Li et al., 2021 - Google Patents
Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systemsLi et al., 2021
- Document ID
- 10702253470119823027
- Author
- Li C
- Liu J
- Li W
- Luo Y
- Publication year
- Publication venue
- Knowledge-Based Systems
External Links
Snippet
With the rapid development and the widespread use of cloud computing in various applications, the number of users distributed in different regions has grown exponentially. Therefore, the Geo-distributed cloud systems have become a research hotspot and big data …
- 230000003044 adaptive 0 title description 5
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]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- 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]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
- G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
-
- 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
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
-
- 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
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/885—Monitoring specific for caches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sun et al. | Re-Stream: Real-time and energy-efficient resource scheduling in big data stream computing environments | |
Zhong et al. | A cost-efficient container orchestration strategy in kubernetes-based cloud computing infrastructures with heterogeneous resources | |
Tantalaki et al. | A review on big data real-time stream processing and its scheduling techniques | |
Glushkova et al. | Mapreduce performance model for Hadoop 2. x | |
De Matteis et al. | Keep calm and react with foresight: Strategies for low-latency and energy-efficient elastic data stream processing | |
Jalaparti et al. | Network-aware scheduling for data-parallel jobs: Plan when you can | |
Li et al. | Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems | |
Liu et al. | Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment | |
Li et al. | Supporting scalable analytics with latency constraints | |
Li et al. | Real-time scheduling based on optimized topology and communication traffic in distributed real-time computation platform of storm | |
Gao et al. | Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision | |
Tang et al. | Dynamic memory-aware scheduling in spark computing environment | |
Fan | Job scheduling in high performance computing | |
Al-Sinayyid et al. | Job scheduler for streaming applications in heterogeneous distributed processing systems | |
Mon et al. | Clustering based on task dependency for data-intensive workflow scheduling optimization | |
Ye et al. | Deep learning workload scheduling in gpu datacenters: A survey | |
Jin et al. | Towards low-latency batched stream processing by pre-scheduling | |
Zhu et al. | A priority-aware scheduling framework for heterogeneous workloads in container-based cloud | |
Ye et al. | Astraea: A fair deep learning scheduler for multi-tenant gpu clusters | |
Zhang et al. | Autrascale: an automated and transfer learning solution for streaming system auto-scaling | |
Li et al. | Cost-efficient scheduling algorithms based on beetle antennae search for containerized applications in Kubernetes clouds | |
Sun et al. | An energy efficient and runtime-aware framework for distributed stream computing systems | |
Alanazi et al. | A multi-optimization technique for improvement of Hadoop performance with a dynamic job execution method based on artificial neural network | |
Li et al. | Energy-aware scheduling for spark job based on deep reinforcement learning in cloud | |
Wang et al. | A round robin with multiple feedback job scheduler in Hadoop |