This paper surveys the design of embedded computer systems, which use software running on programmable computers to im-plement system functions. Creating an embedded computer system which meets its performance, cost, and design time goals is a hardware-software co-design problewhe design of the hard-ware and software components influence each other. This paper emphasizes a historical approach to show the relationships be-tween well-understood design problems and the as-yet unsolved problems in co-design. We describe the relationship between hard-ware and sofhvare architecture in the early stages of embedded system design. We describe analysis techniques for hardware and software relevant to the architectural choices required for hard-ware-software co-design. We also describe design and synthesis techniques for co-design and related problems.

Workflow management systems (WfMSs) have been used to support various types of business processes for more than a decade now. In workflows for e-commerce and Web-services applications, suppliers and customers define a binding agreement or contract between the two parties, specifying Quality of Service (QoS) items such as products or services to be delivered, deadlines, quality of products, and cost of services. The management of QoS metrics directly impacts the success of organizations participating in e-commerce. Therefore, when services or products are created or managed using workflows, the underlying workflow system must accept the specifications and be able to estimate, monitor, and control the QoS rendered to customers. In this paper, we present a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes. To this end, we present a model that specifies QoS and describe an algorithm and a simulation system in order to compute, analyze and monitor workflow QoS metrics. 1

Peer-peer networking has recently emerged as a new paradigm for building distributed networked applications. In this paper we develop simple mathematical models to explore and illustrate fundamental performance issues of peer-peer file sharing systems. The modeling framework introduced and the corresponding solution method are flexible enough to accommodate different characteristics of such systems. Through the specification of model parameters, we apply our framework to three different peer-peer architectures: centralized indexing, distributed indexing with flooded queries, and distributed indexing with hashing directed queries. Using our model, we investigate the effects of system scaling, freeloaders, file popularity and availability on system performance. In particular, we observe that a system with distributed indexing and flooded queries cannot exploit the full capacity of peer-peer systems. We further show that peer-peer file sharing systems can tolerate a significant number of freeloaders without suffering much performance degradation. In many cases, freeloaders can benefit from the available spare capacity of peer-peer systems and increase overall system throughput. Our work shows that simple models coupled with efficient solution methods can be used to understand and answer questions related to the performance of peer-peer file sharing systems.

In this paper we define the notions of agent matchmaking and brokering behaviors that are used while processing requests among initially unacquainted sets of agents. These behaviors are basic components of common organizational roles and thus must be understood by computational agents. Brokering and matchmaking behaviors can be used to construct organizational forms such as centralized or distributed markets, ad hoc teams, and bureaucratic functional or product hierarchies. Each behavior brings with it certain performance characteristics---cost, robustness, and adaptiveness qualities---that are related to characteristics of the external environment and of the agents themselves. For example, while brokered systems are more vulnerable to certain failures, they are also able to cope more quickly with a rapidly fluctuating agent workforce. We present several agent design constraints and related models, which are experimentally validated using the WARREN multi-agent portfolio management sy...

This paper studies the performance of single-user workstations that access files remotely over a local area network. From the environmental, economic, and administrative points of view, workstations that are diskless or that have limited secondary storage are desirable at the present time. Even with changing technology, access to shared data will continue to be important. It is likely that some performance penalty must be paid for remote rather than local file access. Our objectives are to assess this penalty and to explore a number of design alternatives that can serve to minimize it. Our approach is to use the results of measurement experiments to parameterize queuing network performance models. These models then are used to assess performance under load and to evahrate design alternatives. The major conclusions of our study are: (1) A system of diskless workstations with a shared file server can have satisfactory performance. By this, we mean performance comparable to that of a local disk in the lightly loaded case, and the ability to support substantial numbers of client workstations without significant degradation. As with any shared facility, good design is necessary to minimize queuing delays under high load. (2) The key to efficiency is protocols that allow volume transfers at every interface (e.g., between client and server, and between disk and memory at the server) and at every level (e.g., between client and server at the level of logical

Service differentiation that provides prioritized service qualities to multiple classes of client requests can effectively utilize available server resources. This paper studies how demand-driven service differentiation in terms of end-user performance can be supported in cluster-based network servers. Our objective is to deliver better services to high priority request classes without over-sacrificing low priority classes. To achieve this objective, we propose a dynamic scheduling scheme, called DDSD, that adapts to fluctuating request resource demands by periodically repartitioning servers. This scheme also employs priority-based admission control to drop excessive user requests and achieve soft performance guarantees. For each scheduling period, our scheme monitors the system status and uses a queuing model to approximate server behaviors and guide resource allocation. Our experiments show that the proposed technique achieves demand-driven service differentiation while maximizing resource utilization and that it can substantially outperform static server partitioning.

Standard locking (two-phase locking with on-demand lock requests and blocking upon lock conflict) is the primary concurrency control (CC) method for centralized databases. The main source of performance degradation with standard locking is blocking, whereas transaction (txn) restarts to resolve deadlocks have a secondary effect on performance. We provide a performance analysis of standard locking that accurately estimates its performance degradation leading to thrashing. We next introduce two sets of methods to cope with its performance limitations. Restartoriented locking methods selectively abort txns to increase the level of concurrency for active txns with respect to standard locking in high-contention environments. For example, the running-priority method aborts blocked txns based on the essential blocking principle, which only allows blocking by active txns. The waitdepth-limited (WDL) method further minimizes wasted processing by basing abort decisions on the progress made by a txn. Restart waiting serves as a load-control mechanism by deferring the restart of an aborted txn until conflicting txns have left the system. These two methods have performance superior to other restartoriented

Performance monitoring in most distributed systems provides minimal guidance for tuning, problem diagnosis, and decision making. Stardust is a monitoring infrastructure that replaces traditional performance counters with end-to-end traces of requests and allows for efficient querying of performance metrics. Such traces better inform key administrative performance challenges by enabling, for example, extraction of per-workload, per-resource demand information and per-workload latency graphs. This paper reports on our experience building and using end-to-end tracing as an on-line monitoring tool in a distributed storage system. Using diverse system workloads and scenarios, we show that such fine-grained tracing can be made efficient (less than 6% overhead) and is useful for on- and off-line analysis of system behavior. These experiences make a case for having other systems incorporate such an instrumentation framework.

AbstractÐMany distributed systems must be scalable, meaning that they must be economically deployable in a wide range of sizes and configurations. This paper presents a scalability metric based on cost-effectiveness, where the effectiveness is a function of the system's throughput and its quality of service. It is part of a framework which also includes a scaling strategy for introducing changes as a function of a scale factor, and an automated virtual design optimization at each scale factor. This is an adaptation of concepts for scalability measures in parallel computing. Scalability is measured by the range of scale factors that give a satisfactory value of the metric, and good scalability is a joint property of the initial design and the scaling strategy. The results give insight into the scaling capacity of the designs, and into how to improve the design. A rapid simple bound on the metric is also described. The metric is demonstrated in this work by applying it to some well-known idealized systems, and to real prototypes of communications software. Index TermsÐScalability, distributed systems, scalability metric, software performance, performance model, layered queuing, performance optimization, replication. 1

Abstract not found