Multi-resource Fair Allocation Research Articles

Multiresource fair allocation with time window constraints

Multiresource fair allocation with time window constraints

Multi-resource fair allocation with bandwidth requirement compression in the cloud–edge system

For the multi-resource fair allocation problem in cloud–edge collaborative computing systems, we propose a mechanism called dominant resource fairness in the cloud–edge collaborative computing system (DRF-CE). DRF-CE enables users to deploy tasks to cloud and edge servers and considers the particularity of cloud server bandwidth resources. By introducing bandwidth demand compression, DRF-CE allows users to have two different bandwidth resource demands. DRF-CE satisfies envy-freeness, Pareto efficiency, strategyproofness and the weak sharing incentive property. To implement DRF-CE, we design an algorithm and evaluate its performance via simulations driven by a concrete example and Google cluster traces. Simulation results prove that DRF-CE is superior to traditional approaches in terms of users’ dominant share, resource utilization and the number of tasks performed by each user. Furthermore, the simulation results show that considering bandwidth demand compression can improve user efficiency by approximately 40% and the central processing unit (CPU) utilization by more than 25%.

Fairness constraint efficiency optimization for multiresource allocation in a cluster system serving internet of things

SummaryMassive, diverse, and high‐frequency Internet of Things (IoT) applications pose challenges to the operation of cluster systems that serve it. Fair and efficient multidimensional resource allocation is of great significance to the sustainable operation of these systems. However, most of the existing cluster multiresource allocation optimization researches focus too much on the fairness of resource allocation and ignore the efficiency. The unbalanced use of multidimensional system resources reduces the effective utilization of system resources, which seriously affects the service quality of IoT applications. In this paper, we define the multiresource fair and efficient sharing optimization as a fairness‐constrained efficiency optimization problem, which is from dynamics, discrete resources, and heterogeneous perspectives according to the characteristics of cluster system in practical. Moreover, we present a dynamic efficiency‐aware multiresource fair allocation algorithm, DEF, which can improve the ability of the cluster system to serve diverse IoT applications. In the algorithm, large jobs schedule to the servers that expect the least remaining resources. Simulations performed using Google cluster‐usage traces show that DEF can improve system resource utilization and guarantee the fairness of sharing among users.

Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources

Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources

An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers

Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a new server-based approach ; each server allocates resources by maximizing a per-server utility function . We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposed Per-Server Dominant Share Fairness ). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones.

A new multi-resource allocation mechanism: A tradeoff between fairness and efficiency in cloud computing

This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Dominant Resource with Bottlenecked Fairness (DRBF), which generalizes Bottleneck-aware Allocation (BAA) to the settings of Dominant Resource Fairness (DRF). We classify users into different queues by their dominant resources. The goals are to ensure that users in the same queue receive allocations in proportion to their fair shares while users in different queues receive allocations that maximize resource utilization subject to well-studied fairness properties such as those in DRF. Under DRBF, no user 1) is worse off sharing resources than dividing resources equally among all users; 2) prefers the allocation of another user; 3) can improve their own allocation without reducing other users' allocations; and (4) can benefit by misreporting their resource demands. Experiments demonstrate that the proposed allocation policy performs better in terms of high resource utilization than does DRF.

Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems

Resource fair allocation is a challenging problem in heterogeneous cloud computing systems, both in real-life problems and for scientific research purposes. However, it is an NP-hard problem and solutions obtained by existing heuristic algorithms have a significant gap up to the optimal solutions. Motivated by this fact, we propose three swarm optimization algorithms: discrete artificial bee colony, discrete artificial fish swarm, and discrete shuffled frog leaping. In addition, we investigate how to utilize the impact of search behavior to improve the performance of the algorithm, and we design the self-adaptive parameter settings to balance between the exploitation and exploration of the algorithm. Furthermore, we propose a heuristic algorithm to generate a good initial solution. Compared with some algorithms from the literature, the simulation results show that our proposed algorithms can maximize the global dominant share fairly and increase the resource utilization, and they are highly adaptable to different situations.

Towards Multi-Resource Fair Allocation with Placement Constraints

Multi-resource fair schedulers have been widely implemented in compute clusters to provide service isolation guarantees. Existing multi-resource sharing policies, notably Dominant Resource Fairness (DRF) and its variants, are designed for unconstrained jobs that can run on all machines in a cluster. However, an increasing number of datacenter jobs specify placement constraints and can only run on a particular class of machines meeting specific hardware/software requirements (e.g., GPUs or a particular kernel version). We show that directly extending existing policies to constrained jobs either compromises isolation guarantees or allows users to gain more resources by deceiving the scheduler. It remains unclear how multi-resource fair sharing is defined and achieved in the presence of placement constraints. We address this open problem by a new sharing policy, called Task Share Fairness (TSF), that provides provable isolation guarantees and is strategy-proof against gaming the allocation policy. TSF is shown to be envy-free and Pareto optimal as well.

Dynamic fair allocation of multiple resources with bounded number of tasks in cloud computing systems

Resource allocation is an important issue in any shared computer system. Recently, dynamic multi-resource fair allocation became a hot topic in cloud resource management. The most recent studies assume that each agent submits infinite number of tasks. In this paper, we consider a more realistic dynamic environment, where every agent submits a bounded number of tasks. We design a generalized dynamic dominant resource fairness mechanism, and develop a combinatorial optimal algorithm to find a fair allocation. Experimental results show that the solution produced by the proposed mechanism is close to the optimal solution. ©2015-IOS Press and the authors.

Multi-Resource Fair Allocation in Heterogeneous Cloud Computing Systems

We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no coalition behavior of misreporting resource demands can benefit all its members. DRFH also ensures some level of service isolation among the users. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.