Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance the utilization of computational res...
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ISBN:
(纸本)9798350383515;9798350383508
Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance the utilization of computational resources, it still suffers from resource waste. The primary reason is that end-users from the same area are likely to offload similar tasks to edge servers, thereby leading to duplicate computations. To improve system efficiency, the computation results of previously executed tasks can be cached and then reused by subsequent tasks. However, most existing computation reuse algorithms only consider one edge server, which significantly limits the effectiveness of computation reuse. To address this issue, this paper applies computation reuse in CEC networks to exploit the collaboration among edge servers. We formulate an optimization problem that aims to minimize the overall task response time and decompose it into a caching subproblem and a scheduling subproblem. By analyzing the properties of optimal solutions, we show that the optimal caching decisions can be efficiently searched using the bisection method. For the scheduling subproblem, we utilize projected gradient descent and backtracking to find a local minimum. Numerical results show that our algorithm significantly reduces the response time in various situations.
The onset of wireless networks globally has thrust researchers in academia and industry to solve problems related to this ever-growing field. In this paper, we study the multi-constrained opportunistic wireless schedu...
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ISBN:
(纸本)9781728199160
The onset of wireless networks globally has thrust researchers in academia and industry to solve problems related to this ever-growing field. In this paper, we study the multi-constrained opportunistic wireless scheduling problem in cognitive radio networks. Given a collection of secondary user communication links, the channel state of each link is unknown due to the unpredictable primary users' activities, but can be estimated by exploring the channel state transitions and channel state feedback. A scheduling algorithm is used to decide a subset of links to transmit each time with both interference-free constraints and power budget constraints. The objective of this paper is to design a scheduling algorithm to optimize the average reward over a long time horizon. Current existing approaches cannot satisfyingly provide solutions for the wireless opportunistic scheduling problem when considering multiple constraints. In this work, we adopt the paradigm of the restless multi-armed bandit and propose a fast and simple approximation algorithm. The performance of the proposed algorithm is verified with a small approximation bound for the multi-constrained wireless opportunistic wireless scheduling problem.
In modern cloud block storage systems, the routing layer bears the task of slicing and distributing application requests to the underlying storage engine and thus becomes the critical path. However, skewed application...
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ISBN:
(纸本)9798350380415;9798350380408
In modern cloud block storage systems, the routing layer bears the task of slicing and distributing application requests to the underlying storage engine and thus becomes the critical path. However, skewed application traffic leads to load imbalance among routing servers, causing severe performance bottlenecks and hurting the quality of service. Existing load balancing methods typically employ static allocation strategies without considering the access pattern of individual virtual disks. They are not well suited to deal with the highly dynamic loads in production. In this paper, we first collect and analyze 7-day workload traces of 130k virtual disks from a commercial cloud block storage system. Based on several observations, we propose VDMig, an adaptive virtual disk migration scheme. VDMig initially divides virtual disks into two types of access patterns, then uses application-level semantics to characterize the load of virtual disks, and finally implements predictive migrating strategies adaptively to achieve dynamic load balancing. Extensive experiments demonstrate that VDMig can achieve finegrained virtual disk management, effectively balancing the load among routing servers. Compared to the static methods widely deployed in the industry, VDMig can reduce the imbalance by 82.38% on average.
With the rapid development of network devices and increasingly high CPU workloads, packet scheduling will have to be offloaded to hardware. Programmable packet scheduling allows scheduling algorithms to be programmed ...
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With the rapid development of network devices and increasingly high CPU workloads, packet scheduling will have to be offloaded to hardware. Programmable packet scheduling allows scheduling algorithms to be programmed into network devices without modifying the hardware. It not only retains the flexibility of software but also the scalability of hardware. In existing primitives, the most expressive Push-In-ExtractOut (PIEO) is prohibitively expensive to implement due to its complexity. While its variant, Push -In -Pick -Out (PIPO), offers some improvements, it suffers from insufficient scalability. In this paper, we propose the ClassifyIn -Push -Out (CIPO) primitive. The core idea of CIPO is to track the rank and predicate of recent packets through a sliding window, filter and classify packets using a prediction -based two-dimensional classification algorithm and a finite number of First -In -First -Out (FIFO) queues. Through theoretical analysis and evaluation with a range of real workloads, CIPO proves that it has a scheduling performance similar to the most expressive scheduling primitive. Importantly, CIPO requires fewer hardware resources while still providing sufficient expressiveness. Primitive on FPGA show that the CIPO-based scheduler achieves an average of 1 . 24x higher throughput than the PIEO-based scheduler but uses only an average of 26.2% of look -up tables (LUTs) and 12.2% of the block RAM of the latter.
Recommender systems appear on many commercial websites because E-Commerce and social websites are popular. However, many businesses do not have enough capacity to develop their recommender systems, which must be outso...
