Big Red Bits

First talk: Cloud-TM: Harnessing the Cloud with Distributed Transactional Memories given by Luis Rodrigues

MapReduce is nice if your data and task fits the model. However, it is unnatural for many scenarios. Another model for programming in the cloud is: PGAS (Partitioned Global Address Space), it masks machines as different addresses, but it has falls short because programmers do not know how many machines is their program running on ahead of time. D-STM (Distributed Software Transactional Memories) extends the TM abstraction across the boundaries of a single machine.

Research challenges:

• Automatic parallelization: extremely hard, but transactional support makes it easier to implement strategies based on the speculative execution portions of the code.
• Fault tolerance: only started to be considered by recent D-STMs
• Coping with Workload Heterogeneity. STM performance is heavily dependent on the workload, different algorithms exist optimized for different workloads, but this needs to be automated.
• Automatic Resource Provisioning.
• Persistence ACI vs ACID

Dependable Distributed STM (D2-STM) is a distributed fully replicated STM that uses atomic broadcast to coordinate replicas. Bloom filters used to reduce messages. Some prelim results: speculative replication, a technique that runs potentially conflicting transactions speculatively in order to hide the inter-replica coordination latency. Identifying and predicting the data access, we are developing stochastic techniques for identifying and predicting data access pattern. Thread-level speculation.

An application they built based on their techniques. FenixEDU manages the on-line campus activities used in production by the Technical University of Lisbon and being installed in other machines. 1000s of students. Web app, OO-domain model, Relational DMBS to store data, object/relational mapping tool to store objects in the db, runs on a STM implementation.

FenixEDU can be run in the cloud. We want programmers to be able to use the OO model they are familiar with, resource management has to be automatic, and consistency is crucial.

Conclusions: D-STM have many good properties that make them a promising technology to support distributed applications with consistency requirements in the cloud.

Second Talk: Storing and Accessing Live Mashup Content in the Cloud by Krzysztof Ostrowski

Interactive collaborative cloud services currently go through a “centralized” cloud service and users just poll for the updates. In the Cornell Live Objects model, clients collaborate together on the “edge”.

Cloud vs Edge ? if all on edge, we have persistence but potentially no consistency. If all on the cloud, we have consistency from the clients’ view but no scalability. For example, in second life, we can only handle 40 clients/server. The edge is much larger than the cloud. Many more under utilized machines in the edge than in the cloud.

In live objects, we used checkpointed channel. that are abstractions for communication that have proxies on machines and partially reside on the edge and cloud. Data is in that channel. Channels have a proxy and a network facing component. An event on a channel is considered to be delivered only if the proxy declares that and notifies the application above. If an update is received on a checkpoint channel (CC) we get a new CC`. For the programming model, CCs are given types in the programming language that reflect what kind of data is contained in each channel. Channels can have references to other channels which allow users to build complex structures and enable applications that subscribe to many channels representing many different objects. For example, a shared desktop can have references to the different objects in that desktop.

Conclusion: Checkpointed Channels are a new storage abstraction where users express interest in data regardless of where it resides or how it is stored (in a cloud service or a P2P system). Tremendous opportunity for scaling by splitting data between the cloud and edge.

Questions: questions mostly focused on the synchronized timing of correlated channels. For example, if a channel contains video data, and another channel contains audio data, how can the two be synchronized.

Third Talk: A Unified Execution Model for Cloud Computing by Eric Van Hensbergen

In current cloud programming settings, we have two models: platform as a service, and infrastructure as a service. That is, are we given unmanaged machines (EC2) that we are free to use as we want. Or are we given a managed platform (Google App Engine and Microsoft Azure) that manage the underlying complexity but limit what you can do. Idea, can we break down this barrier ? This is similar to previous studies on distributed operating systems from the past. However, the difference here is that they have to be more flexible, elastic, and work at scale.

Many techniques were discussed such as synthetic file systems, execution & control mechanisms, and aggregation techniques. Support for BlueGene/P Preliminary support for EC2.