Guy Laden

Virtual machine time travel using continuous data protection and checkpointing

Virtual machine (VM) time travel enables reverting a virtual machines state, both transient and persistent, to past points in time. This capability can be used to improve virtual machine availability, to enable forensics on past VM states, and to recover from operator errors. We present an approach to virtual machine time travel which combines Continuous Data Protection (CDP) storage support with live-migration-based virtual machine checkpointing. In particular, we present a novel approach for CDP which enables efficient reverts of the storage state to past points in time and makes it possible to undo a revert, and this is achieved using a simple branched-temporal data structure. We also present a design and implementation of a simple live-migration-based checkpointing mechanism in Xen.

Data caching as a cloud service

We discuss the challenges of devising a useful shared data cache service as a part of the cloud platform. Outside the cloud, such a service appeals to developers for two main reasons. Most importantly, data caches reduce the response latency experienced by users. For example, rendering a content page with various personalized boxes as part of a user web session often involves numerous database lookups. Therefore, if the content generation involves cheap memory accesses to a data cache instead of actual database queries, the user experiences will improve. Moreover, data caches are simple to use: they normally expose a simple get/set interface akin to key/value stores and a rudimentary mechanism to expire values [2], thus allowing result-based caching to be seamlessly integrated with existing database-driven code.

Design and implementation of caching services in the cloud

Data caching is a key paradigm for improving the performance of web services in terms of both end-user latency and database load. Such caching is becoming an essential component of any application or service designed for the cloud platform. In order to allow hosted applications to benefit from caching capabilities while avoiding dependence on explicit implementations and idiosyncrasies of internal caches, the caching services should be offered by a cloud provider as an integral part of its platform-as-a-service portfolio. We highlight various challenges associated with supporting cloud-based caching services, such as identifying the appropriate metering and service models, performance management, and resource sharing across cloud tenants. We also describe how these challenges were addressed by our prototype implementation, which is called Simple Cache for Cloud (SC2). We demonstrate the effectiveness of these techniques by experimentally evaluating our prototype on a synthetic multitenant workload.

Adaptive and dynamic funnel replication in clouds

We consider the problem of strongly consistent replication in a multi data center cloud setting. This environment is characterized by high latency communication between data centers, significant fluctuations in the performance of seemingly identical virtual machines (VMs) and temporary disconnects of data centers from the rest of the cloud. In this paper we introduce the adaptive and dynamic Funnel Replication (FR) protocol that is designed to achieve high throughout and low latency for reads, to accommodate arbitrary latency/throughput tradeoffs for writes, to maximize performance in the face of VM performance variations and to provide high availability for read requests in the presence of network partitions. FR is based on the idea of flexible write dissemination topologies which enables it to achieve, per message, the desired tradeoff between latency and throughput, depending on the message size, the observed network conditions, and the importance of latency as indicated by the client. We demonstrate the benefits of flexible dissemination topologies and show that in a cloud setting with N identical replicas FR can improve the write latency up to a factor of N/2 for N ≥ 2 compared to the notable chain replication (CR) protocol at the expense of a slight decrease in the write throughput. In a setting with potentially high variability in the performance of replicas, e.g., as in Amazon EC2, FR can achieve throughput up to a factor of 16 higher than CR while also improving the latency. FR does this by adopting a topology that consists of concurrent disjoint data replication paths so that load on high throughput paths is adaptively increased while load on congested replicas is reduced.