Multi-tier data-intensive applications are widely deployed in virtualized data centers for high scalability and reliability. As the response time is vital for user satisfaction, this requires achieving good performance at each tier of the applications in order to minimize the overall latency. However, in such virtualized environments, each tier (e.g., application, database, web) is likely to be hosted by different virtual machines (VMs) on multiple physical servers, where a guest VM is unaware of changes outside its domain, and the hypervisor also does not know the configuration and runtime status of a guest VM. As a result, isolated virtualization domains lend themselves to performance unpredictability and variance. In this paper, we propose IOrchestra, a holistic collaborative virtualization framework, which bridges the semantic gaps of I/O stacks and system information across multiple VMs, improves virtual I/O performance through collaboration from guest domains, and increases resource utilization in data centers. We present several case studies to demonstrate that IOrchestra is able to address numerous drawbacks of the current practice and improve the I/O latency of various distributed cloud applications by up to 31%.

This paper presents the design and implementation of PUMA, a declarative constraint-solving platform for policy-based routing and channel selection in multiradio wireless mesh networks. In PUMA, users formulate channel selection policies as optimization goals and constraints that are concisely declared using the Colog declarative language. To efficiently execute Colog programs in a distributed setting, PUMA integrates a high-performance constraint solver with a declarative networking engine. We demonstrate the capabilities of PUMA in defining distributed protocols that cross-optimize across channel selection and routing. We have developed a prototype of the PUMA system that we extensively evaluated in simulations and on the ORBIT testbed. Our experimental results demonstrate that PUMA can flexibly and efficiently implement a variety of centralized and distributed channel selection protocols that result in significantly higher throughput compared to single-channel and identical-channel assignment solutions.

This paper presents Cologne, a declarative optimization platform that enables constraint optimization problems (COPs) to be declaratively specified and incrementally executed in distributed systems. Cologne integrates a declarative networking engine with an off-the-shelf constraint solver. We have developed the Colog language that combines distributed Datalog used in declarative networking with language constructs for specifying goals and constraints used in COPs. Cologne uses novel query processing strategies for processing Colog programs, by combining the use of bottom-up distributed Datalog evaluation with top-down goal-oriented constraint solving. Using case studies based on cloud and wireless network optimizations, we demonstrate that Cologne (1) can flexibly support a wide range of policy-based optimizations in distributed systems, (2) results in orders of magnitude less code compared to imperative implementations, and (3) is highly efficient with low overhead and fast convergence times.

This paper presents DAWN, a declarative platform that creates highly adaptive policy-based mobile ad hoc network (MANET) protocols. DAWN leverages declarative networking techniques to achieve extensible routing and forwarding using declarative languages. We make the following contributions. First, we demonstrate that traditional MANET protocols can be expressed in a concise fashion as declarative networks and policy-driven adaptation can be specified in the same language to dictate the dynamic selection of different protocols based on various network and traffic conditions. Second, we propose interprotocol forwarding techniques that ensure packets are able to seamlessly traverse across clusters of nodes running different protocols selected based on their respective policies. Third, we have developed a full-fledged implementation of DAWN using the RapidNet declarative networking system. We experimentally validate a variety of policy-based adaptive MANETs in various dynamic settings using a combination of ns-3 simulations and deployment on the ORBIT testbed. Our experimental results demonstrate that hybrid protocols developed using DAWN outperform traditional MANET routing protocols and are able to flexibly and dynamically adapt their routing mechanisms to achieve a good tradeoff between bandwidth utilization and route quality. We further demonstrate DAWN's capabilities to achieve interprotocol forwarding across different protocols.

As cloud computing becomes widely deployed, one of the challenges faced involves the ability to orchestrate a highly complex set of subsystems (compute, storage, network resources) that span large geographic areas serving diverse clients. To ease this process, we present COPE (Cloud Orchestration Policy Engine), a distributed platform that allows cloud providers to perform declarative automated cloud resource orchestration. In COPE, cloud providers specify system-wide constraints and goals using COPElog, a declarative policy language geared towards specifying distributed constraint optimizations. COPE takes policy specifications and cloud system states as input and then optimizes compute, storage and network resource allocations within the cloud such that provider operational objectives and customer SLAs can be better met. We describe our proposed integration with a cloud orchestration platform, and present initial evaluation results that demonstrate the viability of COPE using production traces from a large hosting company in the US. We further discuss an orchestration scenario that involves geographically distributed data centers, and conclude with an ongoing status of our work.

This paper presents the design and implementation of declarative policy-based adaptive MANET routing protocols. Our work builds upon declarative networking, a recent innovation for building extensible network architectures using declarative languages. We make the following contributions. First, we demonstrate that traditional MANET protocols can be expressed in a compact fashion as declarative networks. We validate these declarative protocols via an experimental study on the ORBIT wireless testbed and a cluster-based emulation environment. Second, we demonstrate that policy-driven adaptation can be specified in a generic set of declarative rule-based policies that dictate the dynamic selection of different protocols based on network conditions. Third, we conduct extensive evaluation results of declarative policy-based adaptation of MANET routing on the ORBIT wireless testbed and the cluster-based emulation environment. Our experimental results show that the specified policies enable MANETs to dynamically hybridize a variety of routing protocols to achieve a good tradeoff in bandwidth utilization and route quality.