Quality of service QoS has been receiving wide attention in recent years in many research communities including networking, multimedia systems, real-time systems and distributed systems. In large distributed systems such as those used in defense systems, on-demand service and inter-networked systems, applications contending for system resources must satisfy timing, reliability and security constraints as well as application-speci c quality requirements. Allocating su cient resources to di erent applications in order to satisfy various requirements is a fundamental problem in these situations. A basic yet exible model for performance-driven resource a l l o cations can therefore b e useful in making appropriate tradeo s.In this paper, we present an analytical model for QoS management in systems which must satisfy application needs along multiple dimensions such as timeliness, reliable delivery schemes, cryptographic security and data quality. We refer to this model as Q-RAM QoS-based Resource A llocation Model. The model assumes a system with multiple concurrent applications, each of which can operate at di erent levels of quality based on the system resources available to it. The goal of the model is to be able to allocate resources to the various applications such that the overall system utility is maximized under the constraint that each application can meet its minimum needs. We identify resource p r o les of applications which allow such decisions to be made e ciently and in real-time. We also identify application utility functions along di erent dimensions which are c omposable to form unique application requirement pro les. We use a videoconferencing system to illustrate the model.

No abstract

Abstract

Today's highly available systems deliver four years of uninterrupted service.The challenge is to build systems with 100-year mean time to failure and one-minute repair times. September 1991aradoxically, the larger a system is, the more critical -but less likelyit is to be highly available. We can build small ultra-available modules, but building large systems involving thousands of modules and millions of lines of code is a poorly understood art, even though such large systems are a core technology of modern society.Three decades ago, hardware components were the major source of faults and outages. Today, hardware faults are a relatively minor cause of system outages when compared with operations, environment, and software faults. Techniques and designs that tolerate these broader classes of faults are still in their infancy.This article sketches the techniques used to build highly available computer systems. Historical perspectiveComputers built in the late 1950s offered a 12-hour mean time to failure. A maintenance staff of a dozen full-time computer engineers could repair the machine in about eight hours. This failure-repair cycle provided 60 percent availability. The vacuum tube and relay components of these computers were the major sources of failures; they had lifetimes of a few months. So the machines rarely operated for more than a day without interruption.'Many fault-detection and fault-masking techniques used today were first used on these early computers. Diagnostics tested the machine. Self-checking computational techniques detected faults while the computation progressed. The program occasionally saved (checkpointed) its state on stable media. After a failure and repair, the program read the most recent checkpoint and continued the computation from that point. This checkpoint-restart technique let computers that failed every few hours perform long-running computations.Device improvements have increased computer system availability. By 1980, typical well-run computer systems offered 99 percent availability.2 This sounds good, but 99 percent availability is 100 minutes of downtime per week. Such

The QoS-based Resource Allocation Model (Q-RAM) proposed in [20] presented an analytical approach for satisfying multiple quality-of-service dimensions in a resource-constrained environment. Using this model, available system resources can be apportioned across multiple applications such that the net utility that accrues to the end-users of those applications is maximized. In this paper, we present several practical solutions to allocation problems that were beyond the limited scope of [20]. First, we show that the Q-RAM problem of finding the optimal resource allocation to satisfy multiple QoS dimensions (at least one of which is dependent on another) is NP-hard. We then present a polynomial solution for this resource allocation problem which yields a solution within a provably fixed and short distance from the optimal allocation. Secondly, [20] dealt mainly with the problem of apportioning a single resource to satisfy multiple QoS dimensions. In this paper, we study the converse problem of apportioning multiple resources to satisfy a single QoS dimension. In practice, this problem becomes complicated, since a single QoS dimension perceived by the user can be satisfied using different combinations of available resources. We show that this problem can be formulated as a mixed integer programming problem that can be solved efficiently to yield an optimal resource allocation. Finally, we also present the run-times of these optimizations to illustrate how these solutions can be applied in practice. We expect that a good understanding of these solutions will yield insights into the general problem of apportioning multiple resources to satisfy simultaneously multiple QoS dimensions of multiple concurrent applications.