A Negotiation Protocol for Dynamic Load Distribution Using Batch Task Assignments

Polling-based load distribution (LD) algorithms suffer from two weaknesses: (i) load information exchanged during a polling session is confined to the two negotiating nodes only; (ii) as the distributed system grows in size (in terms of the number of constituent nodes), a larger number of polling sessions, and thus a higher amount of network bandwidth consumption and CPU overhead, are needed. We propose a new LD algorithm which is based on anti-tasks and load state vectors. This new algorithm avoids the above weaknesses of polling-based LD algorithms. Anti-tasks are composite agents which travel around a distributed system to facilitate the pairing up of task senders and receivers, as well as the collection and dissemination of load information. Time-stamped load information of processing nodes is stored in load state vectors which, when used together with anti-tasks, encourage mutual sharing of load information among processing nodes. Anti-tasks, which make use of load state vectors to decide their traveling paths, are spontaneously directed towards processing nodes having high transient workload, thus allowing their surplus workload to be relocated quickly. Using simulations, we evaluate the performance of our new algorithm by comparing its performance with a number of well-known polling-based load distribution algorithms. We found that our algorithm provides significant reduction of mean task response time over a large range of system sizes. The cost of achieving this performance gain in terms of CPU overhead and channel bandwidth consumption is generally comparable to the other algorithms we studied. © 1998 John Wiley & Sons, Ltd.

These years energy consumption has become the main constraint for the further improvement of computing ability of data centers. In this work, the energy efficient resource management strategies for web servers (one typical class of data centers) are discussed. We firstly introduce the daily pattern of requests sent to web servers which indicates that a simple off-line resource provisioning method would-be effective for web servers. Then, the energy efficiency of two load distribution algorithms are compared by replaying subset of a real-world Wikipedia trace in our local web service environment. The results show that, compared with the relative load distribution (RLB) algorithm, the adaptive load distribution (ALD) algorithm proposed in our previous work is more energy efficient. Finally, we demonstrate a potential that much energy can be saved by shutting down the idle nodes.

Trailer type mobile robots have been studied from the viewpoint of control and design of nonholonomic mechanical systems in literature. However, researches for its detailed closed form kinematic and dynamic modeling are rarely being progressed. Though the trailer type mobile robot is shaped like a kinematically redundant system consisting of surplus joints, its actual kinematic characteristic is similar to a closed chain system since the motion of every trailer is constrained to the ground and the following trailers' motion is dependent on the leading trailer's motion. The exact kinematic relationship between the leading trailer and the following trailers is derived by the transfer method using the augmented generalized coordinates. A general dynamic model is derived using the Lagrangian approach and load distribution algorithm is suggested. It can be seen that the proposed approach facilitates distribution of the system load to all trailers. Forward dynamic simulation is conducted to verify the effectiveness of the suggested kinematic and dynamic models.

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.

The load distribution among processing elements (PE) in the parallel computer system is one of the major factors affecting the performance of the entire system. This paper discusses the dynamic distribution scheme which distributes the load at the execution of the task. The major factor, which affects the performance in the dynamic load distribution scheme, is the information concerning the loading situation of each PE and the data transfer for distributing the load. This paper proposes a dynamic load distribution scheme, which reduces these overheads considerably by utilizing the network. The idea can be applied to both the circuit‐switching network and the packet‐switching network. Especially, the scheme based on the packet‐switching network is one of the load distribution schemes suited to the instruction‐level data‐driven computer, using the packet as the major communication means among PE's. Based on the proposal of the scheme, the performance of the system is evaluated for a typical configuration example for the parallel processing program. It is verified as a result that the proposed system operates very closely to the ideal dynamic load distribution system.

Energy and latency aware mobile task assignment for green cloudlets

Wireless mobile sensing systems are hierarchical and heterogeneous in nature with components that have different energy and performance capabilities. In such systems allocation of tasks to different devices affects both performance and the entire system battery lifetime. In this paper we formulate the problem of optimal task assignment with objectives related to minimizing the total system energy consumption and maximizing the system lifetime as an Integer Linear Program (ILP). We describe a heuristic algorithm and two dynamic graph-based partitioning algorithms that are computationally efficient and that are able to adapt in real-time to changing system conditions. ILP based solutions are able to achieve optimal task assignment, but cannot be used in dynamically changing conditions due to their computationally expensive nature. We evaluate the performance of our three dynamic algorithms using Qualnet and show that they have up to 88% longer system lifetime than the ILP based solutions.

This paper proposed an unmanned air vehicle (UAV) swarm task and resource dynamic assignment algorithm based on task sequence mechanism. By establishing a task sequence, each UAV strictly separates the necessary task time and synchronization waiting time. For the found new targets, each UAV quickly determines its available time period. According to the available time and task resources, an auction algorithm and consensus algorithm are used to decompose the task assignment into initial distributed assignment phase and swarm consensus phase to develop real-time conflict-free task solutions for UAV swarm. The simulation experiment verified that the algorithm could assign different tasks to UAVs in a real-time distributed manner with the limited resources of each UAV.

Clusters of GPUs are becoming commonly used to execute computationally demanding applications. Due to the frequent changes in GPU architecture, many clusters contain heterogeneous types of GPUs, leading to the problem of load distribution among the machines. In this work, we propose a load distribution algorithm for scientific applications executed in heterogeneous GPU clusters. The algorithm finds a distribution of data that minimizes the execution time of the application, by guaranteeing that all GPUs spend the same amount of time processing its assigned kernels and data. We use the algorithm to execute the simulation of large scale neuronal networks. We show that the algorithm effectively balances the load among the GPUs and reduces the execution time of the application.

