DPR, LPR: proactive resource allocation algorithms for asynchronous real-time distributed systems

Ravindran, Binoy; Li, P.

doi:10.1109/tc.2004.1261829

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…The framework includes a fast heuristic incremental allocation mechanism for dynamically performing resource allocation and a redistribution optimization algorithm maintains optimal cost. Reference [9] proposed two active resource allocation algorithms, DPR and LPR, to meet the time-efficiency requirements of real-time tasks in asynchronous real-time distributed systems. It allows the specified task workload to be allocated for resource allocation to maximize the deadline satisfaction rate.…”

Section: Related Workmentioning

confidence: 99%

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

et al. 2019

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As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources' allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms. INDEX TERMS Edge computing, multiple business, power Internet of Things, service delay, workload allocation.

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Section: Related Workmentioning

confidence: 99%

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

et al. 2019

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“…In this paper, we experiment primarily with a static configuration approach, leaving the dynamic modification (through either application profiling or resource usage profiling) of configuration as future work. There have been numerous algorithms proposed for resource allocation [23,43,12,38,25,39,49]. We assume that the specific limits/weights are provided by OS or runtime layer to ensure a certain level of performance.…”

Section: Class-of-service Mappingmentioning

confidence: 99%

CoQoS: Coordinating QoS-aware shared resources in NoC-based SoCs

Zhao

Iyer

et al. 2011

Journal of Parallel and Distributed Computing

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“…In recent years, numerous studies have been conducted on the mapping problem and mapping heuristics (e.g., [1,9,10,[12][13][14]16,17,19,20,31,37,49,59,63,65,81,84,87]). …”

mentioning

confidence: 99%

Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

Ali¹,

Braun²,

Siegel³

et al. 2005

Advances in Computers

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In many distributed computing environments, collections of applications need to be processed using a set of heterogeneous computing (HC) resources to maximize some performance goal. An important research problem in these environments is how to assign resources to applications (matching) and order the execution of the applications (scheduling) so as to maximize some performance criterion without violating any constraints. This process of matching and scheduling is called mapping.1Howard Jay Siegel holds a joint appointment in the Computer Science Department as well. 2Albert I. Reuther is currently with MIT Lincoln Laboratory, Lexington, MA. CHARACTERIZING RESOURCE ALLOCATION HEURISTICSTo make meaningful comparisons among mapping heuristics, a system designer needs to understand the assumptions made by the heuristics for (1) the model used for the application and communication tasks, (2) the model used for system platforms, and (3) the attributes of the mapping heuristics. This chapter presents a three-part classification scheme (3PCS) for HC systems. The 3PCS is useful for researchers who want to (a) understand a mapper given in the literature, (b) describe their design of a mapper more thoroughly by using a common standard, and (c) select a mapper to match a given real-world environment.

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DPR, LPR: proactive resource allocation algorithms for asynchronous real-time distributed systems

Cited by 7 publications

References 23 publications

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

CoQoS: Coordinating QoS-aware shared resources in NoC-based SoCs

Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

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