180 Park Ave - Bldng 103 - E217
Florham Park, NJ
http://lagarcavilla.org/
I now work here at AT&T as a researcher. Please go to my personal web page for all the goods on what I'm doing and links to my projects and papers. You can also call me at (973) 360-7016. My fax # is (973) 360-8077. Thanks for dropping by!
Traffic Backfilling: Subsidizing Lunch for Delay-Tolerant Applications in UMTS Networks
Horacio Lagar , Kaustubh Joshi, Alexander Varshavsky, Jeffrey Bickford, Darwin Parra
ACM MobiHeld workshop 2011,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM MobiHeld workshop 2011 , 2011-10-23.
{Mobile application developers pay little attention to the interactions between applications and the cellular net- work carrying their traffic. This results in wastage of de- vice energy and network signaling resources. We place part of the blame on mobile OSes: they do not expose adequate interfaces through which applications can in- teract with the network. We propose traffic backfilling, a technique in which delay-tolerant traffic is opportunis- tically transmitted by the OS using resources left over by the naturally occurring bursts caused by interactive traffic. Backfilling presents a simple interface with two classes of traffic, and grants the OS and network large flexibility to maximize the use of network resources and reduce device energy consumption. Using device traces and network data from a major US carrier, we demon- strate a large opportunity for traffic backfilling.}
Security versus Energy Tradeoffs in Host-Based Mobile Malware Detection
Horacio Lagar , Alexander Varshavsky, Jeffrey Bickford, Vinod Ganapathy, Liviu Iftode
ACM Conference on Mobile Systems, Applications and Services (MobiSys),
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Conference on Mobile Systems, Applications and Services (MobiSys) , 2011-06-29.
{The rapid growth of mobile malware necessitates the presence of robust malware detectors on mobile devices. However, running malware detectors on mobile devices may drain their battery, causing users to disable these protection mechanisms to save power. This paper studies the security versus energy tradeoffs for a particularly challenging class of malware detectors, namely rootkit detectors. Specifically, we investigate the security/energy tradeoffs along two axes: attack surface and malware scanning frequency, for both code and data based rootkit detectors. Our findings, based on a real implementation on a phone-like device, reveal that protecting against code-driven attacks is relatively cheap, while protecting against all data-driven attacks is prohibitively expensive. Based on our findings, we determine a sweet spot in the security/energy tradeoff, called the balanced profile, which protects a mobile device against a vast majority of attacks, while consuming limited amount of extra battery power. }
PipeCloud: Using Causality to Overcome Speed-of-Light Delays in Cloud-Based Disaster Recovery
Horacio Lagar , Jacobus Van , Kadangode Ramakrishnan, Tim Wood, Prashant Shenoy
ACM Symposium on Cloud Computing,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in [ACM Symposium on Cloud Computing , 2011-10-27.
{Disaster Recovery (DR) is a desirable feature for all enterprises, and a crucial one for many. However, adoption of DR remains limited due to the stark tradeoffs it imposes. To be able to recover an application to the point of crash, one is limited by financial considerations, substantial application overhead, or minimal geographical separation between the primary and recovery sites. In this paper, we argue for cloud-based DR and pipelined synchronous replication as an antidote to these problems. Cloud hosting promises economies of scale and on-demand provisioning that are a perfect fit for the infrequent yet urgent needs of DR. However the WAN latency between a cloud site and an enterprise can become a major performance bottleneck when synchronously replicating an application's data into the cloud. Pipelined synchrony addresses this problem by tracking the causal consequences of the disk modifications that are persisted to a recovery site, while allowing the application to make forward progress in its handling of client requests. In this manner, we efficiently overlap replication delay with application processing for multi-tier distributed servers, while retaining full consistency guarantees for application state in the event of a disaster. PipeCloud, our prototype, is able to sustain these guarantees for multi-node servers composed of black-box VMs, with no need of application modification, resulting in a perfect fit for the arbitrary nature of VM-based cloud hosting. Our extensive evaluation shows that PipeCloud achieves significant performance improvements over existing replication strategies, and demonstrates proper disaster failover to the Amazon EC2 platform. PipeCloud can increase throughput by an order of magnitude and reduces response times by more than half compared to synchronous replication, all while providing the same zero data loss consistency guarantees.}
Kaleidoscope: Cloud Micro-Elasticity via VM State Coloring
Horacio Lagar , Kaustubh Joshi, Matti Hiltunen, Roy Bryant, Eyal Lara, Alexey Tumanov, Olga Irzak, Adin Scannell
Eurosys 2011, ACM European Conference on Computer Systems,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM European Conference on Computer Systems - Eurosys 2011, 2011-04-10.
