Network calculus for parallel processing

In this note, we present preliminary results on the use of "network calculus" for parallel processing systems, specifically MapReduce

Using Performance Forecasting to Accelerate Elasticity

Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. In this context, monitoring of a running service plays a crucial role, and adjustments are made when certain thresholds are crossed. On such occasions, it is common practice to simply add or remove resources. In this paper we investigate how we can predict the performance of a service to dynamically adjust allocated resources based on predictions. In other words, instead of “repairing” because a threshold has been crossed, we attempt to stay ahead and allocate an optimized amount of resources in advance. To do so, we need to have accurate predictive models that are based on workloads. We present our approach, based on the Universal Scalability Law, and discuss initial experiments

Dynamic service placement for mobile micro-clouds with predicted future costs

Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost overtime, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is 0(1)-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) usermobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems

Artificial Polyploidy Improves Bacterial Single Cell Genome Recovery

BACKGROUND: Single cell genomics (SCG) is a combination of methods whose goal is to decipher the complete genomic sequence from a single cell and has been applied mostly to organisms with smaller genomes, such as bacteria and archaea. Prior single cell studies showed that a significant portion of a genome could be obtained. However, breakages of genomic DNA and amplification bias have made it very challenging to acquire a complete genome with single cells. We investigated an artificial method to induce polyploidy in Bacillus subtilis ATCC 6633 by blocking cell division and have shown that we can significantly improve the performance of genomic sequencing from a single cell. METHODOLOGY/PRINCIPAL FINDINGS: We inhibited the bacterial cytoskeleton protein FtsZ in B.subtilis with an FtsZ-inhibiting compound, PC190723, resulting in larger undivided single cells with multiple copies of its genome. qPCR assays of these larger, sorted cells showed higher DNA content, have less amplification bias, and greater genomic recovery than untreated cells. SIGNIFICANCE: The method presented here shows the potential to obtain a nearly complete genome sequence from a single bacterial cell. With millions of uncultured bacterial species in nature, this method holds tremendous promise to provide insight into the genomic novelty of yet-to-be discovered species, and given the temporary effects of artificial polyploidy coupled with the ability to sort and distinguish differences in cell size and genomic DNA content, may allow recovery of specific organisms in addition to their genomes

PULP: an Adaptive Gossip-Based Dissemination Protocol for Multi-Source Message Streams

Gossip-based protocols provide a simple, scalable, and robust way to disseminate messages in large-scale systems. In such protocols, messages are spread in an epidemic manner. Gossiping may take place between nodes using push, pull, or a combination. Push-based systems achieve reasonable latency and high resilience to failures but may impose an unnecessarily large redundancy and overhead on the system. At the other extreme, pull-based protocols impose a lower overhead on the network at the price of increased latencies. A few hybrid approaches have been proposed-typically pushing control messages and pulling data-to avoid the redundancy of high-volume content and single-source streams. Yet, to the best of our knowledge, no other system intermingles push and pull in a multiple-senders scenario, in such a way that data messages of one help in carrying control messages of the other and in adaptively adjusting its rate of operation, further reducing overall cost and improving both on delays and robustness. In this paper, we propose an efficient generic push-pull dissemination protocol, Pulp, which combines the best of both worlds. Pulp exploits the efficiency of push approaches, while limiting redundant messages and therefore imposing a low overhead, as pull protocols do. Pulp leverages the dissemination of multiple messages from diverse sources: by exploiting the push phase of messages to transmit information about other disseminations, Pulp enables an efficient pulling of other messages, which themselves help in turn with the dissemination of pending messages. We deployed Pulp on a cluster and on PlanetLab. Our results demonstrate that Pulp achieves an appealing trade-off between coverage, message redundancy, and propagation delay. © 2011 Springer Science+Business Media, LLC

Towards a Fluid Cloud: An Extension of the Cloud into the Local Network

Part 2: Ph.D. Student Workshop — Management of Future NetworkingInternational audienceCloud computing offers an attractive platform to provide resources on-demand, but currently fails to meet the corresponding latency requirements for a wide range of Internet of Things (IoT) applications. In recent years efforts have been made to distribute the cloud closer to the user environment, but they were typically limited to the fixed network infrastructure as current cloud management algorithms cannot cope with the unpredictable nature of wireless networks. This fixed deployment of clouds often does not suffice as, for some applications, the access network itself already introduces an intolerable delay in response time. Therefore we propose to extend the cloud formalism into the (wireless) IoT environment itself, incorporating the infrastructure that is already present. Given the mobile nature of local infrastructure, we refer to this as a fluid extension to the cloud, or more simply as a fluid cloud

An architecture for automatic scaling of replicated services

Replicated services that allow to scale dynamically can adapt to requests load. Choosing the right number of replicas is fundamental to avoid performance worsening when input spikes occur and to save resources when the load is low. Current mechanisms for automatic scaling are mostly based on fixed thresholds on CPU and memory usage, which are not sufficiently accurate and often entail late countermeasures. We propose Make Your Service Elastic (MYSE), an architecture for automatic scaling of generic replicated services based on queuing models for accurate response time estimation. Requests and service times patterns are analyzed to learn and predict over time their distribution so as to allow for early scaling. A novel heuristic is proposed to avoid the flipping phenomenon. We carried out simulations that show promising results for what concerns the effectiveness of our approach. © 2014 Springer International Publishing