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Offloading of tasks to the cloud is one of the approaches to improve the performance of distributed applications. When monetary constraints are present, selection of the tasks to be offloaded becomes important in order to ensure efficient use of the available cloud resources. This becomes a challenge for large scale distributed applications as the decisions on offloading have to be made locally at the nodes without an exact global view of the system. In our earlier work, we modeled this challenge as a new class of formal problems termed cloudy knapsack problem and derived some theoretical bounds on the solution space for worst case task sequences. In many real world applications, the task sequences have inherent patterns which can be exploited to improve offloading. In this work, we propose a cloud offloading algorithm that exploits these patterns through offline and online learning. Experimental evaluation using realistic datasets for a cloud-assisted peer-to-peer search case study reveals that the proposed solution performs close to a hypothetical omniscient offloading algorithm having a complete view of the system. The proposed cloud-assisted peer-to-peer search engine provides a cost-effective approach to address scalability bottleneck in peer-to-peer search engines.

Today, batch processing frameworks like Hadoop MapReduce are difficult to scale to multiple clouds due to latencies involved in inter-cloud data transfer and synchronization overheads during shuffle-phase. This inhibits the MapReduce framework from guaranteeing performance at variable load surges without over-provisioning in the internal cloud (IC). We propose BStream, a cloud bursting framework for MapReduce that couples stream-processing in the external cloud (EC) with Hadoop in the internal cloud (IC). Stream processing in EC enables pipelined uploading, processing and downloading of data to minimize network latencies. We use this framework to meet job deadlines. BStream uses an analytical model to minimize the usage of EC. We propose different checkpointing strategies that overlap output transfer with input transfer/processing and simultaneously reduce the computation involved in merging the results from EC and IC. Checkpointing further reduces job completion time. We experimentally compare BStream with other related works and illustrate performance benefits due to stream processing and checkpointing strategies in EC. Lastly, we characterize the operational regime of BStream. � 2013 IEEE.

Cloud-assisted approaches for both peer-to-peer systems and mobile apps require optimized use of elastic cloud resources. Due to budget constraints, a subset of the tasks has to be selected for offloading considering context parameters like device battery level and task variability. This leads to the challenging problem of context-sensitive task scheduling on elastic resources with a limited global view, which is not addressed by existing works. We identify a new class of formal problems called cloudy knapsack problems to effectively model the same. Abstracting out the problem formally can spur future independent works related to different variants of the problem and corresponding bounds and optimal algorithms. We illustrate the global view related issues through simulations, identify some theoretical bounds for a variant of cloudy knapsack problems and discuss several open problems.

Optimizing ordered throughput not only improves the system efficiency but also makes cloud bursting transparent to the user. This is critical from the perspective of user fairness in customer-facing systems, correctness in stream processing systems, and so on. In this paper, we consider optimizing ordered throughput for near real-time, data-intensive, independent computations using cloud bursting. Intercloud computation of data-intensive applications is a challenge due to large data transfer requirements, low intercloud bandwidth, and best-effort traffic on the Internet. The system model we consider is comprised of two processing stages. The first stage uses cloud bursting opportunistically for parallel processing, while the second stage (sequential) expects the output of the first stage to be in the same order as the arrival sequence. We propose three scheduling heuristics as part of an autonomic cloud bursting approach that adapt to changing workload characteristics, variation in bandwidth, and available resources to optimize ordered throughput. We also characterize the operational regimes for cloud bursting as stabilization mode versus acceleration mode, depending on the workload characteristics like the size of data to be transferred for a given compute load. The operational regime characterization helps in deciding how many instances can be optimally utilized in the external cloud. � 1976-2012 IEEE.

The practice of computing across two or more data centers separated by the Internet is growing in popularity due to an explosion in scalable computing demands and pay-as-you-go schemes offered on the cloud. While cloud-bursting is addressing this process of scaling up and down across data centers (i.e. between private and public clouds), offering service level guarantees, is a challenge for inter-cloud computation, particularly for best-effort traffic and large files. The parallel workload we address is real-time and involves inter-cloud processing and analysis of images and documents. In our production printing domain, dedicated processing/network resources are cost-prohibitive. Further, the problem is exacerbated by data intensive computing - we encounter huge file sizes atypical of intercloud parallel processing. To address these problems we propose three flavors of autonomic cloud-bursting schedulers that offer probabilistic guarantees on service levels required by customers (such as speed-up and queue sequence preservation) by adapting to changing workload characteristics, variation in bandwidth and available resources. In particular, these opportunistic schedulers use a quadratic response surface model for processing time in concert with a time-of-day dependent bandwidth predictor to increase the throughput and utilization while simultaneously reducing out-of-sequence completions for a document processing workload.