{We introduce cloud micro-elasticity, a new model for cloud Virtual Machine (VM) allocation and management. Currentcloud users over-provision long-lived VMs with large memory footprints, to better absorb load spikes and to conserve performance-sensitive caches. Instead, we achieve elasticity by swiftly cloning VMs into many transient, short-lived, fractional worker clones, to multiplex physical resources at a much finer granularity. The memory of micro-elastic clones is a logical replica of all parent VM state including caches, and its footprint is a fraction of the nominal maximum proportional to the workload. We enable micro-elasticity through a novel technique dubbed VM state coloring, which classifies VM memory into sets of semantically-related regions, and optimizes the propagation, allocation and deduplication of these regions. Using coloring we build Kaleidoscope and empirically demonstrate its ability to create micro-elastic cloned servers.We model the impact of microelasticity on a demand dataset from a hosting provider, and show that fine-grained multiplexing yields infrastructure reductions of 30% relative to state-of-the art techniques for managing elastic clouds.}
The Case for Energy-Oriented Partial Desktop Migration
Kaustubh Joshi, Horacio Lagar-Cavilla, Matti Hiltunen, Nilton Bila, Eyal Lara, Mahadev Satyanarayanan
2nd USENIX Workshop on Hot Topics in Cloud Computing ,
2010.
[BIB]
USENIX Copyright
The definitive version was published in Proceedings of WOSN 2010, Usenix. , 2010-06-22
{Office and home environments are increasingly crowded with personal computers. Even though these computers see little use in the course of the day, they often remain powered, even when idle. Leaving idle PCs running is not only wasteful, but with rising energy costs it is in- creasingly more expensive. We propose partial migration of idle desktop sessions into the cloud to achieve energy- proportional computing. Partial migration only propa- gates the small footprint of state that will be needed dur- ing idle period execution, and returns the session to the PC when it is no longer idle. We show that this approach can reduce energy usage of an idle desktop by up to 50% over an hour and by up to 69% overnight. We also show that idle desktop sessions have small working sets, up to an order of magnitude smaller than their allocated mem- ory, enabling significant consolidation ratios.}
SnowFlock: Virtual Machine Cloning as a First Class Cloud Primitive
Horacio Lagar , Joseph Whitney, Roy Bryant, Philip Patchin, Michael Brudno, Eyal Lara, Stephen M. Rumble, M. Satyanarayanan, Adin Scannell
ACM Transactions on Computer Systems,
2010.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computer Systems , 2010-12-31
{A basic building block of cloud computing is virtualization. Virtual machines
(VMs) encapsulate a users computing environment and efficiently isolate it from
that of other users. VMs, however, are large entities, and no clear APIs exist
yet that allow users to efficiently and programatically control them.
We present SnowFlock, a paradigm and system for cloud computing that introduces VM cloning
as a first-class cloud abstraction. VM cloning exploits the well-understood
and effective semantics of UNIX fork. We demonstrate multiple usage models
of VM cloning: users can incorporate the primitive in their code, can wrap around existing
toolchains via scripting, can encapsulate the API within a parallel programming framework,
or can use it to load-balance and self-scale clustered servers.
VM cloning needs to be efficient to be usable. It must efficiently transmit
VM state in order to avoid cloud IO bottlenecks. We demonstrate how the semantics
of cloning aid us in realizing its efficiency: state is propagated in parallel to multiple
VM clones, and is transmitted during runtime, allowing for optimizations that
substantially reduce the IO load. We show detailed microbenchmark results highlighting the
effciency of our optimizations, and macrobenchmark numbers demonstrating the
effectiveness of the different usage models of SnowFlock.}
