Availability-Aware Virtual Cluster Allocation in Bandwidth-Constrained Datacenters

Liu, Jialei; Wang, Shangguang; Zhou, Aoying; Buyya, Rajkumar; Yang, Fangchun

doi:10.1109/tsc.2017.2694838

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…The comprehensive testbed and system modelling evaluation presented in this paper would enable industrial system's practitioners to adopt our model, especially if their focus is to enhance computing efficiency, reduce memory and target faster node discoveries within remote systems. Further steps at improving the bandwidth‐constrained scheme with deep learning using multi‐core cooperative scheduling techniques [34, 35, 36, 37, 38] are major future research concerns and directions.…”

Section: Discussionmentioning

confidence: 99%

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MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

et al. 2023

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The rampant deployment of data distribution service (DDS) as middle‐ware service providers for industrial network platforms has been widely investigated. All DDS‐based node discovery protocols establish communication with its intended target systems by accessing matched endpoints/nodes information. These matched endpoints/nodes information is usually embedded with the system’s programmable control plane network which acts as the conveyor vehicle. The introduction of software defined networking (SDN) is to characterize the control plane from embedded data plane. The DDS implements the simple discovery protocol (SDP) as its inherent node discovery protocol. Deploying DDS for data packet exchange in server‐based collaborative distributed networked control (DNC) systems has gained traction. The current automatic discovery protocol (ADP) based on SDP is fraught with real‐time limitations such as high memory consumption and poor packet transmission. This work presents novel memory‐efficient automatic discovery data distribution service (MAD‐DDS) with enhanced threshold bloom filters (ETBF) where ETBF stores transmission packets at simulation end‐nodes. The packet is further adjusted using optimized binarization and decision thresholds inside ADP, hence guaranteeing memory reduction. The testbed computation recorded significantly improved quality of service (QoS) whereas numerical results depict significant decline in memory consumption with consistent packet transmission rates that produces increased computational capacity.

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Section: Discussionmentioning

confidence: 99%

“…discoveries within remote systems. Further steps at improving the bandwidth-constrained scheme with deep learning using multi-core cooperative scheduling techniques [34,35,36,37,38] are major future research concerns and directions.…”

Section: Author Contributionsmentioning

confidence: 99%

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

et al. 2023

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“…In order to manage multiple users and different scenarios, required for IoT, the main OS can run virtualized operating systems (which emulates the entire hardware resources required by an OS) or it can exploit the containerization paradigm, which lets the user have multiple instances of the OS running at the same time while sharing the kernel for resources allocation. The containerization of tasks makes the organization and the allocation of workloads more efficient, by exploiting the bandwidth of the system at its maximum, without exceeding constraints [12]. For sake of clarity, since this current survey is focusing on hardware aspects, OS and software related technologies are not discussed in the following sections; however, the reader can refer to the provided references for further details.…”

Section: Introductionmentioning

confidence: 99%

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

et al. 2019

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In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

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“…Nevertheless, cloud computing is usually located remotely that is far away from terminal devices. Besides, with the increasing popularity of mobile services running on terminal devices, scheduling all the offloaded service requests to cloud computing can put a significant burden on the networks [9,10]. To meet this challenge, recent researches have been proposed to put edge servers with computing capacities at the edge of the networks in close proximity to terminal devices.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Service Request Scheduling for Mobile Edge Computing Systems

Chen

Zhang

Chen

2018

Wireless Communications and Mobile Computing

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Nowadays, mobile services (applications) running on terminal devices are becoming more and more computation-intensive. Offloading the service requests from terminal devices to cloud computing can be a good solution, but it would put a high burden on the network. Edge computing is an emerging technology to solve this problem, which places servers at the edge of the network. Dynamic scheduling of offloaded service requests in mobile edge computing systems is a key issue. It faces challenges due to the dynamic nature and uncertainty of service request patterns. In this article, we propose a Dynamic Service Request Scheduling (DSRS) algorithm, which makes request scheduling decisions to optimize scheduling cost while providing performance guarantees. The DSRS algorithm can be implemented in an online and distributed way. We present mathematical analysis which shows that the DSRS algorithm can achieve arbitrary tradeoff between scheduling cost and performance. Experiments are also carried out to show the effectiveness of the DSRS algorithm.

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Availability-Aware Virtual Cluster Allocation in Bandwidth-Constrained Datacenters

Cited by 17 publications

References 37 publications

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

Dynamic Service Request Scheduling for Mobile Edge Computing Systems

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