Partial Replication Policies for Dynamic Distributed Transactional Memory in Edge Clouds
Article No.: 2, Pages 1 - 6
Abstract
Distributed Transactional Memory (DTM) can play a fundamental role in the coordination of participants in edge clouds as a support for mobile distributed applications. DTM emerges as a concurrency mechanism aimed at simplifying distributed programming by allowing groups of operations to execute atomically, mirroring the well-known transaction model of relational databases.
In spite of recent studies showing that partial replication approaches can present gains in the scalability of DTMs by reducing the amount of data stored at each node, most DTM solutions follow a full replication scheme. The few partial replicated DTM frameworks either follow a random or round-robin algorithm for distributing data onto partial replication groups. In order to overcome the poor performance of these schemes, this paper investigates policies to extend the DTM to efficiently and dynamically map resources on partial replication groups. The goal is to understand if a dynamic service that constantly evaluates the data mapped into partial replicated groups can contribute to improve DTM based systems performance.
References
[1]
M. Couceiro, P. Romano, N. Carvalho, and L. Rodrigues. D2STM: Dependable distributed software transactional memory. In Proc. of the 2009 15th IEEE Pacific Rim Int'l Symposium on Dependable Computing (PRDC'09), pages 307--313, 2009.
[2]
X. Défago, A. Schiper, and P. Urbán. Total order broadcast and multicast algorithms: Taxonomy and survey. ACM Comput. Surv., 36(4):372--421, Dec. 2004.
[3]
J. Gray. Notes on data base operating systems. In Operating Systems, An Advanced Course, pages 393--481, 1978.
[4]
M. Herlihy, V. Luchangco, and M. Moir. A flexible framework for implementing software transactional memory. SIGPLAN, 41(10):253--262, Oct. 2006.
[5]
M. Herlihy, V. Luchangco, M. Moir, and W. N. Scherer, III. Software transactional memory for dynamic-sized data structures. In Proc. of the 22nd Annual Symposium on Principles of Distributed Computing (P0DC'03), pages 92--101, 2003.
[6]
M. Herlihy and J. E. B. Moss. Transactional memory: Architectural support for lock-free data structures. SIGARCH Comput. Archit. News, 21(2):289--300, May 1993.
[7]
M. Herlihy and Y. Sun. Distributed transactional memory for metric-space networks. In Proc. of the 19th Int'l Conference on Distributed Computing (DISC'05), pages 324--338, 2005.
[8]
L. L. Hoang, C. E. Bezerra, and F. Pedone. Dynamic scalable state machine replication. In 46th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), June 2016.
[9]
B. Kemme and G. Alonso. A suite of database replication protocols based on group communication primitives. In Proc. of the 18th Int'l Conference on Distributed Computing Systems, pages 156--163, May 1998.
[10]
J. Kim and B. Ravindran. Scheduling transactions in replicated distributed software transactional memory. In Proc. of the 13th IEEE/ACM Int'l Symposium on Cluster, Cloud and Grid Computing (CCGrid), pages 227--234, May 2013.
[11]
K. Manassiev, M. Mihailescu, and C. Amza. Exploiting distributed version concurrency in a transactional memory cluster. In Proc. of the 11th ACM SIGPLAN Symposium on Principles and, Practice of Parallel Programming (PPoPP'06), pages 198--208, 2006.
[12]
C. C. Minh, J. Chung, C. Kozyrakis, and K. Olukotun. STAMP: Stanford Transactional Applications for MultiProcessing. In IEEE International Symposium on Workload Characterization, pages 35--46, 2008.
[13]
F. Morstatter, J. Pfeffer, H. Liu, and K. M. Carley. Is the sample good enough? comparing data from twitter's streaming API with twitter's firehose. CoRR, abs/1306.5204, 2013.
[14]
S. Peluso, P. Romano, and F. Quaglia. Score: A scalable one-copy serializable partial replication protocol. In Proc. of the 13th Int'l Middleware Conference (Middleware' 12), pages 456--475, 2012.
[15]
M. M. Saad and B. Ravindran. Hyflow: A high performance distributed software transactional memory framework. In Proc. of the 20th Int'l Symposium on High Performance Distributed Computing (HPDC'11), pages 265--266, 2011.
[16]
D. Sciascia and F. Pedone. Geo-replicated storage with scalable deferred update replication. In Proceedings of the 2014 IEEE 33rd International Symposium on Reliable Distributed Systems Workshops, SRDSW '14, pages 26--29, Washington, DC, USA, 2014. IEEE Computer Society.
[17]
J. a. A. Silva, T. M. Vale, R. J. Dias, H. Paulino, and J. a. M. Lourenco. Supporting multiple data replication models in distributed transactional memory. In Proc. of the 2015 Int l Conf. on Distributed Computing and Networking (ICDCN'15), pages 11:1--11:10, 2015.
[18]
A. Turcu, B. Ravindran, and R. Palmieri. Hyflow2: A high performance distributed transactional memory framework in scala. In Proc. of the 2013 Int'l Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools (PPPJ'13), pages 79--88, 2013.
[19]
B. Zhang and B. Ravindran. Relay: A cache-coherence protocol for distributed transactional memory. Principles of Distributed Systems, pages 48--53, 2009.
- Partial Replication Policies for Dynamic Distributed Transactional Memory in Edge Clouds
Recommendations
Optimistic transactional active replication
ICUIMC '08: Proceedings of the 2nd international conference on Ubiquitous information management and communicationCritical database applications require 2-safe replication between at least two sites for disaster-tolerant services. At the same time, they must provide consistent and low-latency results to their clients in normal cases. In this paper, we propose ...
Partial Collection Replication for Information Retrieval
AbstractThe explosion of content in distributed information retrieval (IR) systems requires new mechanisms in order to attain timely and accurate retrieval of unstructured text. This paper shows how to exploit locality by building, using, and searching ...
High-throughput state-machine replication using software transactional memory
State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could ...
Comments
Information & Contributors
Information
Published In
December 2016
25 pages
ISBN:9781450346689
DOI:10.1145/3017116
Copyright © 2016 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- ACM: Association for Computing Machinery
- USENIX Assoc: USENIX Assoc
- IFIP
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 December 2016
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
Middleware '16
Sponsor:
- ACM
- USENIX Assoc
Acceptance Rates
Overall Acceptance Rate 4 of 9 submissions, 44%
Upcoming Conference
MIDDLEWARE '24
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 137Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in