Process Management in Distributed Operating Systems

As part of designing and building the Amoeba distributed operating system, we have come up with a simple set of mechanisms for process management that allows downloading process migration, checkpointing, remote debugging and emulation of alien operating system interfaces.\ud The basic process management facilities are realized by the Amoeba Kernel and can be augmented by user-space services: Debug Service, Load-Balancing Service, Unix-Emulation Service, Checkpoint Service, etc.\ud The Amoeba Kernel can produce a representation of the state of a process which can be given to another Kernel where it is accepted for continued execution. This state consists of the memory contents in the form of a collection of segments, and a Process Descriptor which contains the additional state, program counters, stack pointers, system call state, etc.\ud Careful separation of mechanism and policy has resulted in a compact set of Kernel operations for process creation and management. A collection of user-space services provides process management policies and a simple interface for application programs.\ud In this paper we shall describe the mechanisms as they are being implemented in the Amoeba Distributed System at the Centre for Mathematics and Computer Science in Amsterdam. We believe that the mechanisms described here can also apply to other distributed systems

Designing SSI clusters with hierarchical checkpointing and single I/O space

Adopting a new hierarchical checkpointing architecture, the authors develop a single I/O address space for building highly available clusters of computers. They propose a systematic approach to achieving a single system image by integrating existing middleware support with the newly developed features.published_or_final_versio

Investigating grid computing technologies for use with commercial simulation packages

As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Single system image: A survey

Single system image is a computing paradigm where a number of distributed computing resources are aggregated and presented via an interface that maintains the illusion of interaction with a single system. This approach encompasses decades of research using a broad variety of techniques at varying levels of abstraction, from custom hardware and distributed hypervisors to specialized operating system kernels and user-level tools. Existing classification schemes for SSI technologies are reviewed, and an updated classification scheme is proposed. A survey of implementation techniques is provided along with relevant examples. Notable deployments are examined and insights gained from hands-on experience are summarized. Issues affecting the adoption of kernel-level SSI are identified and discussed in the context of technology adoption literature

Experimental Study of Remote Job Submission and Execution on LRM through Grid Computing Mechanisms

Remote job submission and execution is fundamental requirement of distributed computing done using Cluster computing. However, Cluster computing limits usage within a single organization. Grid computing environment can allow use of resources for remote job execution that are available in other organizations. This paper discusses concepts of batch-job execution using LRM and using Grid. The paper discusses two ways of preparing test Grid computing environment that we use for experimental testing of concepts. This paper presents experimental testing of remote job submission and execution mechanisms through LRM specific way and Grid computing ways. Moreover, the paper also discusses various problems faced while working with Grid computing environment and discusses their trouble-shootings. The understanding and experimental testing presented in this paper would become very useful to researchers who are new to the field of job management in Grid.Comment: Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), 201

Cluster Computing Review

In the past decade there has been a dramatic shift from mainframe or ‘host−centric’ computing to a distributed ‘client−server’ approach. In the next few years this trend is likely to continue with further shifts towards ‘network−centric’ computing becoming apparent. All these trends were set in motion by the invention of the mass−reproducible microprocessor by Ted Hoff of Intel some twenty−odd years ago. The present generation of RISC microprocessors are now more than a match for mainframes in terms of cost and performance. The long−foreseen day when collections of RISC microprocessors assembled together as a parallel computer could out perform the vector supercomputers has finally arrived. Such high−performance parallel computers incorporate proprietary interconnection networks allowing low−latency, high bandwidth inter−processor communications. However, for certain types of applications such interconnect optimization is unnecessary and conventional LAN technology is sufficient. This has led to the realization that clusters of high−performance workstations can be realistically used for a variety of applications either to replace mainframes, vector supercomputers and parallel computers or to better manage already installed collections of workstations. Whilst it is clear that ‘cluster computers’ have limitations, many institutions and companies are exploring this option. Software to manage such clusters is at an early stage of development and this report reviews the current state−of−the−art. Cluster computing is a rapidly maturing technology that seems certain to play an important part in the ‘network−centric’ computing future