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ISBN:
(纸本)9781665485500
Recommender systems appear on many commercial websites because E-Commerce and social websites are popular. However, many businesses do not have enough capacity to develop their recommender systems, which must be outsourced by cloud-based service providers because the amount and complexity of data increase. Recently, network function virtualization (NFV) has been proposed to use virtualization technology to replace dedicated network equipment for software network functions. Virtual network functions (VNFs) run on commodity servers or standard physical machines. In this paper, an NFV-based scheduling and flexible deployment scheme (SFDS) is proposed to consider the characteristics of recommendation-as-a-service (RaaS), including the popularity and execution order of the functions. According to the popularity of the functions, a dynamic deployment algorithm is able to flexibly create or remove the VNFs on the virtual nodes. Finally, the simulation results show a shorter completion time. SFDS enhances the resource utilization of the system and has a better successful reception rate.
Time Sensitive Networks (TSN), as an important representative of deterministic networks, provide low -latency and highly reliable communication services for the growing network applications that have strict requiremen...
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Time Sensitive Networks (TSN), as an important representative of deterministic networks, provide low -latency and highly reliable communication services for the growing network applications that have strict requirements. Cyclic Queuing and Forwarding (CQF) is a well-known mechanism proposed by IEEE 802.1Qch for low -latency flow control of time -sensitive networks. It achieves bounded end -to -end delay and jitter transmission through a set of queues without complicated queue gating. However, most of the current work overlooks the widespread existence of multi -link rate networks in LANs and WANs, and the single -cycle CQF is unable to adjust different link rates, resulting in low bandwidth utilization and high latency. In this paper, we propose a novel scheduling approach named Multi -Cycle CQF (MCCQF) to solve the transmission problem in multi -link rate networks, aiming to reduce deterministic end -to -end delay and improve link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi -linkrate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single -cycle CQF online scheduling algorithm in hierarchical multi -link -rate networking scenarios, and the evaluation shows that our algorithm achieves better end -to -end ultra -low latency (38.9% reduction) with a smaller schedulability gap compared with single -cycle CQF. And we also improved the schedulability based on MCCQF by utilizing internal offset.
The multicore systems are designed to give excellent performance by parallelly executing the various tasks on different cores. The execution of tasks requires lots of energy which can be saved if the tasks are schedul...
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Spark Streaming is currently one of the mainstream stream processing frameworks which process real-time stream data by using micro-batch approach. However, there are some issues with its default task scheduling proces...
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Spark Streaming is currently one of the mainstream stream processing frameworks which process real-time stream data by using micro-batch approach. However, there are some issues with its default task scheduling process, such as the high cost of cluster usage due to inappropriate executor placement strategy in heterogeneous cluster environments. Meanwhile, most of the current scheduling studies focus on improving the processing performance of the clusters, while ignoring the cost efficiency and service quality assurance of the clusters. In this paper, we propose a low-cost executor placement method based on resource demand prediction using machine learning under heterogeneous clusters, which is called Cost-Efficient and Best-Fit Decrease (CEBFD). First, a cost-efficient model is constructed for the Spark Streaming framework, then the Sparrow Search algorithm (SSA) and eXtreme Gradient Boosting (XGboost) algorithm are combined to predict the resources required by streaming tasks, and finally the executor placement method for the heterogeneous Spark Streaming clusters is designed based on the cost-efficient model and resource demand prediction. Furthermore, the proposed method also improves the Service Level Agreement (SLA) of cost minimization and job deadline guarantee for streaming processing. Experimental results show that the proposed approach reduces the cluster usage cost by 6.89% to 52.24% and effectively optimizes SLA compared to existing algorithms.
In safety-critical systems many software components of different criticalities or assurance levels need to interact in a timely manner to keep the system and environment safe. Nowadays, these systems are challenged by...
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In safety-critical systems many software components of different criticalities or assurance levels need to interact in a timely manner to keep the system and environment safe. Nowadays, these systems are challenged by technological progress resulting in rapid increases in both software complexity and processing demands. Efficiently designing safety-critical systems subject to stringent timing requirements is therefore a challenge and a necessity. In this article, we consider the mixed-criticality execution model and homogeneous multi-core processors. We begin by defining a task model incorporating mixed-criticality, real-time and precedence constraints in the form of directed acyclic graphs. A meta-heuristic to solve the scheduling problem of this task model is then defined and proved to respect deadlines, even when the system needs to give more processing power to the most critical tasks. The state-of-the-art techniques capable of scheduling a similar task model have only been developed for dual-criticality systems. Conversely, the meta-heuristic we propose has been generalized to support an arbitrary number of criticality levels. We instantiated our meta-heuristic adopting scheduling algorithms such as G-EDF, G-LLF, or G-EDZL for each level of criticality. The experiments show excellent results in terms of acceptance ratio and number of preemptions.
This paper models a dynamic task scheduling problem on a distributed computing platform. It presents a new cyclic scheduling approach for multiple task scientific applications. We propose a scheduling algorithm CEFT—...
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