The recent emergence of clouds is making the vision of utility computing realizable, i.e., computing resources and services can be delivered, utilized, and paid for as utilities such as water or electricity. This, however, creates new resource provisioning problems. Because of the pay-as-you-go model, resource provisioning should be performed in a way to keep resource costs to a minimum, while meeting an application's needs. In this work, we focus on the use of cloud resources for a class of adaptive applications, where there could be application-specific flexibility in the computation that may be desired. Furthermore, there may be a fixed time-limit as well as a resource budget. Within these constraints, such adaptive applications need to maximize their Quality of Service (QoS), more precisely, the value of an application-specific benefit function, by dynamically changing adaptive parameters. We present the design, implementation, and evaluation of a framework that can support such dynamic adaptation for applications in a cloud computing environment. The key component of our framework is a multi-input-multi-output feedback control model-based dynamic resource provisioning algorithm which adopts reinforcement learning to adjust adaptive parameters to guarantee the optimal application benefit within the time constraint. Then a trained resource model changes resource allocation accordingly to satisfy the budget. We have evaluated our framework with two real-world adaptive applications, and have demonstrated that our approach is effective and causes a very low overhead.

The recent emergence of, cloud computing is making the, vision, of, utility computing, realizable, i.e., computing resources and services from a cloud can be delivered, utilized, and paid for in the same fashion as utilities like water or electricity. Current, cloud service providers have taken some steps towards supporting the true, pay-as-you-go or a utility-like pricing model, and current research points towards more fine-grained, allocation and pricing of resources in the future., In such environments, resource provisioning becomes a challenging problem, since one needs to avoid both under-provisioning (leading to application slowdown) and over-provisioning (leading to unnecessary resource costs). In this paper, we consider this problem in the context of streaming applications., In these applications, since the data is generated by external sources, the goal is to, carefully allocate resources so that the processing rate can, match the rate of data, arrival. We have developed a solution that can, handle, unexpected data rates, including, the, transient rates., We evaluate our approach using two streaming applications in a virtualized environment.

Utility computing was a vision stated more than 40 years ago. It refers to the idea that computing resources and services can be delivered, utilized, and paid for as utilities such as water or electricity. The recent emergence of cloud computing is making this vision realizable. Examples of efforts in this area include Infrastructure as a Service (IaaS) providers like Amazon EC2 and Software as a Service (SaaS) providers like Google AppEngine and Microsoft Azure. More recently, there is a growing interest in the use of cloud computing for scientific computing, as evidenced by a large-scale funded project like Magellan, and new workshops.

The recent emergence of clouds with large, virtualized pools of compute and storage resources raises the possibility of a new compute paradigm for scientific research. With virtualization technologies, consolidation of scientific workflows presents a promising opportunity for energy and resource cost optimization, while achieving high performance. We have developed pSciMapper, a power-aware consolidation framework for scientific workflows. We view consolidation as a hierarchical clustering problem, and introduce a distance metric that is based on interference between resource requirements. A dimensionality reduction method (KCCA) is used to relate the resource requirements to performance and power consumption. We have evaluated pSciMapper with both real-world and synthetic scientific workflows, and demonstrated that it is able to reduce power consumption by up to 56%, with less than 15% slowdown. Our experiments also show that scheduling overheads of pSciMapper are low, and the algorithm can scale well for workflows with hundreds of tasks.

There are many grid-based applications where a timely response to an important event is needed. Often such response can require a significant computation and possibly communication, and it can be very challenging to complete it within the time-frame the response is needed. At the same time, there could be application-specific flexibility in the computation that may be desired. We have been developing an autonomic middleware targeting this class of applications. In this paper, we consider the resource allocation problem for such adaptive applications, which comprise services with adaptable service parameters in heterogeneous grid environments. Our goal is to optimize a benefit function while meeting a time deadline. We define an efficiency value to reflect how effectively a particular service can be executed on a particular node. We have developed a greedy scheduling algorithm, which is based on prioritizing the services considering their impact on the benefit function, and choosing resources using the computed efficiency values. We have carefully evaluated our resource allocation approach using two applications from our target class, a volume rendering application and a Great Lake forecasting application. When compared to the resource allocation performed by an Optimal algorithm, which enumerates all mappings, the benefit achieved by our approach was an average of 87% and the average success- rate was 90%. Furthermore, the benefit we obtained was 32% higher than that of the GrADS algorithm, an existing approach we compared our approach with.

