Malekloo et al., 2018 - Google Patents
An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environmentsMalekloo et al., 2018
- Document ID
- 17237421862703401146
- Author
- Malekloo M
- Kara N
- El Barachi M
- Publication year
- Publication venue
- Sustainable Computing: Informatics and Systems
External Links
Snippet
Cloud computing is a promising paradigm that enables a “computing-as-a-service” model, in which a dynamic pool of virtualized computational resources (eg CPU) can be leased and released on demand. With the increased demand for cloud computing infrastructures and …
- 238000005265 energy consumption 0 abstract description 74
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
- G06F9/50—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]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
-
- 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
- G06F9/5011—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
- 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
- G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized 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]
- G06F9/5083—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]
- G06F9/5094—Allocation of resources, e.g. of the central processing unit [CPU] where the allocation takes into account power or heat criteria
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
- Y02B60/10—Energy efficient computing
- Y02B60/16—Reducing energy-consumption in distributed systems
- Y02B60/167—Resource sharing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
- Y02B60/10—Energy efficient computing
- Y02B60/14—Reducing energy-consumption by means of multiprocessor or multiprocessing based techniques, other than acting upon the power supply
- Y02B60/142—Resource allocation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Malekloo et al. | An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments | |
Ala’Anzy et al. | Load balancing and server consolidation in cloud computing environments: a meta-study | |
Chaurasia et al. | Comprehensive survey on energy-aware server consolidation techniques in cloud computing | |
Zakarya | Energy, performance and cost efficient datacenters: A survey | |
Wu et al. | A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters | |
Addya et al. | Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers | |
Patel et al. | VM provisioning method to improve the profit and SLA violation of cloud service providers | |
Al-Dulaimy et al. | Type-aware virtual machine management for energy efficient cloud data centers | |
Zhang et al. | Minimizing communication traffic in data centers with power-aware VM placement | |
Chen et al. | Improving resource utilization via virtual machine placement in data center networks | |
Kaur et al. | Optimization techniques for resource provisioning and load balancing in cloud environment: a review | |
Gul et al. | CPU and RAM energy-based SLA-aware workload consolidation techniques for clouds | |
Hussein et al. | Green cloud computing: Datacenters power management policies and algorithms | |
Remesh Babu et al. | Service‐level agreement–aware scheduling and load balancing of tasks in cloud | |
Li et al. | A dynamic I/O sensing scheduling scheme in Kubernetes | |
Mehta et al. | Energy conservation in cloud infrastructures | |
Lin et al. | Novel resource allocation model and algorithms for cloud computing | |
Gul et al. | CPU–RAM-based energy-efficient resource allocation in clouds | |
Barlaskar et al. | Energy-efficient virtual machine placement using enhanced firefly algorithm | |
Bermejo et al. | Improving the energy efficiency in cloud computing data centres through resource allocation techniques | |
Verma et al. | MADLVF: an energy efficient resource utilization approach for cloud computing | |
Kumar et al. | A swarm intelligence-based quality of service aware resource allocation for clouds | |
Vatsal et al. | Virtual machine migration based algorithmic approach for safeguarding environmental sustainability by renewable energy usage maximization in Cloud data centres | |
Nadjar et al. | Hierarchical VM scheduling to improve energy and performance efficiency in IaaS Cloud data centers | |
Rajnikant et al. | Improving energy efficiency and minimizing service-level agreement violation in mobile cloud computing environment |