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[서평]「Computer Organization and Design, The Hardware/Software Interface」

Lecture Notes in Economics and Mathematical Systems

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration.

Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.

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Ernest: efficient performance prediction for large-scale advanced analytics

A system and method for providing on-line, real-time, transparent data migration from an existing storage device to a replacement storage device. The existing and replacement storage devices are connected as a composite storage device that is coupled to a host, network or other data processing system. The replacement storage device includes a table which identifies data elements that have migrated to the replacement storage device. When a host system makes a data transfer request for one or more data elements, the replacement storage device determines whether the data elements have been migrated. If the data elements have migrated, the replacement storage device responds to the data transfer request independently of any interaction with the existing storage device. If the data elements have not migrated, the replacement storage device migrates the requested data elements and then responds to the data request and updates the data element map or table. When not busy servicing other requests, the replacement storage device operates in a background mode to migrate data elements so the data migration can occur concurrently with and transparently to system operations.

System and method for on-line, real time, data migration

Simulated annealing has proven to be a good technique for solving hard combinatorial optimization problems. Some attempts at speeding up annealing algorithms have been based on shared memory multiprocessor systems. Also parallelizations for certain problems on distributed memory multiprocessor systems are known.

Problem Independent Distributed Simulated Annealing and its Applications

Distributed high-performance computing (so-called metacomputing) refers to the coordinated use of a pool of geographically distributed high-performance computers. The user's view of an ideal metacomputer is that of a powerful monolithic virtual machine. The implementor's view, on the other hand, is that of a variety of interacting services implemented in a scalable and extensible manner. We present MOL, the Metacomputer Online environment. In contrast to other metacomputing environments, MOL is not based on specific programming models or tools. It has rather been designed as an open, extensible software system comprising a variety of software modules, each of them specialized in serving one specific task such as resource scheduling, job control, task communication, task migration, user interface, and much more. All of these modules exist and are working. The main challenge in the design of MOL lies in the specification of suitable, generic interfaces for the effective interaction between the modules.

The MOL project: an open, extensible metacomputer

A performance prediction method is presented, which accurately predicts the expected program execution time on massively parallel systems. We consider distributed-memory architectures with SMD nodes and a fast communication network. The method is based on a relaxed task graph model, a queuing model, and a memory hierarchy model. The relaxed task graph is a compact representation of communicating processes of an application mapped onto the target machine. Simultaneous accesses to the resources of a multi-processor node are modeled by a queuing network. The execution time of the application is computed by an evaluation algorithm. An example application implemented on a massively parallel computer demonstrates the high accuracy of our model. Furthermore, two applications of our accurate prediction method are presented.

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Accurate Performance Prediction for Assively Parallel Systems and Its Applications

Software as a Service (SaaS) providers enable the on-demand use of software, which is an intriguing concept for business and scientific applications. Typically, service level agreements (SLAs) are specified between the provider and the user, defining the required quality of service (QoS). Today SLA aware solutions only exist for business applications. We present a general SaaS architecture for scientific software that offers an easy-to-use web interface. Scientists define their problem description, the QoS requirements and can access the results through this portal. Our algorithms autonomously test the feasibility of the SLA and, if accepted, guarantee its fulfillment. This approach is independent of the underlying cloud infrastructure and successfully deals with performance fluctuations of cloud instances. Experiments are done with a scientific application in private and public clouds and we also present the implementation of a high-performance computing (HPC) cloud dedicated for scientific applications.

Enforcing SLAs in Scientific Clouds

The use of virtualization technology makes data centers more dynamic and easier to administrate. Today, cloud providers offer customers access to complex applications running on virtualized hardware. Nevertheless, big virtualized data centers become stochastic environments and the implification on the user side leads to many challenges for the provider. He has to find cost-efficient configurations and has to deal with dynamic environments to ensure service guarantees. In this paper, we introduce a software solution that reduces the degree of human intervention to manage cloud services. We present a multi-agent system located in the Software as a Service (SaaS) layer. Agents allocate resources, configure applications, check the feasibility of requests, and generate cost estimates. The agents learn behavior models of the services via Support Vector Machines (SVMs) and share their experiences via a global knowledge base. We evaluate our approach on real cloud systems with three different applications, a brokerage system, a high-performance computing software, and a web server.

Jens Simon

Papers

Problem Independent Distributed Simulated Annealing and its Applications

The MOL project: an open, extensible metacomputer

Accurate Performance Prediction for Assively Parallel Systems and Its Applications

Enforcing SLAs in Scientific Clouds

Autonomic Resource Management with Support Vector Machines