Constantin Adam

Service Middleware for Self-Managing Large-Scale Systems

Resource management poses particular challenges in large-scale systems, such as server clusters that simultaneously process requests from a large number of clients. A resource management scheme for such systems must scale both in the in the number of cluster nodes and the number of applications the cluster supports. Current solutions do not exhibit both of these properties at the same time. Many are centralized, which limits their scalability in terms of the number of nodes, or they are decentralized but rely on replicated directories, which also reduces their ability to scale. In this paper, we propose novel solutions to request routing and application placement- two key mechanisms in a scalable resource management scheme. Our solution to request routing is based on selective update propagation, which ensures that the control load on a cluster node is independent of the system size. Application placement is approached in a decentralized manner, by using a distributed algorithm that maximizes resource utilization and allows for service differentiation under overload. The paper demonstrates how the above solutions can be integrated into an overall design for a peer-to-peer management middleware that exhibits properties of self-organization. Through complexity analysis and simulation, we show to which extent the system design is scalable. We have built a prototype using accepted technologies and have evaluated it using a standard benchmark. The testbed measurements show that the implementation, within the parameter range tested, operates efficiently, quickly adapts to a changing environment and allows for effective service differentiation by a system administrator.

Adaptable server clusters with QoS objectives

We present a decentralized design for a server cluster that supports a single service with response time guarantees. Three distributed mechanisms represent the key elements of our design. Topology construction maintains a dynamic overlay of cluster nodes. Request routing directs service requests towards available servers. Membership control allocates/releases servers to/from the cluster, in response to changes in the external load. We advocate a decentralized approach, because it is scalable, fault-tolerant, and has a lower configuration complexity than a centralized solution. We demonstrate through simulations that our system operates efficiently by comparing it to an ideal centralized system. In addition, we show that our system rapidly adapts to changing load. We found that the interaction of the various mechanisms in the system leads to desirable global properties. More precisely, for a fixed connectivity c (i.e., the number of neighbors of a node in the overlay), the average experienced delay in the cluster is independent of the external load. In addition, increasing c increases the average delay but decreases the system size for a given load. Consequently, the cluster administrator can use c as a management parameter that permits control of the tradeoff between a small system size and a small experienced delay for the service.

A Service Middleware that Scales in System Size and Applications

We present a peer-to-peer service management middleware that dynamically allocates system resources to a large set of applications. The system achieves scalability in number of nodes (1000s or more) through three decentralized mechanisms that run on different time scales. First, overlay construction interconnects all nodes in the system for exchanging control and state information. Second, request routing directs requests to nodes that offer the corresponding applications. Third, application placement controls the set of offered applications on each node, in order to achieve efficient operation and service differentiation. The design supports a large number of applications (100s or more) through selective propagation of configuration information needed for request routing. The control load on a node increases linearly with the number of applications in the system. Service differentiation is achieved through assigning a utility to each application, which influences the application placement process. Simulation studies show that the system operates efficiently for different sizes, adapts fast to load changes and failures and effectively differentiates between different applications under overload.

A middleware design for large-scale clusters offering multiple services

We present a decentralized design that dynamically allocates resources to multiple services inside a global server cluster. The design supports QoS objectives (maximum response time and maximum loss rate) for each service. A system administrator can modify policies that assign relative importance to services and, in this way, control the resource allocation process. Distinctive features of our design are the use of an epidemic protocol to disseminate state and control information, as well as the decentralized evaluation of utility functions to control resource partitioning among services. Simulation results show that the system operates both effectively and efficiently; it meets the QoS objectives and dynamically adapts to load changes and to failures. In case of overload, the service quality degrades gracefully, controlled by the cluster policies.

Distributed Resource Allocation Strategies for Achieving Quality of Service in Server Clusters

We investigate the resource allocation problem for large-scale server clusters with quality-of-service objectives, where key functions are decentralized. Specifically, the optimal service selection is posed as a discrete utility maximization problem that reflects management objectives and resource constraints. We develop an efficient centralized algorithm that solves this problem, and we propose three suboptimal schemes that operate with local information. The performance of the suboptimal schemes is evaluated in simulations, both under idealized conditions and in a full-scale system simulator

Patterns for routing and self-stabilization

The paper contributes towards engineering self-stabilizing networks and services. We propose the use of navigation patterns, which define how information for state updates is disseminated in the system, as fundamental building blocks for self-stabilizing systems. We present two navigation patterns for self-stabilization: the progressive wave pattern and the stationary wave pattern. The progressive wave pattern defines the update dissemination in Internet routing systems running the DUAL and OSPF (open shortest path first) protocols. Similarly, the stationary wave pattern defines the interactions of peer nodes in structured peer-to-peer systems, including Chord, Pastry, Tapestry, and CAN. It turns out that the two patterns are related. They both disseminate information in the form of waves, i.e, sets of messages that originate from single events. Patterns can be instrumented to obtain wave statistics, which enables monitoring the process of self-stabilization in a system. We focus on Internet routing and peer-to-peer systems, since we believe that studying these (existing) systems can lead to engineering principles for self-stabilizing systems in various application areas.