 238000005265 energy consumption 0 abstract description 47
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Programme initiating; Programme switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/44—Arrangements for executing specific programmes
- G06F9/455—Emulation; Software simulation, i.e. virtualisation or emulation of application or operating system execution engines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power Management, i.e. event-based initiation of power-saving mode
- G06F1/3234—Action, measure or step performed to reduce power consumption
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3442—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment for planning or managing the needed capacity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ruan et al. | Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds | |
Cheng et al. | Energy efficiency aware task assignment with dvfs in heterogeneous hadoop clusters | |
Liu et al. | On arbitrating the power-performance tradeoff in SaaS clouds | |
Yao et al. | Power cost reduction in distributed data centers: A two-time-scale approach for delay tolerant workloads | |
Kumar et al. | ARPS: An autonomic resource provisioning and scheduling framework for cloud platforms | |
Chen et al. | Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment | |
Xiao et al. | A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory | |
Zhang et al. | Energy-efficient workload allocation and computation resource configuration in distributed cloud/edge computing systems with stochastic workloads | |
Mirmohseni et al. | LBPSGORA: create load balancing with particle swarm genetic optimization algorithm to improve resource allocation and energy consumption in clouds networks | |
Wang et al. | An adaptive model-free resource and power management approach for multi-tier cloud environments | |
Asadi et al. | Unified power and performance analysis of cloud computing infrastructure using stochastic reward nets | |
Pei et al. | Asyfunc: A high-performance and resource-efficient serverless inference system via asymmetric functions | |
Mishra et al. | Allocation of energy-efficient task in cloud using DVFS | |
Vasudevan et al. | Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers | |
Uma et al. | Optimized intellectual resource scheduling using deep reinforcement Q‐learning in cloud computing | |
Ataie et al. | Modeling and evaluation of dispatching policies in IaaS cloud data centers using SANs | |
Li et al. | Topology-aware scheduling framework for microservice applications in cloud | |
Hu et al. | Improve the energy efficiency of datacenters with the awareness of workload variability | |
Bai et al. | A queue waiting cost-aware control model for large scale heterogeneous cloud datacenter | |
Meng et al. | Achieving energy efficiency through dynamic computing offloading in mobile edge-clouds | |
Liu et al. | ScaleFlux: Efficient stateful scaling in NFV | |
Wang et al. | Energy minimization for cloud services with stochastic requests | |
Tunc et al. | Value of service based resource management for large-scale computing systems | |
Ho et al. | Improving energy efficiency for transactional workloads in cloud environments | |
Fu et al. | DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoT |