Faisal Nawab

Serializability, not serial: concurrency control and availability in multi-datacenter datastores

We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Googles Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any system that satisfies the abstraction properties. We first develop and analyze a transaction management and replication protocol based on a straightforward implementation of the Paxos algorithm. Our investigation reveals that this protocol acts as a concurrency prevention mechanism rather than a concurrency control mechanism. We then propose an enhanced protocol called Paxos with Combination and Promotion (Paxos-CP) that provides true transaction concurrency while requiring the same per instance message complexity as the basic Paxos protocol. Finally, we compare the performance of Paxos and Paxos-CP in a multi-datacenter experimental study, and we demonstrate that Paxos-CP results in significantly fewer aborted transactions than basic Paxos.

Minimizing Commit Latency of Transactions in Geo-Replicated Data Stores

Cross datacenter replication is increasingly being deployed to bring data closer to the user and to overcome datacenter outages. The extent of the influence of wide-area communication on serializable transactions is not yet clear. In this work, we derive a lower-bound on commit latency. The sum of the commit latency of any two datacenters is at least the Round-Trip Time (RTT) between them. We use the insights and lessons learned while deriving the lower-bound to develop a commit protocol, called Helios, that achieves low commit latencies. Helios actively exchanges transaction logs (history) between datacenters. The received logs are used to decide whether a transaction can commit or not. The earliest point in the received logs that is needed to commit a transaction is decided by Helios to ensure a low commit latency. As we show in the paper, Helios is theoretically able to achieve the lower-bound commit latency. Also, in a real-world deployment on five datacenters, Helios has a commit latency that is close to the optimal.

Fair packet scheduling in Wireless Mesh Networks

In this paper we study the interactions of TCP and IEEE 802.11 MAC in Wireless Mesh Networks (WMNs). We use a Markov chain to capture the behavior of TCP sessions, particularly the impact on network throughput due to the effect of queue utilization and packet relaying. A closed form solution is derived to numerically determine the throughput. Based on the developed model, we propose a distributed MAC protocol called Timestamp-ordered MAC (TMAC), aiming to alleviate the unfairness problem in WMNs. TMAC extends CSMA/CA by scheduling data packets based on their age. Prior to transmitting a data packet, a transmitter broadcasts a request control message appended with a timestamp to a selected list of neighbors. It can proceed with the transmission only if it receives a sufficient number of grant control messages from these neighbors. A grant message indicates that the associated data packet has the lowest timestamp of all the packets pending transmission at the local transmit queue. We demonstrate that a loose ordering of timestamps among neighboring nodes is sufficient for enforcing local fairness, subsequently leading to flow rate fairness in a multi-hop WMN. We show that TMAC can be implemented using the control frames in IEEE 802.11, and thus can be easily integrated in existing 802.11-based WMNs. Our simulation results show that TMAC achieves excellent resource allocation fairness while maintaining over 90% of maximum link capacity across a large number of topologies.

TMAC: Timestamp-Ordered MAC for CSMA/CA Wireless Mesh Networks

We propose TMAC, a timestamp-ordered MAC protocol for Wireless Mesh Networks (WMNs). TMAC extends CSMA/CA by scheduling data packets based on their age. Prior to transmitting a data packet, a transmitter broadcasts a request control message appended with a timestamp to a selected list of neighbors. It can proceed with the transmission only if it receives a sufficient number of grant control messages from these neighbors. A grant message indicates that the associated data packet has the lowest timestamp of all the packets pending transmission at the local transmit queue. We demonstrate that a loose ordering of timestamps among neighboring nodes is sufficient for enforcing local fairness, subsequently leading to flow rate fairness in a multi-hop WMN. We show that TMAC can be implemented using the control frames in IEEE~802.11 stack, and thus can be easily integrated in existing 802.11-based WMNs. Our simulation results show that TMAC achieves excellent resource allocation fairness while maintaining over 90% of maximum link capacity in parking lot and large grid topologies.

MaaT: effective and scalable coordination of distributed transactions in the cloud

The past decade has witnessed an increasing adoption of cloud database technology, which provides better scalability, availability, and fault-tolerance via transparent partitioning and replication, and automatic load balancing and fail-over. However, only a small number of cloud databases provide strong consistency guarantees for distributed transactions, despite decades of research on distributed transaction processing, due to practical challenges that arise in the cloud setting, where failures are the norm, and human administration is minimal. For example, dealing with locks left by transactions initiated by failed machines, and determining a multi-programming level that avoids thrashing without under-utilizing available resources, are some of the challenges that arise when using lock-based transaction processing mechanisms in the cloud context. Even in the case of optimistic concurrency control, most proposals in the literature deal with distributed validation but still require the database to acquire locks during two-phase commit when installing updates of a single transaction on multiple machines. Very little theoretical work has been done to entirely eliminate the need for locking in distributed transactions, including locks acquired during two-phase commit. In this paper, we re-design optimistic concurrency control to eliminate any need for locking even for atomic commitment, while handling the practical issues in earlier theoretical work related to this problem. We conduct an extensive experimental study to evaluate our approach against lock-based methods under various setups and workloads, and demonstrate that our approach provides many practical advantages in the cloud context.

