This paper investigates deploying a village level community radio application on an ad hoc network substrate comprising completely of basic mobile phones. We envision a system where any user in the network is equally empowered to generate and distribute audio content to the entire network, using his or her mobile phone. The paper focuses on the study of suitable broadcast algorithms for such a network. In this context we propose a novel broadcast technique called MaBA (Memoryaided Broadcast Algorithm) where nodes bank on their memory to decide whether to forward packets of an audio stream - capitalizing on their past behavior to stabilize on fixed routes for the entire stream. In our scheme called Environs Aware Broadcast Mechanism (EABA), a node gauges the local mobility around itself and uses that to decide which of two broadcast mechanisms to use. When mobility is high it uses SBA (Scalable Broadcast Algorithm), a well-known neighbor knowledge broadcast algorithm with relatively high overheads; but when mobility is low it switches to MaBA. Extensive simulations on a village level MANET confirm that EABA is successful in substantially reducing jitter, latency and packet loss: all critical metrics for an audio application. At the same time EABA does not incur other overheads and maintains the same levels of reachability and efficiency as SBA.

In large-scale, distributed systems such as Grids, an agreement between a client and a service provider specifies service level objectives both as expressions of client requirements and as provider assurances. From an application perspective, these objectives should be expressed in a high-level, service or application-specific manner rather than requiring clients to detail the necessary resources. Resource providers on the other hand, expect low-level, resource-specific performance criteria that are uniform across applications and can be easily interpreted and provisioned. This paper presents a framework for service management that addresses this gap between high-level specification of client performance objectives and existing resource management infrastructures. The paper identifies three levels of abstraction for resource requirements a service provider needs to manage, namely: detailed specification of raw resources, virtualization of heterogeneous resources as abstract resources, and performance objectives at an application level. The paper also identifies three key functions for managing service-level agreements, namely: translation of resource requirements across abstraction layers, arbitration in allocating resources to client requests, and aggregation and allocation of resources from multiple lower-level resource managers. One or more of these key functions may be present at each abstraction layer of a service-level manager. Thus, layering and the composition of these functions across abstraction layers enables modeling of a wide array of management scenarios. The framework we present uses service metadata and/or service performance models to map client requirements to resource capabilities, uses business value associated with objectives to arbitrate between competing requests, and allocates resources based on previously negotiated agreements. We instantiate this framework for three different scenarios and explain how the architectural principles we introduce are used in the real-word.

We study the nature of sharing resources in distributed collaborations such as Grids and peer-to-peer systems. By applying the theoretical framework of the multi-person prisoner's dilemma to this resource sharing problem, we show that in the absence of incentive schemes, individual users are apt to hold back resources, leading to decreased system utility. Using both the theoretical framework as well as simulations, we compare and contrast three different incentive schemes aimed at encouraging users to contribute resources. Our results show that soft-incentive schemes are effective in incentivizing autonomous entities to collaborate, leading to increased gains for all participants in the system.

Efficient data sharing in global peer-to-peer systems is complicated by erratic node failure, unreliable network connectivity and limited bandwidth. Replicating data on multiple nodes can improve availability and response time. Yet determining when and where to replicate data in order to meet performance goals in large-scale systems with many users and files, dynamic network characteristics, and changing user behavior is difficult. We propose an approach in which peers create replicas automatically in a decentralized fashion, as required to meet availability goals. The aim of our framework is to maintain a threshold level of availability at all times. We identify a set of factors that hinder data availability and propose a model that decides when more replication is necessary. We evaluate the accuracy and performance of the proposed model using simulations. Our preliminary results show that the model is effective in predicting the required number of replicas in the system.

In high-energy physics, bioinformatics, and other disciplines, we encounter applications involving numerous, loosely coupled jobs that both access and generate large data sets. So-called Data Grids seek to harness geographically distributed resources for such large-scale data-intensive problems. Yet effective scheduling in such environments is challenging, due to a need to address a variety of metrics and constraints while dealing with multiple, potentially independent sources of jobs and a large number of storage, compute, and network resources. We describe a scheduling framework that addresses these problems. Within this framework, data movement operations may be either tightly bound to job scheduling decisions or, alternatively, performed by a decoupled, asynchronous process on the basis of observed data access patterns and load. We develop a family of algorithms and use simulation studies to evaluate various combinations. Our results suggest that while it is necessary to consider the impact of replication, it is not always necessary to couple data movement and computation scheduling. Instead, these two activities can be addressed separately, thus significantly simplifying the design and implementation.