In this paper, we consider the problem of supporting fault tolerance for adaptive and time-critical applications in heterogeneous and unreliable grid computing environments. Our goal for this class of applications is to optimize a user-specified benefit function while meeting the time deadline. Our first contribution in this paper is a multi-objective optimization algorithm for scheduling the application onto the most efficient and reliable resources. In this way, the processing can achieve the maximum benefit while also maximizing the success-rate, which is the probability of finishing execution without failures. However, for the cases where failures do occur, we have developed a hybrid failure-recovery scheme to ensure that the application can complete within the pre-specified time interval. Our experimental results show that our scheduling algorithm can achieve better benefit when compared to several heuristics-based greedy scheduling algorithms, while still having a negligible overhead. Benefit is further improved when we apply the hybrid failure recovery scheme, and the success-rate becomes 100%.

We consider resource allocation for distributed streaming applications running in a grid environment, where continuously streaming data needs to be aggregated and processed to produce output streams. Because such an application comprises a pipeline of processing stages, both communication and computational requirements need to be taken into account while performing resource allocation. In this paper, we give a rigorous formulation of this resource allocation problem, based on the DAG representation of the application as well as the environment. We have shown how we can use the notion of subgraph isomorphism and developed an effective resource allocation algorithm. The main observations from the experiments we conducted to evaluate our algorithms were as follows: the overhead caused by our algorithm is comparable to an existing algorithm, Streamline, which is based onheuristics. At the same time, the application performance was improved by 30% on average. When compared to the allocation performed by the optimal algorithm, which enumerates all mappings, the application performance with our algorithm was within 4%. At the same time, unlike the optimal algorithm, our algorithm scaled well to large graphs.

This paper considers the problem of supporting and efficiently implementing fault-tolerance for tightly-coupled and pipelined applications, especially streaming applications, in a grid environment. We provide an alternative to basic checkpointing and use the notion of light-weight summary structure(LSS) to enable efficient failure-recovery. The idea behind LSS is that at certain points during the execution of a processing stage, the state of the program can be summarized by a small amount of memory. This allows us to store copies of LSS for enabling failure-recovery, which causes low overhead fault-tolerance. Our work can be viewed as an optimization and adaptation of the idea of application-level checkpointing to a different execution environment, and for a different class of applications. Our implementation and evaluation of LSS based failure- recovery has been in the context of the GATES (grid-based adaptive execution on streams) middleware. An observation we use for providing very low overhead support for fault-tolerance is that algorithms analyzing data streams are only allowed to take a single pass over data, which means they only perform approximate processing. Therefore, we believe that in supporting fault-tolerant execution for these applications, it is acceptable to not analyze a small number of packets of data during failure-recovery. We show how we perform failure-recovery and also demonstrate how we could use additional buffers to limit data loss during the recovery procedure. We also present an efficient algorithm for allocating a new computation resource for failure-recovery at runtime. We have extensively evaluated our implementation using three stream data processing applications, and shown that the use of LSS allows effective and low-overhead failure-recovery.

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In recent years, there has been a growing trend towards supporting more tightly coupled applications on the grid, including scientific workflows, applications that use pipelined or data-flow like processing, and distributed streaming applications. As availability of resources can vary over time in a grid environment, dynamic reallocation of resources is very important for these applications, particularly because of their long-running nature, and because they often require large-volume data transfers between processing stages.This paper considers the problem of supporting and efficiently implementing dynamic resource allocation for tightly-coupled and pipelined applications in a grid environment. We provide an alternative to basic checkpointing, using the notion of Light-weight Summary Structure (LSS), to enable efficient migration. The idea behind LSS is that at certain points during the execution of a processing stage, the state of the program can be summarized by a small amount of memory. This allows us to perform low-cost process migration, as long as such memory can be identified by an application developer, and migration is performed only at these points.Our implementation and evaluation of LSS based process migration has been in the context of the GATES (Grid-based Adap-Tive Execution on Streams) middleware that we have been developing. We also present an algorithm for dynamic resource allocation, and have shown an architecture for resource monitoring and allocation. We have extensively evaluated our implementation using three stream data processing applications, and show that the use of LSS allows efficient process migration.