Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers

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 user has an incentive to lie about its resource demand. 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

An Appropriate Parameterized Utility Technique On Heterogeneous Server Dependencies

A new server-based approach incorporated in Heterogeneous Servers. Current cloudinfrastructures are mostly homogeneous composed of a large number of machines of the same type – centrally managed and made available to the end user.In a cloud computing pattern, multiple resources types were utilizing. 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. 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 resource parameterized mechanism, we develop an iterative algorithm which is shown to be globally convergent on Heterogeneous server dependencies

MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users’ tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRF

UDRF: Multi-resource Fairness for Complex Jobs with Placement Constraints

In this paper, we study the problem of multi- resource fairness in systems running complex jobs that consist of multiple interconnected tasks. A job is considered finished when all its corresponding tasks have been executed in the system. Tasks can have different resource requirements. Because of special demands on particular hardware or software, tasks may have placement constraints limiting the type of machines they can run on. We develop User-Dependence Dominant Resource Fairness (UDRF), a generalized version of max-min fairness that combines graph theory and the notion of dominant re- source shares to ensure multi-resource fairness between complex workflows. UDRF satisfies several desirable properties including strategy proofness, which ensures that users do not benefit from misreporting their true resource demands. We propose an offline algorithm that computes optimal UDRF allocation. But optimality comes at a cost, especially for systems where schedulers need to make thousands of online scheduling decisions per second. Therefore, we develop a lightweight online algorithm that closely approximates UDRF. Besides that, we propose a simple mechanism to decentralize the UDRF scheduling process across multiple schedulers. Large-scale simulations driven by Google cluster-usage traces show that UDRF achieves better resource utilization and throughput compared to the current state-of-the-art in fair resource allocation

Multi-Resource Fair Allocation In Heterogeneous Cloud Computing Systems

A Multi-Resource Fair Allocation Mechanism, Also Called Per-Server Dominant Share Fairness (PSDSF), Which Is Appropriate To Heterogeneous Servers In The Occurrence Of Position Restraint. The Instinct Following PSDSF Is To Imprison The Collision Of Server Heterogeneity By Compute The Entirety Allocated Possessions To Each User Clearly From The Outlook Of Each Server. Particularly, PS-DSF Makes Out A Practical Dominant Share (VDS) For Each User With Deference To Each Server As Opposed To A Single System-Wide Dominant Share In DRF. Capable And Fair Resource Distribution In Such A Collective Computing System Is For The Most Part Exigent Since Of The Incidence Of Multiple Types Of Resources, Multiplicity In The Workloads’ Needs For These Possessions, Heterogeneity In The Resource Capacities Of Servers, And Assignment Restraint On Which Servers May Be Used By A Workload

Symbiot: Congestion-driven Multi-resource Fairness for Multi-User Sensor Networks

© 2015 IEEE.In this paper, we study the problem of multi-resource fairness in multi-user sensor networks with heterogeneous and time-varying resources. Particularly we focus on data gathering applications run on Wireless Sensor Networks (WSNs) or Internet of Things (IoT) in which users require to run a serious of sensing operations with various resource requirements. We consider both the resource demands of sensing tasks, and data forwarding tasks needed to establish multi-hop relay communications. By exploiting graph theory, queueing theory and the notion of dominant resource shares, we develop Symbiot, a light-weight, distributed algorithm that ensures multi-resource fairness between these users. With Symbiot, nodes can independently schedule its resources while maintaining network-level resource fairness through observing traffic congestion levels. Large-scale simulations based Contiki OS and Cooja network emulator show the effectiveness of Symbiot in adaptively utilizing available resources and reducing average completion times