Fine Grained Component Engineering of Adaptive Overlays: Experiences and Perspectives

Recent years have seen significant research being carried out into peer-to-peer (P2P) systems. This work has focused on the styles and applications of P2P computing, from grid computation to content distribution; however, little investigation has been performed into how these systems are built. Component based engineering is an approach that has seen successful deployment in the field of middleware development; functionality is encapsulated in ‘building blocks’ that can be dynamically plugged together to form complete systems. This allows efficient, flexible and adaptable systems to be built with lower overhead and development complexity. This paper presents an investigation into the potential of using component based engineering in the design and construction of peer-to-peer overlays. It is highlighted that the quality of these properties is dictated by the component architecture used to implement the system. Three reusable decomposition architectures are designed and evaluated using Chord and Pastry case studies. These demonstrate that significant improvements can be made over traditional design approaches resulting in much more reusable, (re)configurable and extensible systems

Where are we at with Cloud Computing?: A Descriptive Literature Review

Cloud computing is an exciting area for research, because of its relative novelty and exploding growth. In this paper, we present a descriptive literature review and classification scheme for cloud computing research. The former consists of 58 articles published since the recent inception of cloud computing. Clearly, there is an explosively increasing amount of cloud computing research has been conducted this year. The articles are classified and results are presented, based on a scheme that consists of four main categories: technological issues, business issues, applications, and general. The results show that although current cloud computing research is still skewed towards technological issues, such as performance, network, and data management, new research theme regarding the social and organisational implications of cloud computing is emerging. We hope this review will provide a snapshot and reference source of the current state of cloud computing research and stimulate further research interest

Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

Fairness-aware scheduling on single-ISA heterogeneous multi-cores

Single-ISA heterogeneous multi-cores consisting of small (e.g., in-order) and big (e.g., out-of-order) cores dramatically improve energy- and power-efficiency by scheduling workloads on the most appropriate core type. A significant body of recent work has focused on improving system throughput through scheduling. However, none of the prior work has looked into fairness. Yet, guaranteeing that all threads make equal progress on heterogeneous multi-cores is of utmost importance for both multi-threaded and multi-program workloads to improve performance and quality-of-service. Furthermore, modern operating systems affinitize workloads to cores (pinned scheduling) which dramatically affects fairness on heterogeneous multi-cores. In this paper, we propose fairness-aware scheduling for single-ISA heterogeneous multi-cores, and explore two flavors for doing so. Equal-time scheduling runs each thread or workload on each core type for an equal fraction of the time, whereas equal-progress scheduling strives at getting equal amounts of work done on each core type. Our experimental results demonstrate an average 14% (and up to 25%) performance improvement over pinned scheduling through fairness-aware scheduling for homogeneous multi-threaded workloads; equal-progress scheduling improves performance by 32% on average for heterogeneous multi-threaded workloads. Further, we report dramatic improvements in fairness over prior scheduling proposals for multi-program workloads, while achieving system throughput comparable to throughput-optimized scheduling, and an average 21% improvement in throughput over pinned scheduling

Using Virtual Markets to Program Global Behavior in Sensor Networks

This paper presents market-based macroprogramming (MBM), a new paradigm for achieving globally efficient behavior in sensor networks. Rather than programming the individual, low-level behaviors of sensor nodes, MBM defines a virtual market where nodes sell "actions" (such as taking a sensor reading or aggregating data) in response to global price information. Nodes take actions to maximize their own utility, subject to energy budget constraints. The behavior of the network is determined by adjusting the price vectors for each action, rather than by directly specifying local node actions, resulting in a globally efficient allocation of network resources. We present the market-based macro-programming paradigm, as well as several experiments demonstrating its value for a sensor network vehicle tracking application.Engineering and Applied Science

A Survey of Research on Power Management Techniques for High Performance Systems

This paper surveys the research on power management techniques for high performance systems. These include both commercial high performance clusters and scientific high performance computing (HPC) systems. Power consumption has rapidly risen to an intolerable scale. This results in both high operating costs and high failure rates so it is now a major cause for concern. It is imposed new challenges to the development of high performance systems. In this paper, we first review the basic mechanisms that underlie power management techniques. Then we survey two fundamental techniques for power management: metrics and profiling. After that, we review the research for the two major types of high performance systems: commercial clusters and supercomputers. Based on this, we discuss the new opportunities and problems presented by the recent adoption of virtualization techniques, and again we present the most recent research on this. Finally, we summarise and discuss future research directions