D'souza et al., 2017 - Google Patents
Time-based Coordination in {Geo-Distributed}{Cyber-Physical} SystemsD'souza et al., 2017
View PDF- Document ID
- 995711381459177991
- Author
- D'souza S
- Rajkumar R
- Publication year
- Publication venue
- 9th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 17)
External Links
Snippet
Emerging Cyber-Physical Systems (CPS) such as connected vehicles and smart cities span large geographical areas. These systems are increasingly distributed and interconnected. Hence, a hierarchy of cloudlet and cloud deployments will be key to enable scaling, while …
- 238000000034 method 0 abstract description 6
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
- 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
- G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
- G06F9/4856—Task life-cycle, e.g. stopping, restarting, resuming execution resumption being on a different machine, e.g. task migration, virtual machine 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
- G06F9/54—Interprogramme communication; Intertask communication
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Luo et al. | Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT | |
US11159609B2 (en) | Method, system and product to implement deterministic on-boarding and scheduling of virtualized workloads for edge computing | |
Wan et al. | Application deployment using Microservice and Docker containers: Framework and optimization | |
Zhou et al. | Augmentation techniques for mobile cloud computing: A taxonomy, survey, and future directions | |
US11139991B2 (en) | Decentralized edge computing transactions with fine-grained time coordination | |
CN110932839B (en) | Network card, time synchronization method, equipment and computer storage medium | |
Alsboui et al. | Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents | |
Hoque et al. | Towards container orchestration in fog computing infrastructures | |
EP3885908A1 (en) | A computer-readable storage medium, an apparatus and a method to select access layer devices to deliver services to clients in an edge computing system | |
D'souza et al. | Time-based Coordination in {Geo-Distributed}{Cyber-Physical} Systems | |
US10972579B2 (en) | Adaptive scheduling for edge devices and networks | |
Pawar et al. | A review of resource allocation policies in cloud computing | |
Patel et al. | On demand clock synchronization for live VM migration in distributed cloud data centers | |
Lordan et al. | An architecture for programming distributed applications on fog to cloud systems | |
Bertier et al. | Beyond the clouds: How should next generation utility computing infrastructures be designed? | |
Katenbrink et al. | Dynamic scheduling for seamless computing | |
Dou et al. | Scheduling for real-time mobile MapReduce systems | |
Petri et al. | Autonomics at the edge: Resource orchestration for edge native applications | |
Pham et al. | Towards an Elastic Fog‐Computing Framework for IoT Big Data Analytics Applications | |
D'souza et al. | Quartz: Time-as-a-service for coordination in geo-distributed systems | |
Khalifa et al. | Towards a mobile ad-hoc cloud management platform | |
Štefanič et al. | Quality of Service‐aware matchmaking for adaptive microservice‐based applications | |
Gomez-Folgar et al. | An e-Science infrastructure for nanoeletronic simulations based on grid and cloud technologies | |
D'Souza et al. | Quartzv: Bringing quality of time to virtual machines | |
Cicirelli et al. | An agent framework for high performance simulations over multi-core clusters |