Managing Geo-replicated Data in Multi-datacenters

Over the past few years, cloud computing and the growth of global large scale computing systems have led to applications which require data management across multiple datacenters. Initially the models provided single row level transactions with eventual consistency. Although protocols based on these models provide high availability, they are not ideal for applications needing a consistent view of the data. There has been now a gradual shift to provide transactions with strong consistency with Google’s Megastore and Spanner. We propose protocols for providing full transactional support while replicating data in multi-datacenter environments. First, an extension of Megastore is presented, which uses optimistic concurrency control. Second, a contrasting method is put forward, which uses gossip-based protocol for providing distributed transactions across datacenters. Our aim is to propose and evaluate different approaches for geo-replication which may be beneficial for diverse applications.

Chariots : A Scalable Shared Log for Data Management in Multi-Datacenter Cloud Environments

Web-based applications face unprecedented workloads demanding the processing of a large number of events reaching to the millions per second. That is why developers are increasingly relying on scalable cloud platforms to implement cloud applications. Chariots exposes a shared log to be used by cloud applications. The log is essential for many tasks like bookkeeping, recovery, and debugging. Logs offer linearizability and simple append and read operations of immutable records to facilitate building complex systems like stream processors and transaction managers. As a cloud platform, Chariots offers fault-tolerance, persistence, and high-availability, transparently. Current shared log infrastructures suffer from the bottleneck of serializing log records through a centralized server which limits the throughput to that of a single machine. We propose a novel distributed log store, called the Fractal Log Store (FLStore), that overcomes the bottleneck of a single-point of contention. FLStore maintains the log within the datacenter. We also propose Chariots, which provides multi-datacenter replication for shared logs. In it, FLStore is leveraged as the log store. Chariots maintains causal ordering of records in the log and has a scalable design that allows elastic expansion of resources.

DB-Risk: The Game of Global Database Placement

Geo-replication is the process of maintaining copies of data at geographically dispersed datacenters for better availability and fault-tolerance. The distinguishing characteristic of geo-replication is the large wide-area latency between datacenters that varies widely depending on the location of the datacenters. Thus, choosing which datacenters to deploy a cloud application has a direct impact on the observable response time. We propose an optimization framework that automatically derives a geo-replication placement plan with the objective of minimizing latency. By running the optimization framework on real placement scenarios, we learn a set of placement optimizations for geo-replication. Some of these optimizations are surprising while others are in retrospect straight-forward. In this demonstration, we highlight the geo-replication placement optimizations through the DB-Risk game. DB-Risk invites players to create different placement scenarios while experimenting with the proposed optimizations. The placements created by the players are tested on real cloud deployments.

MAC-layer protocol for TCP fairness in Wireless Mesh Networks

In this paper we study the interactions of TCP and IEEE 802.11 MAC in Wireless Mesh Networks (WMNs). We use a Markov chain to capture the behavior of TCP sessions, particularly the impact on network throughput performance due to the effect of queue utilization and packet relaying. A closed form solution is derived to numerically determine the throughput. Based on the developed model, we propose a distributed MAC protocol to alleviate the unfairness problem in WMNs. Our protocol uses the age of packet as a priority metric for packet scheduling. Simulation is conducted to validate our model and to illustrate the fairness characteristics of our proposed MAC protocol. We conclude that we can achieve fairness with only little impact on network capacity.

Multi-representation Based Data Processing Architecture for IoT Applications

Internet of Things (IoT) applications like smart cars, smart cities and wearables are becoming widespread and are the future of the Internet. One of the major challenges for IoT applications is efficiently processing, storing and analyzing the continuous stream of incoming data from a large number of connected sensors. We propose a multi-representation based data processing architecture for IoT applications. The data is stored in multiple representations, like rows, columns, graphs which provides support for diverse application demands. A unifying update mechanism based on deterministic scheduling is used to update the data representations, which completely removes the need for data transfer pipelines like ETL (Extract, Transform and Load). The combination of multiple representations, and the deterministic update mechanism, provides the ability to support real-time analytics and caters to IoT applications by minimizing the latency of operations like computing pre-defined aggregates.