Probabilistic Scheduling Algorithm Research Articles

Elastic Load Balancing Using Self-Adaptive Replication Management

Load balancing is a critical problem within storage clusters. Existing algorithms often require high communication overhead, because they have to collect sufficient information that they can then use to dispatch requests for hotspot data fairly. We propose an efficient scheme to achieve approximately optimal load balancing while keeping communication overhead low, namely, self-adaptive replication management (SARM). Our approach estimates the access strength of hotspot data and establishes adequate number of replicas on nodes based on their load conditions. Each node uses a dynamic scheduling algorithm to address requests for hotspot data. If the load conditions of all dispatched nodes exceed the fair load estimate, a minimum scheduling algorithm is used to dispatch the requests; otherwise, a probabilistic scheduling algorithm is adopted instead. In another word, SARM automatically switches the scheduling algorithms according to fair load estimates and the load conditions on nodes. Consequently, it eliminates request burstiness while achieving stable load balancing. To avoid excessive communication overhead, the fair load estimates are updated within a fixed time interval. Moreover, when the load variations in a node exceed a specific threshold, their load conditions are dynamically updated to other nodes. Finally, we also consider data availability in SARM. We present simulations and analysis on the performance of our approach compared with other schemes under a variety of load conditions.

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Mobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.

Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.

Improving MANET performance by a hop‐aware and energy‐based buffer management scheme

ABSTRACTA hop‐aware and energy‐based buffer management scheme (HEB) is proposed in this paper. HEB can provide better quality of service to packets with real‐time requirements and improve MANET power efficiency. In our algorithm, the buffer is divided into real‐time and non‐real‐time partitions. We consider the number of hops passed, the power levels of the transmitting node, the predicted number of remaining hops, and waiting time in the buffer to determine packet transmission priority. In addition, specialized queue management and a probabilistic scheduling algorithm are proposed to decrease retransmissions caused by packet losses. Mathematical derivations of loss rates and end‐to‐end delays are also proposed. Coincidence between mathematical and simulation results is also shown. Finally, the HEB is compared with first in first out, random early detection, and hop‐aware buffering scheme. Simulation results show that the proposed algorithm reduces loss rates, power consumption, and end‐to‐end delays for real‐time traffic, considerably improving the efficiency of queue management in MANET. Copyright © 2012 John Wiley & Sons, Ltd.

A real-time vision module for interactive perceptual agents

Abstract. Interactive robotics demands real-time visual information about the environment. Real-time vision processing, however, places a heavy load on the robot's limited resources, which must accommodate multiple other processes running simultaneously. This paper describes a vision module capable of providing real-time information from ten or more operators while maintaining at least a 20-Hz frame rate and leaving sufficient processor time for a robot's other capabilities. The vision module uses a probabilistic scheduling algorithm to ensure both timely information flow and a fast frame capture. In addition, it tightly integrates the vision operators with control of a pan-tilt-zoom camera. The vision module makes its information available to other modules in the robot architecture through a shared memory structure. The information provided by the vision module includes the operator information along with a time stamp indicating information relevance. Because of this design, our robots are able to react in a timely manner to a wide variety of visual events.