The study of energy filtering management process for microgrid based on the dynamic response model of vanadium redox flow battery

A new wearable robot for upper-extremity was designed for handling heavy weights. The human-robot integrated structure constitutes a closed-chain mechanism which allows a proper load distribution between the robot and the human. The kinematic and dynamic models for this human-robot integrated exoskeleton system were presented. Through the load distribution algorithm between the human and the robot, it was shown that the robot could withstand majority of the payload. Copyright ?? 2012 IEEE.

Distributed systems such as clusters of PCs are low-cost alternatives for running parallel rendering systems, but they have high communication overhead, and limited memory capacity on each processing node. We focus on the strategy for distributing the parallel rendering work among the PCs. A good distribution strategy provides better load balance, and avoids the need for replicating data on the relatively small memory of each PC. Our goal is to study different distribution strategies on the scope of the Parallel ZSweep algorithm, introducing in PZSweep another work distribution strategy: work stealing. This strategy allows a decentralized control of the work to be done, and provides a dynamic load redistribution. We propose two different algorithms to select the processor that will be stolen and show that the simplest one, nearest neighbor, was the most efficient. We also showed that the load redistribution schemes strongly depended on the initial load distribution, with an interleaved assignment, our systems could outperform the original parallel ZSweep algorithm. We conclude that for running large datasets on a cluster of PCs, parallel ZSweep requires dynamic load distribution strategy.

Coarse-grained reconfigurable architectures (CGRAs) are increasingly employed as domain-specific accelerators due to their efficiency and flexibility. However, the existing CGRA architectures suffer from low hardware resource utilization and performance due to the limitations of the scheduling scheme. In this article, an adaptive scheduler (denoted as DPTM) for concurrent and multi-task interleaved pipelining-oriented CGRA is introduced, which exploits timeslot matching and release methods to avoid the pipeline conflicts and improve the scheduling performance. The characteristics of task scheduling based on directed acyclic graph (DAG) are analyzed, and several performance-influencing factors are extracted to build a scheduling performance model for reducing the time cost of scheduling and guiding the design of scheduling schemes. Moreover, the scoreboard method of dynamic instruction schedulers is optimized to control the entry time of multiple tasks into the pipeline, and then a timeslot matching method is proposed to provide non-conflict pipelining for the multiple tasks. Further, a timeslot release method is presented to release the timeslots for unscheduled sub-tasks dynamically, which can adapt the parallel processing of multiple tasks and decrease the scheduling time. Then, an adaptive scheduling scheme combines the dynamic priority-based task assignment method, timeslot matching method, and timeslot release method to schedule massive tasks for CGRA. Finally, the overall architecture of DPTM is introduced and designed to validate the efficacy of the proposed scheduling scheme. Experimental results show that the proposed timeslot matching/release approach reduces 84% total scheduling time and decreases 40% average scheduling time at most compared to the non-timeslot-matching scheduling schemes, the proposed task assignment approach decreases 8% total scheduling time and lowers 3% average scheduling time compared to the existing approaches, and the proposed scheduler decreases 51% critical path delay, lowers 35% area overhead, and reduces 12% power consumption at most compared with the existing schedulers.

We study an online task assignment problem for multi-robot systems where robots can do multiple tasks during their mission and the tasks arrive dynamically in groups. Each robot can do at most one task from a group and the total number of tasks a robot can do is bounded by its limited battery life. There is a payoff for assigning each robot to a task and the objective is to maximize the total payoff. A special case, where each group has one task and each robot can do one task is the online maximum weighted bipartite matching problem (MWBMP). For online MWBMP, it is known that, under some assumptions on the payoffs, a greedy algorithm has a competitive ratio of 1 over 3. Our key result is to prove that for the general problem, under the same assumptions on the payoff as in MWBMP and an assumption on the number of tasks arising in each group, a repeated auction algorithm, where each group of tasks is (near) optimally allocated to the available group of robots has a guaranteed competitive ratio. We also prove that (a) without the assumptions on the payoffs, it is impossible to design an algorithm with any performance guarantee and (b) without the assumption on the task profile, the algorithms that can guarantee a feasible allocation (if one exists) have arbitrarily bad performance in the worst case. Additionally, we present simulation results depicting the average case performance of the repeated greedy auction algorithm.

Affordable housing supply in China follows a target responsibility system, implemented through a top-down task assignment framework. This unique system may be a pivotal factor contributing to the rapid expansion and supply-demand mismatch of affordable housing in China. This study uses a multitask principal-agent model for theoretical analysis and a quantile regression model for empirical analysis of how China’s housing security task affects the supply-demand mismatch. The results show that China’s target responsibility system for affordable housing is a double-edged sword. The housing security task can significantly increase affordable housing supply, but through the strategic task assignment mechanism, the housing security task can also lead to the supply-demand mismatch of affordable housing between cities. In addition, as provincial governments face conflicts among multiple tasks, the economic growth task can aggravate the strategic task assignment and accentuate the supply-demand mismatch. This study holds significant importance for optimizing the balance between supply and demand of affordable housing in China and some other government-led countries.