An Analysis of MOSIX Load Balancing Capabilities (original) (raw)

Load balancing for heterogeneous clusters of PCs

Future Generation Computer Systems, 2002

With commercial supercomputers and homogeneous clusters of PCs, static load balancing is accomplished by assigning equal tasks to each processor. With heterogeneous clusters, the system designers have the option of quickly adding newer hardware that is more powerful than the existing hardware. When this is done, the assignment of equal tasks to each processor results in suboptimal performance.

A protocol for load sharing among a cluster of heterogeneous Unix workstations

Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid, 2001

In this paper, we propose a protocol for load sharing among a cluster of heterogeneous Unix workstations. Our protocol, called the Distributed Process Management Protocol (DPMP), not only enables load sharing using nonpreemptive process migration but also seamlessly integrates the processes running on a network of machines. Remote processes can be accessed (for signalling, for example), in the same way as local processes making process migration highly transparent to the users and the applications. DPM-P also has builtin mechanisms to detect and recover from node and network failures. DPMP can be implemented at either the kernel or the user level. We also describe an implementation of DPMP within the Linux kernel. Preliminary performance studies show that the performance gains obtained by using DPMP are substantial.

Mosix the Cluster Operating System Having Advancements & Many Features

Mosix is a running of modifications to the Linux kernel. MOSIX Design Objectives turn a network of Linux computers into a High Performance Cluster computer. The Founder o f MOSIX is the Amnon Barak. MOSIX is a cluster operating system that provides users and applications with the impression of running on a single computer with multiple processors which is called as single - system image and Hide cluster complexity to users. T his paper describes the enhancement of MOSIX to openMosix and its cloud environment. There are many advance features of MOSIX by which large number of appli cation work fastly and properly. Balancing Load is the most effective feature we mentioned it in thi s paper.

An algorithm for load balancing in multiprocessor systems

Information Processing Letters, 1990

We present an algorithm for dynamic load balancing in a multiprocessor system that minimizes the number of accesses to the shared memory. The algorithm makes no assumptions, probabilistic or otherwise, regarding task arrivals or processing requirements. For k processors to process n tasks, the algorithm incurs O(k log k log n) potential memory collisions in the worst care. The algorithm itself is a simple variation of the strategy of visiting the longest queue. T'he key idea is to delay reporting task arrivals and completions, where the delay is a function of dynamic loading conditions.

Dynamic load balancing in heterogeneous clusters

Pdcn, 2004

The dynamic load balancing techniques, practically, do not assume any information about the tasks to be executed at compilation time. Parameters like execution time or communication time are unknown at compilation time. These techniques are used to distribute the computation tasks of an application between different processors at execution time to achieve some defined performance objectives [1]. In this paper we present a dynamic load balancing algorithm designed especially for heterogeneous network of workstations. The algorithm distributes the parallel tasks dynamically attempting to minimize its execution time. The experiments are done over a network of workstation interconnected via a fast Ethernet. It is a Linux cluster which has some degree of heterogeneity in the processing nodes. Our algorithm is shown to be efficient in increasing the resource utilization and reducing the total execution time of the applications.

Deploying CPU Load Balancing in the Linux Cluster Using Non-Repetitive CPU Selection

Maintaining load balancing in a computing cluster is an evident problem in distributed systems and research in this field is not new. The challenges in designing the load balancing algorithms are immense. This paper lists some of those challenges in the design of CPU load balancing algorithm and provides solutions to some of them. The algorithm considers one node in the cluster as the Master Server and another as the Load Balancer. The master server maintains the CPU and IP information of each machine. The nodes in the cluster send their CPU status and IP information to the master server after every 30 seconds. The implementation solves “readers-writers” problem exclusively using sockets. If a number of requests are sent before the next central database update, the load balancer selects other less busy nodes in the cluster. This ensures that all nodes are allocated with the new tasks coming from remote systems, thereby maintaining a load balance among the CPUs. This implementation is highly fault tolerant and reliable, guaranteeing a high probability of task completion. Results show that this scheme handles task allocation in much optimized way and with fewer overheads. The implementation can handle CPUs ranging in numbers from 1 to 255.

PODOS -- The design and implementation of a performance oriented Linux cluster

Future Gener. Comput. Syst., 2002

PODOS is a performance oriented distributed operating system being developed to harness the performance capabilities of a cluster-computing environment. In order to address the growing demand for performance, we are designing a distributed operating system (DOS) that can utilize the computing potential of a number of systems. Earlier clustering approaches have traditionally stressed more on resource sharing or reliability and have given lesser priority to performance. PODOS adds just four new components to the existing Linux operating system to make it distributed. These components are a Communication Manager (CM), a PODOS Distributed File System (PDFS), a Resource Manager (RM), and Global Interprocess Communication (GIPC). This paper addresses the design and implementation of the various components of the PODOS system.